ai/notebooks/2_efficientad.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Efficient AD Model For Anomaly Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary\n",
    "\n",
    "- Introduction\n",
    "- Before running the notebook\n",
    "- Different Model testing for easiest cookies type (cookies 3)\n",
    "- Create model for cookies 2\n",
    "- Create model for cookies 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The approach is called **EfficientAD**.  \n",
    "It is a anomaly detection models that takes few milliseconds on a modern GPU to do an inference.\n",
    "\n",
    "More on [Arxiv](https://arxiv.org/abs/2303.14535)\n",
    "\n",
    "It creates three models:  \n",
    "    - Student  \n",
    "    - Teacher  \n",
    "    - Auto encoder\n",
    "\n",
    "This notebooks is used to train multiple models and using [MLFLOW](https://mlflow.org/) to store the images on a remote t2.micro instance on AWS.\n",
    "\n",
    "The model is trained on a g4dn.xlarge instance.  \n",
    "If you want to train it on the same instance, make sure to create a support ticket on AWS requesting to have access on the \"G series\" instances in your region."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Before running the notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's prepare the environement before running the notebook.\n",
    "\n",
    "1. Do you have the datasets ? Download it [here](https://drive.google.com/file/d/19VM3RtzVFyDZ4s0HKJ8eVMnijAKEJdRJ/view?usp=drive_link) and extract it in the `notebooks` folder\n",
    "\n",
    "2. Run the following command in the terminal from the **`ai`** folder.\n",
    "```sh\n",
    "conda create -p envs/2_efficientad python=3.11.7 -y\n",
    "conda activate envs/2_efficientad\n",
    "\n",
    "pip install -r requirements/2_efficientad.txt\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys  # Import the sys module, required to access system-specific parameters and functions  \n",
    "sys.path.append('../code/')  # Add the '../code/' directory to the path that Python looks in for files to import  \n",
    "  \n",
    "# Import all functions from the efficientad_fns module  \n",
    "from efficientad_fns import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Define a dictionary to hold configuration parameters  \n",
    "config = {  \n",
    "    \"seed\": 42,  # Seed for random number generation for reproducibility  \n",
    "    \"on_gpu\": torch.cuda.is_available(),  # Boolean flag to use GPU if available  \n",
    "      \n",
    "    \"out_channels\": 384,  # Number of output channels in the model  \n",
    "    \"image_size\": 256,  # Size of input images  \n",
    "      \n",
    "    \"model_type\": \"small\",  # Type of model, identified here as 'small'  \n",
    "      \n",
    "    \"dataset_path\": \"datasets\",  # Path to the datasets directory  \n",
    "    \"subdataset\": \"cookies_3\",  # Specific sub-dataset to use  \n",
    "      \n",
    "    \"output_dir\": \"../output\",  # Directory to save output files  \n",
    "    \n",
    "    # Usage of weight_path\n",
    "    # \"weight_path\": \"../weights/teacher_small.pth\",\n",
    "    # Download the model here: https://github.com/nelson1425/EfficientAD/tree/main/models\n",
    "    # Or pretrain it yourself here: https://github.com/nelson1425/EfficientAD/blob/main/pretraining.py\n",
    "    \"weight_path\": \"\",  # Path to pre-trained weights (empty means no pre-loading)  \n",
    "      \n",
    "    # \"mlflow_tracking_uri\": \"\",  # URI for MLflow tracking server  \n",
    "    \"mlflow_tracking_uri\": \"http://3.90.103.58:5000/\",  # URI for MLflow tracking server  \n",
    "      \n",
    "    \"train_steps\": 20,  # Number of training steps  \n",
    "    \"learning_rate\": 1e-4,  # Learning rate for model training  \n",
    "    \"weight_decay\": 1e-5,  # Weight decay for regularization  \n",
    "}  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Different Model testing for easiest cookies type (cookies 3)\n",
    "\n",
    "- Step = 20,    model = small, weight = none\n",
    "- Step = 500,   model = small, weight = none\n",
    "- Step = 5000,  model = small, weight = none\n",
    "- Step = 10000, model = small, weight = none\n",
    "- Step = 70000, model = small, weight = none\n",
    "\n",
    "---\n",
    "\n",
    "- Step = 20,    model = medium, weight = none\n",
    "- Step = 500,   model = medium, weight = none\n",
    "- Step = 5000,  model = medium, weight = none\n",
    "- Step = 10000, model = medium, weight = none\n",
    "- ~~Step = 70000, model = medium, weight = none~~\n",
    "\n",
    "---\n",
    "\n",
    "- Step = 20,    model = small, weight = on\n",
    "- Step = 500,   model = small, weight = on\n",
    "- Step = 5000,  model = small, weight = on\n",
    "- Step = 10000, model = small, weight = on\n",
    "- ~~Step = 70000, model = small, weight = on~~\n",
    "\n",
    "---\n",
    "\n",
    "- Step = 20,    model = medium, weight = on\n",
    "- Step = 500,   model = medium, weight = on\n",
    "- Step = 5000,  model = medium, weight = on\n",
    "- Step = 10000, model = medium, weight = on\n",
    "- ~~Step = 70000, model = medium, weight = on~~"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (1.09 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (3.26 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_3_steps_20_small\n",
      "- OK - Setting config (4.36 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     No weight to load\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (254.78 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 46.89it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (2.97 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 23.5000  :   5%|████▎                                                                                 | 1/20 [00:02<00:45,  2.37s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.6451612903225806\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 100.5513  : 100%|████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:04<00:00,  4.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Train (4.64 s)\n",
      "\n",
      "- Saving models to ../output/cookies_3_steps_20_small/all_models.pth\n",
      "- OK - Saving models (69.14 ms)\n",
      "\n",
      "- Saving map normalization to ../output/cookies_3_steps_20_small/map_normalization.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.75it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Saving map normalization (1008.84 ms)\n",
      "\n",
      "- Evaluating model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.03it/s]\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     - AUC: 46.56%\n",
      "    - Optimal Threshold: 0.0438165\n",
      "    - F1 Score: 0.78\n",
      "    - CONFUSION MATRIX:\n",
      " [[ 8 12]\n",
      " [21 59]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 22.32it/s]\n",
      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.18it/s]\n",
      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.17it/s]\n",
      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.19it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- OK - Evaluating model (13.78 s)\n",
      "\n",
      "Dataset               F1 Score\n",
      "------------------------------\n",
      "Anonaly lvl 1 test        0.93\n",
      "Anonaly lvl 2 test        0.86\n",
      "Anonaly lvl 3 test        0.62\n",
      "\n",
      "Anomaly all test          0.86\n",
      "\n",
      "No Anomaly Test           0.57\n",
      "\n",
      "All test                  0.78\n"
     ]
    }
   ],
   "source": [
    "# STEPS = 20, MODEL TYPE = SMALL, WEIGHT = none\n",
    "model1 = EfficientAD({**config, \"train_steps\": 20, \"model_type\": \"small\", \"weight_path\":\"\"})\n",
    "model1.create_model()\n",
    "model1.display_eval_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.75 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (4.08 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_3_steps_500_small\n",
      "- OK - Setting config (3.91 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     No weight to load\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (65.90 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.39it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (2.80 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 23.5000  :   0%|▏                                                                                    | 1/500 [00:01<13:07,  1.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.6451612903225806\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 14.7164  :  20%|████████████████▊                                                                  | 101/500 [00:15<03:20,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7878787878787878\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.1046  :  40%|█████████████████████████████████▊                                                  | 201/500 [00:28<02:31,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.85\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.5540  :  60%|██████████████████████████████████████████████████▌                                 | 301/500 [00:42<01:40,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.85\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 4.9979  :  80%|███████████████████████████████████████████████████████████████████▎                | 401/500 [00:56<00:50,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7096774193548387\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 4.3021  : 100%|████████████████████████████████████████████████████████████████████████████████████| 500/500 [01:08<00:00,  7.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Train (68.59 s)\n",
      "\n",
      "- Saving models to ../output/cookies_3_steps_500_small/all_models.pth\n",
      "- OK - Saving models (79.69 ms)\n",
      "\n",
      "- Saving map normalization to ../output/cookies_3_steps_500_small/map_normalization.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Saving map normalization (1013.53 ms)\n",
      "\n",
      "- Evaluating model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 21.98it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     - AUC: 66.62%\n",
      "    - Optimal Threshold: 0.0519325\n",
      "    - F1 Score: 0.90\n",
      "    - CONFUSION MATRIX:\n",
      " [[ 8 12]\n",
      " [ 5 75]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 22.02it/s]\n",
      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 21.99it/s]\n",
      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 21.96it/s]\n",
      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- OK - Evaluating model (13.85 s)\n",
      "\n",
      "Dataset               F1 Score\n",
      "------------------------------\n",
      "Anonaly lvl 1 test        0.93\n",
      "Anonaly lvl 2 test        1.00\n",
      "Anonaly lvl 3 test        1.00\n",
      "\n",
      "Anomaly all test          0.97\n",
      "\n",
      "No Anomaly Test           0.57\n",
      "\n",
      "All test                  0.90\n"
     ]
    }
   ],
   "source": [
    "# STEPS = 500, MODEL TYPE = SMALL, WEIGHT = none\n",
    "model2 = EfficientAD({**config, \"train_steps\": 500, \"model_type\": \"small\", \"weight_path\":\"\"})\n",
    "model2.create_model()\n",
    "model2.display_eval_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.82 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (4.21 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_3_steps_5000_small\n",
      "- OK - Setting config (4.03 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     No weight to load\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (61.64 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.22it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 50.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (2.85 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 23.5000  :   0%|                                                                                  | 1/5000 [00:01<2:12:18,  1.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.6451612903225806\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 14.7530  :   2%|█▋                                                                                | 101/5000 [00:15<41:08,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.0656  :   4%|███▎                                                                               | 201/5000 [00:28<40:06,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8205128205128205\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.4918  :   6%|████▉                                                                              | 301/5000 [00:42<39:44,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.6666666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 4.9741  :   8%|██████▋                                                                            | 401/5000 [00:56<38:45,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7096774193548387\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.8714  :  10%|████████▎                                                                          | 501/5000 [01:10<38:00,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8717948717948718\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.3687  :  12%|█████████▉                                                                         | 601/5000 [01:24<37:03,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 3.4545  :  14%|███████████▋                                                                       | 701/5000 [01:37<36:05,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.5714285714285714\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.2899  :  16%|█████████████▎                                                                     | 801/5000 [01:51<35:46,  1.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.625\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 2.3064  :  18%|██████████████▉                                                                    | 901/5000 [02:05<34:36,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.6875\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 3.9667  :  20%|████████████████▍                                                                 | 1001/5000 [02:19<33:40,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7777777777777778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 4.5612  :  22%|██████████████████                                                                | 1101/5000 [02:32<32:50,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7777777777777778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 3.1548  :  24%|███████████████████▋                                                              | 1201/5000 [02:46<31:57,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7777777777777778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 2.6697  :  26%|█████████████████████▎                                                            | 1301/5000 [03:00<32:03,  1.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.5517241379310345\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 1.9708  :  28%|██████████████████████▉                                                           | 1401/5000 [03:14<30:18,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 2.1729  :  30%|████████████████████████▌                                                         | 1501/5000 [03:27<29:24,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7058823529411765\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 1.4776  :  32%|██████████████████████████▎                                                       | 1601/5000 [03:41<29:06,  1.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 2.6586  :  34%|███████████████████████████▉                                                      | 1701/5000 [03:55<27:34,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7096774193548387\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 3.3335  :  36%|█████████████████████████████▌                                                    | 1801/5000 [04:09<29:54,  1.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8333333333333334\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 1.5362  :  38%|███████████████████████████████▏                                                  | 1901/5000 [04:23<26:03,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 1.7777  :  40%|████████████████████████████████▊                                                 | 2000/5000 [04:36<06:55,  7.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7777777777777778\n",
      "Early stopping at iteration 2001 because validation F1 did not improve.\n",
      "- OK - Train (276.81 s)\n",
      "\n",
      "- Saving models to ../output/cookies_3_steps_5000_small/all_models.pth\n",
      "- OK - Saving models (59.25 ms)\n",
      "\n",
      "- Saving map normalization to ../output/cookies_3_steps_5000_small/map_normalization.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Saving map normalization (1008.47 ms)\n",
      "\n",
      "- Evaluating model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.16it/s]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     - AUC: 88.62%\n",
      "    - Optimal Threshold: 0.0289546\n",
      "    - F1 Score: 0.86\n",
      "    - CONFUSION MATRIX:\n",
      " [[18  2]\n",
      " [18 62]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 22.19it/s]\n",
      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.18it/s]\n",
      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.08it/s]\n",
      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- OK - Evaluating model (13.76 s)\n",
      "\n",
      "Dataset               F1 Score\n",
      "------------------------------\n",
      "Anonaly lvl 1 test        1.00\n",
      "Anonaly lvl 2 test        0.75\n",
      "Anonaly lvl 3 test        0.67\n",
      "\n",
      "Anomaly all test          0.86\n",
      "\n",
      "No Anomaly Test           0.95\n",
      "\n",
      "All test                  0.86\n"
     ]
    }
   ],
   "source": [
    "# STEPS = 5000, MODEL TYPE = SMALL, WEIGHT = none\n",
    "model3 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"small\", \"weight_path\":\"\"})\n",
    "model3.create_model()\n",
    "model3.display_eval_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.22 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (4.31 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_3_steps_10000_small\n",
      "- OK - Setting config (3.99 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     No weight to load\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (61.42 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.34it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 50.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (2.83 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 23.5000  :   0%|                                                                                 | 1/10000 [00:01<4:30:22,  1.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.6451612903225806\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 31.6716  :   0%|▍                                                                                 | 49/10000 [00:07<25:23,  6.53it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# STEPS = 10000, MODEL TYPE = SMALL, WEIGHT = none\u001b[39;00m\n\u001b[1;32m      2\u001b[0m model4 \u001b[38;5;241m=\u001b[39m EfficientAD({\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain_steps\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m10000\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_type\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msmall\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweight_path\u001b[39m\u001b[38;5;124m\"\u001b[39m:\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m})\n\u001b[0;32m----> 3\u001b[0m \u001b[43mmodel4\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m model4\u001b[38;5;241m.\u001b[39mdisplay_eval_result()\n",
      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:69\u001b[0m, in \u001b[0;36mEfficientAD.create_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     64\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_params({\n\u001b[1;32m     65\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcfg,\n\u001b[1;32m     66\u001b[0m }) \n\u001b[1;32m     68\u001b[0m \u001b[38;5;66;03m# Execute all train-validation-testing steps\u001b[39;00m\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;66;03m# Log performance metrics\u001b[39;00m\n\u001b[1;32m     72\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_metric(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinal_auc)\n",
      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:49\u001b[0m, in \u001b[0;36mEfficientAD.do_all\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_teacher_student_autoencoder()\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mteacher_normalization()\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_models()\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_normalization()\n",
      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:260\u001b[0m, in \u001b[0;36mEfficientAD.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[38;5;66;03m# Move data to GPU if enabled \u001b[39;00m\n\u001b[1;32m    259\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcfg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mon_gpu\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m--> 260\u001b[0m     image_st \u001b[38;5;241m=\u001b[39m \u001b[43mimage_st\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcuda\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    261\u001b[0m     image_ae \u001b[38;5;241m=\u001b[39m image_ae\u001b[38;5;241m.\u001b[39mcuda()\n\u001b[1;32m    263\u001b[0m \u001b[38;5;66;03m# Teacher model inference followed by normalization \u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# STEPS = 10000, MODEL TYPE = SMALL, WEIGHT = none\n",
    "model4 = EfficientAD({**config, \"train_steps\": 10000, \"model_type\": \"small\", \"weight_path\":\"\"})\n",
    "model4.create_model()\n",
    "model4.display_eval_result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.50 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (3.57 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_3_steps_20_medium\n",
      "- OK - Setting config (11.83 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     No weight to load\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (180.60 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.89it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (6.59 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 91.8934  :   5%|████▎                                                                                 | 1/20 [00:03<01:07,  3.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.5517241379310345\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 59.0334  : 100%|█████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:09<00:00,  2.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Train (9.72 s)\n",
      "\n",
      "- Saving models to ../output/cookies_3_steps_20_medium/all_models.pth\n",
      "- OK - Saving models (197.52 ms)\n",
      "\n",
      "- Saving map normalization to ../output/cookies_3_steps_20_medium/map_normalization.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 10.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Saving map normalization (2794.26 ms)\n",
      "\n",
      "- Evaluating model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.19it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     - AUC: 45.31%\n",
      "    - Optimal Threshold: 0.0491857\n",
      "    - F1 Score: 0.90\n",
      "    - CONFUSION MATRIX:\n",
      " [[ 2 18]\n",
      " [ 0 80]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:04<00:00,  9.16it/s]\n",
      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.18it/s]\n",
      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.19it/s]\n",
      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- OK - Evaluating model (32.92 s)\n",
      "\n",
      "Dataset               F1 Score\n",
      "------------------------------\n",
      "Anonaly lvl 1 test        1.00\n",
      "Anonaly lvl 2 test        1.00\n",
      "Anonaly lvl 3 test        1.00\n",
      "\n",
      "Anomaly all test          0.99\n",
      "\n",
      "No Anomaly Test           0.18\n",
      "\n",
      "All test                  0.90\n"
     ]
    }
   ],
   "source": [
    "# STEPS = 20, MODEL TYPE = MEDIUM, WEIGHT = none\n",
    "model6 = EfficientAD({**config, \"train_steps\": 20, \"model_type\": \"medium\", \"weight_path\":\"\"})\n",
    "model6.create_model()\n",
    "model6.display_eval_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.26 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (4.93 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_3_steps_500_medium\n",
      "- OK - Setting config (10.06 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     No weight to load\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (164.97 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.62it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (6.65 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 91.8934  :   0%|▏                                                                                    | 1/500 [00:03<29:39,  3.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.5517241379310345\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 10.9337  :  20%|████████████████▊                                                                  | 101/500 [00:39<08:22,  1.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8108108108108109\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.4086  :  40%|█████████████████████████████████▊                                                  | 201/500 [01:15<06:17,  1.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.4997  :  60%|██████████████████████████████████████████████████▌                                 | 301/500 [01:52<04:11,  1.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7272727272727273\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 11.0552  :  80%|██████████████████████████████████████████████████████████████████▌                | 401/500 [02:28<02:05,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8333333333333334\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 3.2578  : 100%|████████████████████████████████████████████████████████████████████████████████████| 500/500 [03:01<00:00,  2.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Train (181.31 s)\n",
      "\n",
      "- Saving models to ../output/cookies_3_steps_500_medium/all_models.pth\n",
      "- OK - Saving models (206.78 ms)\n",
      "\n",
      "- Saving map normalization to ../output/cookies_3_steps_500_medium/map_normalization.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 10.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Saving map normalization (2804.30 ms)\n",
      "\n",
      "- Evaluating model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.11it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     - AUC: 89.75%\n",
      "    - Optimal Threshold: 0.0412511\n",
      "    - F1 Score: 0.87\n",
      "    - CONFUSION MATRIX:\n",
      " [[18  2]\n",
      " [17 63]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:04<00:00,  9.14it/s]\n",
      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.16it/s]\n",
      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.15it/s]\n",
      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- OK - Evaluating model (33.06 s)\n",
      "\n",
      "Dataset               F1 Score\n",
      "------------------------------\n",
      "Anonaly lvl 1 test        0.99\n",
      "Anonaly lvl 2 test        0.82\n",
      "Anonaly lvl 3 test        0.67\n",
      "\n",
      "Anomaly all test          0.88\n",
      "\n",
      "No Anomaly Test           0.95\n",
      "\n",
      "All test                  0.87\n"
     ]
    }
   ],
   "source": [
    "# STEPS = 500, MODEL TYPE = MEDIUM, WEIGHT = none\n",
    "model7 = EfficientAD({**config, \"train_steps\": 500, \"model_type\": \"medium\", \"weight_path\":\"\"})\n",
    "model7.create_model()\n",
    "model7.display_eval_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.27 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (4.74 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_3_steps_5000_medium\n",
      "- OK - Setting config (0.12 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     No weight to load\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (160.22 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.55it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (6.69 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 91.8934  :   0%|                                                                                  | 1/5000 [00:03<4:58:50,  3.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.5517241379310345\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 10.9421  :   2%|█▌                                                                              | 101/5000 [00:39<1:43:11,  1.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8108108108108109\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.5011  :   4%|███▎                                                                             | 201/5000 [01:16<1:41:18,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.5772  :   6%|████▉                                                                            | 301/5000 [01:52<1:39:31,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7647058823529411\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 11.0889  :   8%|██████▍                                                                         | 401/5000 [02:28<1:37:15,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8333333333333334\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.0276  :  10%|████████                                                                         | 501/5000 [03:05<1:34:52,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 10.1115  :  12%|█████████▌                                                                      | 601/5000 [03:41<1:33:16,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7647058823529411\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 3.4369  :  14%|███████████▎                                                                     | 701/5000 [04:18<1:31:02,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.4180  :  16%|████████████▉                                                                    | 801/5000 [04:54<1:28:50,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8333333333333334\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 4.4896  :  18%|██████████████▌                                                                  | 901/5000 [05:31<1:26:41,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8717948717948718\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 2.0717  :  20%|████████████████                                                                | 1001/5000 [06:07<1:24:38,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7647058823529411\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 2.3179  :  22%|█████████████████▌                                                              | 1101/5000 [06:43<1:22:30,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7096774193548387\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 2.6633  :  24%|███████████████████▏                                                            | 1201/5000 [07:20<1:20:23,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 2.8343  :  26%|████████████████████▊                                                           | 1301/5000 [07:56<1:19:08,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7272727272727273\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 3.3417  :  28%|██████████████████████▍                                                         | 1401/5000 [08:33<1:16:12,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9047619047619048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 2.0842  :  30%|████████████████████████                                                        | 1501/5000 [09:09<1:14:08,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7096774193548387\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 3.1276  :  32%|█████████████████████████▌                                                      | 1601/5000 [09:46<1:11:43,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 2.1818  :  34%|███████████████████████████▏                                                    | 1701/5000 [10:22<1:09:46,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8780487804878049\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 2.2747  :  36%|████████████████████████████▊                                                   | 1801/5000 [10:59<1:10:19,  1.32s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8717948717948718\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 3.8479  :  38%|██████████████████████████████▍                                                 | 1901/5000 [11:35<1:05:27,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8333333333333334\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.6651  :  40%|████████████████████████████████                                                | 2001/5000 [12:12<1:03:31,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9047619047619048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 2.1156  :  42%|█████████████████████████████████▌                                              | 2101/5000 [12:48<1:01:26,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 1.9430  :  44%|████████████████████████████████████                                              | 2201/5000 [13:25<59:14,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8717948717948718\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 1.5055  :  46%|█████████████████████████████████████▋                                            | 2301/5000 [14:01<57:02,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.926829268292683\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 2.7284  :  48%|███████████████████████████████████████▍                                          | 2401/5000 [14:38<55:06,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9047619047619048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 3.6469  :  50%|█████████████████████████████████████████                                         | 2501/5000 [15:14<53:01,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.926829268292683\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 3.2708  :  52%|██████████████████████████████████████████▋                                       | 2601/5000 [15:51<51:03,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 3.8423  :  54%|████████████████████████████████████████████▎                                     | 2701/5000 [16:27<48:37,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7272727272727273\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 1.4583  :  56%|█████████████████████████████████████████████▉                                    | 2801/5000 [17:04<46:40,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7777777777777778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 2.9438  :  58%|███████████████████████████████████████████████▌                                  | 2901/5000 [17:41<44:36,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7777777777777778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 1.4852  :  60%|█████████████████████████████████████████████████▏                                | 3001/5000 [18:17<42:19,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8333333333333334\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 1.7922  :  62%|██████████████████████████████████████████████████▊                               | 3101/5000 [18:54<40:32,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 1.9035  :  64%|████████████████████████████████████████████████████▍                             | 3201/5000 [19:30<38:03,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 0.9275  :  66%|██████████████████████████████████████████████████████▏                           | 3301/5000 [20:06<35:54,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.6666666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 1.6014  :  68%|███████████████████████████████████████████████████████▊                          | 3401/5000 [20:43<33:54,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7272727272727273\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 1.8778  :  70%|█████████████████████████████████████████████████████████▍                        | 3501/5000 [21:20<31:48,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7272727272727273\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 4.0031  :  72%|███████████████████████████████████████████████████████████                       | 3601/5000 [21:56<30:53,  1.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8333333333333334\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 1.2849  :  74%|████████████████████████████████████████████████████████████▋                     | 3701/5000 [22:33<27:28,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 1.8299  :  76%|██████████████████████████████████████████████████████████████▎                   | 3800/5000 [23:09<07:18,  2.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8\n",
      "Early stopping at iteration 3801 because validation F1 did not improve.\n",
      "- OK - Train (1389.47 s)\n",
      "\n",
      "- Saving models to ../output/cookies_3_steps_5000_medium/all_models.pth\n",
      "- OK - Saving models (148.95 ms)\n",
      "\n",
      "- Saving map normalization to ../output/cookies_3_steps_5000_medium/map_normalization.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 10.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Saving map normalization (2820.04 ms)\n",
      "\n",
      "- Evaluating model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.13it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     - AUC: 90.19%\n",
      "    - Optimal Threshold: 0.0372543\n",
      "    - F1 Score: 0.87\n",
      "    - CONFUSION MATRIX:\n",
      " [[17  3]\n",
      " [16 64]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:04<00:00,  9.10it/s]\n",
      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.10it/s]\n",
      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.12it/s]\n",
      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- OK - Evaluating model (33.13 s)\n",
      "\n",
      "Dataset               F1 Score\n",
      "------------------------------\n",
      "Anonaly lvl 1 test        1.00\n",
      "Anonaly lvl 2 test        0.79\n",
      "Anonaly lvl 3 test        0.71\n",
      "\n",
      "Anomaly all test          0.88\n",
      "\n",
      "No Anomaly Test           0.92\n",
      "\n",
      "All test                  0.87\n"
     ]
    }
   ],
   "source": [
    "# STEPS = 5000, MODEL TYPE = MEDIUM, WEIGHT = none\n",
    "model8 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"medium\", \"weight_path\":\"\"})\n",
    "model8.create_model()\n",
    "model8.display_eval_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.50 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (4.48 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_3_steps_10000_medium\n",
      "- OK - Setting config (0.11 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     No weight to load\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (160.58 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.63it/s]\n",
      "    Computing std of features:   0%|                                                                                               | 0/72 [00:00<?, ?it/s]\n",
      "    Computing std of features:   0%|                                                                                               | 0/72 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# STEPS = 10000, MODEL TYPE = MEDIUM, WEIGHT = none\u001b[39;00m\n\u001b[1;32m      2\u001b[0m model9 \u001b[38;5;241m=\u001b[39m EfficientAD({\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain_steps\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m10000\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_type\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedium\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweight_path\u001b[39m\u001b[38;5;124m\"\u001b[39m:\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m})\n\u001b[0;32m----> 3\u001b[0m \u001b[43mmodel9\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m model9\u001b[38;5;241m.\u001b[39mdisplay_eval_result()\n",
      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:69\u001b[0m, in \u001b[0;36mEfficientAD.create_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     64\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_params({\n\u001b[1;32m     65\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcfg,\n\u001b[1;32m     66\u001b[0m }) \n\u001b[1;32m     68\u001b[0m \u001b[38;5;66;03m# Execute all train-validation-testing steps\u001b[39;00m\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;66;03m# Log performance metrics\u001b[39;00m\n\u001b[1;32m     72\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_metric(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinal_auc)\n",
      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:47\u001b[0m, in \u001b[0;36mEfficientAD.do_all\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;66;03m# Prepare models and perform training  \u001b[39;00m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_teacher_student_autoencoder()\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mteacher_normalization\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_models()\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:439\u001b[0m, in \u001b[0;36mEfficientAD.teacher_normalization\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    436\u001b[0m channel_mean \u001b[38;5;241m=\u001b[39m channel_mean[\u001b[38;5;28;01mNone\u001b[39;00m, :, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m]\n\u001b[1;32m    438\u001b[0m mean_distances \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m--> 439\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m train_image, _ \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_loader, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m    Computing std of features\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m    440\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcfg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mon_gpu\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    441\u001b[0m         train_image \u001b[38;5;241m=\u001b[39m train_image\u001b[38;5;241m.\u001b[39mcuda()\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/tqdm/std.py:1181\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1178\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m   1180\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1181\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m   1182\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m   1183\u001b[0m         \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m   1184\u001b[0m         \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/data/dataloader.py:631\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    628\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    629\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m    630\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 631\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    633\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    634\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    635\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1329\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1326\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_data(data)\n\u001b[1;32m   1328\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shutdown \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m-> 1329\u001b[0m idx, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1330\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   1331\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable:\n\u001b[1;32m   1332\u001b[0m     \u001b[38;5;66;03m# Check for _IterableDatasetStopIteration\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1285\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._get_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1283\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m   1284\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_thread\u001b[38;5;241m.\u001b[39mis_alive():\n\u001b[0;32m-> 1285\u001b[0m         success, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_try_get_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1286\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m success:\n\u001b[1;32m   1287\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m data\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1133\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._try_get_data\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1120\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_try_get_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, timeout\u001b[38;5;241m=\u001b[39m_utils\u001b[38;5;241m.\u001b[39mMP_STATUS_CHECK_INTERVAL):\n\u001b[1;32m   1121\u001b[0m     \u001b[38;5;66;03m# Tries to fetch data from `self._data_queue` once for a given timeout.\u001b[39;00m\n\u001b[1;32m   1122\u001b[0m     \u001b[38;5;66;03m# This can also be used as inner loop of fetching without timeout, with\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1130\u001b[0m     \u001b[38;5;66;03m# Returns a 2-tuple:\u001b[39;00m\n\u001b[1;32m   1131\u001b[0m     \u001b[38;5;66;03m#   (bool: whether successfully get data, any: data if successful else None)\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1133\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_queue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1134\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;28;01mTrue\u001b[39;00m, data)\n\u001b[1;32m   1135\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m   1136\u001b[0m         \u001b[38;5;66;03m# At timeout and error, we manually check whether any worker has\u001b[39;00m\n\u001b[1;32m   1137\u001b[0m         \u001b[38;5;66;03m# failed. Note that this is the only mechanism for Windows to detect\u001b[39;00m\n\u001b[1;32m   1138\u001b[0m         \u001b[38;5;66;03m# worker failures.\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/queue.py:180\u001b[0m, in \u001b[0;36mQueue.get\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    178\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m remaining \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n\u001b[1;32m    179\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m Empty\n\u001b[0;32m--> 180\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnot_empty\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mremaining\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    181\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get()\n\u001b[1;32m    182\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnot_full\u001b[38;5;241m.\u001b[39mnotify()\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/threading.py:324\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    322\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    323\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 324\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m \u001b[43mwaiter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    325\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    326\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m waiter\u001b[38;5;241m.\u001b[39macquire(\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# STEPS = 10000, MODEL TYPE = MEDIUM, WEIGHT = none\u001b[39;00m\n\u001b[1;32m      2\u001b[0m model9 \u001b[38;5;241m=\u001b[39m EfficientAD({\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain_steps\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m10000\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_type\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedium\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweight_path\u001b[39m\u001b[38;5;124m\"\u001b[39m:\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m})\n\u001b[0;32m----> 3\u001b[0m \u001b[43mmodel9\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m model9\u001b[38;5;241m.\u001b[39mdisplay_eval_result()\n",
      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:69\u001b[0m, in \u001b[0;36mEfficientAD.create_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     64\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_params({\n\u001b[1;32m     65\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcfg,\n\u001b[1;32m     66\u001b[0m }) \n\u001b[1;32m     68\u001b[0m \u001b[38;5;66;03m# Execute all train-validation-testing steps\u001b[39;00m\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;66;03m# Log performance metrics\u001b[39;00m\n\u001b[1;32m     72\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_metric(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinal_auc)\n",
      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:47\u001b[0m, in \u001b[0;36mEfficientAD.do_all\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;66;03m# Prepare models and perform training  \u001b[39;00m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_teacher_student_autoencoder()\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mteacher_normalization\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_models()\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:439\u001b[0m, in \u001b[0;36mEfficientAD.teacher_normalization\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    436\u001b[0m channel_mean \u001b[38;5;241m=\u001b[39m channel_mean[\u001b[38;5;28;01mNone\u001b[39;00m, :, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m]\n\u001b[1;32m    438\u001b[0m mean_distances \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m--> 439\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m train_image, _ \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_loader, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m    Computing std of features\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m    440\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcfg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mon_gpu\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    441\u001b[0m         train_image \u001b[38;5;241m=\u001b[39m train_image\u001b[38;5;241m.\u001b[39mcuda()\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/tqdm/std.py:1181\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1178\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m   1180\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1181\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m   1182\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m   1183\u001b[0m         \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m   1184\u001b[0m         \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/data/dataloader.py:439\u001b[0m, in \u001b[0;36mDataLoader.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    437\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterator\n\u001b[1;32m    438\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 439\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/data/dataloader.py:387\u001b[0m, in \u001b[0;36mDataLoader._get_iterator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    386\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcheck_worker_number_rationality()\n\u001b[0;32m--> 387\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_MultiProcessingDataLoaderIter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1040\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter.__init__\u001b[0;34m(self, loader)\u001b[0m\n\u001b[1;32m   1033\u001b[0m w\u001b[38;5;241m.\u001b[39mdaemon \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m   1034\u001b[0m \u001b[38;5;66;03m# NB: Process.start() actually take some time as it needs to\u001b[39;00m\n\u001b[1;32m   1035\u001b[0m \u001b[38;5;66;03m#     start a process and pass the arguments over via a pipe.\u001b[39;00m\n\u001b[1;32m   1036\u001b[0m \u001b[38;5;66;03m#     Therefore, we only add a worker to self._workers list after\u001b[39;00m\n\u001b[1;32m   1037\u001b[0m \u001b[38;5;66;03m#     it started, so that we do not call .join() if program dies\u001b[39;00m\n\u001b[1;32m   1038\u001b[0m \u001b[38;5;66;03m#     before it starts, and __del__ tries to join but will get:\u001b[39;00m\n\u001b[1;32m   1039\u001b[0m \u001b[38;5;66;03m#     AssertionError: can only join a started process.\u001b[39;00m\n\u001b[0;32m-> 1040\u001b[0m \u001b[43mw\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1041\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_index_queues\u001b[38;5;241m.\u001b[39mappend(index_queue)\n\u001b[1;32m   1042\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_workers\u001b[38;5;241m.\u001b[39mappend(w)\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/multiprocessing/process.py:121\u001b[0m, in \u001b[0;36mBaseProcess.start\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _current_process\u001b[38;5;241m.\u001b[39m_config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdaemon\u001b[39m\u001b[38;5;124m'\u001b[39m), \\\n\u001b[1;32m    119\u001b[0m        \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdaemonic processes are not allowed to have children\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    120\u001b[0m _cleanup()\n\u001b[0;32m--> 121\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_popen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Popen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    122\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sentinel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_popen\u001b[38;5;241m.\u001b[39msentinel\n\u001b[1;32m    123\u001b[0m \u001b[38;5;66;03m# Avoid a refcycle if the target function holds an indirect\u001b[39;00m\n\u001b[1;32m    124\u001b[0m \u001b[38;5;66;03m# reference to the process object (see bpo-30775)\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/multiprocessing/context.py:224\u001b[0m, in \u001b[0;36mProcess._Popen\u001b[0;34m(process_obj)\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m    223\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_Popen\u001b[39m(process_obj):\n\u001b[0;32m--> 224\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_default_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_context\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mProcess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Popen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/multiprocessing/context.py:281\u001b[0m, in \u001b[0;36mForkProcess._Popen\u001b[0;34m(process_obj)\u001b[0m\n\u001b[1;32m    278\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m    279\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_Popen\u001b[39m(process_obj):\n\u001b[1;32m    280\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpopen_fork\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Popen\n\u001b[0;32m--> 281\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mPopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/multiprocessing/popen_fork.py:19\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturncode \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinalizer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_launch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/multiprocessing/popen_fork.py:66\u001b[0m, in \u001b[0;36mPopen._launch\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m     64\u001b[0m parent_r, child_w \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpipe()\n\u001b[1;32m     65\u001b[0m child_r, parent_w \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpipe()\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpid \u001b[38;5;241m=\u001b[39m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfork\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpid \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# STEPS = 10000, MODEL TYPE = MEDIUM, WEIGHT = none\n",
    "model9 = EfficientAD({**config, \"train_steps\": 10000, \"model_type\": \"medium\", \"weight_path\":\"\"})\n",
    "model9.create_model()\n",
    "model9.display_eval_result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.42 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (4.70 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_3_steps_20_small_weighted\n",
      "- OK - Setting config (0.21 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     Loading weight ../weights/teacher_small.pth\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (80.52 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 50.12it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 49.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (2.90 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 39.9363  :   5%|████▎                                                                                 | 1/20 [00:01<00:30,  1.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9230769230769231\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 60.6087  : 100%|█████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:03<00:00,  5.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Train (3.88 s)\n",
      "\n",
      "- Saving models to ../output/cookies_3_steps_20_small_weighted/all_models.pth\n",
      "- OK - Saving models (81.50 ms)\n",
      "\n",
      "- Saving map normalization to ../output/cookies_3_steps_20_small_weighted/map_normalization.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Saving map normalization (1018.72 ms)\n",
      "\n",
      "- Evaluating model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.01it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     - AUC: 70.12%\n",
      "    - Optimal Threshold: 0.0674505\n",
      "    - F1 Score: 0.83\n",
      "    - CONFUSION MATRIX:\n",
      " [[13  7]\n",
      " [18 62]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 22.27it/s]\n",
      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.20it/s]\n",
      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.28it/s]\n",
      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- OK - Evaluating model (13.78 s)\n",
      "\n",
      "Dataset               F1 Score\n",
      "------------------------------\n",
      "Anonaly lvl 1 test        0.92\n",
      "Anonaly lvl 2 test        0.86\n",
      "Anonaly lvl 3 test        0.79\n",
      "\n",
      "Anomaly all test          0.88\n",
      "\n",
      "No Anomaly Test           0.79\n",
      "\n",
      "All test                  0.83\n"
     ]
    }
   ],
   "source": [
    "# STEPS = 20, MODEL TYPE = SMALL, WEIGHT = on\n",
    "model11 = EfficientAD({**config, \"train_steps\": 20, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\"})\n",
    "model11.create_model()\n",
    "model11.display_eval_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.78 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (3.94 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_3_steps_500_small_weighted\n",
      "- OK - Setting config (0.11 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     Loading weight ../weights/teacher_small.pth\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (74.82 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 49.95it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 49.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (2.90 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 39.9363  :   0%|▏                                                                                    | 1/500 [00:01<13:26,  1.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9230769230769231\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 20.2424  :  20%|████████████████▊                                                                  | 101/500 [00:15<03:21,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 12.9697  :  40%|█████████████████████████████████▎                                                 | 201/500 [00:29<02:30,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 11.6319  :  60%|█████████████████████████████████████████████████▉                                 | 301/500 [00:43<01:40,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 9.2919  :  80%|███████████████████████████████████████████████████████████████████▎                | 401/500 [00:56<00:50,  1.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 8.1361  : 100%|████████████████████████████████████████████████████████████████████████████████████| 500/500 [01:09<00:00,  7.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Train (69.28 s)\n",
      "\n",
      "- Saving models to ../output/cookies_3_steps_500_small_weighted/all_models.pth\n",
      "- OK - Saving models (79.04 ms)\n",
      "\n",
      "- Saving map normalization to ../output/cookies_3_steps_500_small_weighted/map_normalization.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Saving map normalization (1025.01 ms)\n",
      "\n",
      "- Evaluating model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 21.85it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     - AUC: 97.94%\n",
      "    - Optimal Threshold: 0.0322094\n",
      "    - F1 Score: 0.96\n",
      "    - CONFUSION MATRIX:\n",
      " [[18  2]\n",
      " [ 4 76]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 22.12it/s]\n",
      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.10it/s]\n",
      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.01it/s]\n",
      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- OK - Evaluating model (13.87 s)\n",
      "\n",
      "Dataset               F1 Score\n",
      "------------------------------\n",
      "Anonaly lvl 1 test        1.00\n",
      "Anonaly lvl 2 test        1.00\n",
      "Anonaly lvl 3 test        0.89\n",
      "\n",
      "Anomaly all test          0.98\n",
      "\n",
      "No Anomaly Test           0.95\n",
      "\n",
      "All test                  0.96\n"
     ]
    }
   ],
   "source": [
    "# STEPS = 500, MODEL TYPE = SMALL, WEIGHT = on\n",
    "model12 = EfficientAD({**config, \"train_steps\": 500, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\"})\n",
    "model12.create_model()\n",
    "model12.display_eval_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.27 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (4.67 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_3_steps_5000_small_weighted\n",
      "- OK - Setting config (0.14 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     Loading weight ../weights/teacher_small.pth\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (74.20 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.03it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 49.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (2.86 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 39.9363  :   0%|                                                                                  | 1/5000 [00:01<2:13:48,  1.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9230769230769231\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 20.2371  :   2%|█▋                                                                                | 101/5000 [00:15<41:17,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 12.9873  :   4%|███▎                                                                              | 201/5000 [00:29<40:32,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 11.6855  :   6%|████▉                                                                             | 301/5000 [00:43<39:49,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 9.3164  :   8%|██████▋                                                                            | 401/5000 [00:57<38:47,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 10.1041  :  10%|████████▏                                                                         | 501/5000 [01:11<38:14,  1.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9743589743589743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 8.1916  :  12%|█████████▉                                                                         | 601/5000 [01:25<37:40,  1.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9743589743589743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.4264  :  14%|███████████▋                                                                       | 701/5000 [01:39<36:17,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9473684210526315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 8.9889  :  16%|█████████████▎                                                                     | 801/5000 [01:53<36:03,  1.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9473684210526315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.6123  :  18%|██████████████▉                                                                    | 901/5000 [02:07<34:44,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9473684210526315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.0918  :  20%|████████████████▍                                                                 | 1001/5000 [02:21<33:56,  1.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9743589743589743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.6462  :  22%|██████████████████                                                                | 1101/5000 [02:35<32:54,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.4376  :  24%|███████████████████▋                                                              | 1201/5000 [02:49<32:13,  1.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.3026  :  26%|█████████████████████▎                                                            | 1301/5000 [03:03<32:17,  1.91it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.3493  :  28%|██████████████████████▉                                                           | 1401/5000 [03:17<30:47,  1.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9743589743589743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.6122  :  30%|████████████████████████▌                                                         | 1501/5000 [03:31<29:39,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 13.8073  :  32%|█████████████████████████▉                                                       | 1601/5000 [03:45<29:10,  1.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9743589743589743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.6579  :  34%|███████████████████████████▉                                                      | 1701/5000 [03:59<28:15,  1.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.0115  :  36%|█████████████████████████████▌                                                    | 1801/5000 [04:13<31:47,  1.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.4137  :  38%|███████████████████████████████▏                                                  | 1901/5000 [04:27<26:20,  1.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.8775  :  40%|████████████████████████████████▊                                                 | 2001/5000 [04:41<25:23,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.6189  :  42%|██████████████████████████████████▍                                               | 2101/5000 [04:55<24:29,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.2148  :  44%|████████████████████████████████████                                              | 2201/5000 [05:09<24:07,  1.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.0581  :  46%|█████████████████████████████████████▋                                            | 2301/5000 [05:23<22:56,  1.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.0633  :  48%|███████████████████████████████████████▍                                          | 2401/5000 [05:37<22:26,  1.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9473684210526315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.7604  :  50%|█████████████████████████████████████████                                         | 2501/5000 [05:51<21:04,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.0447  :  52%|██████████████████████████████████████████▋                                       | 2601/5000 [06:05<20:35,  1.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.9753  :  54%|████████████████████████████████████████████▎                                     | 2701/5000 [06:19<19:29,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 4.8369  :  56%|█████████████████████████████████████████████▉                                    | 2801/5000 [06:33<18:54,  1.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.9145  :  58%|███████████████████████████████████████████████▌                                  | 2901/5000 [06:47<17:48,  1.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.3674  :  60%|█████████████████████████████████████████████████▏                                | 3001/5000 [07:01<17:11,  1.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.0132  :  62%|██████████████████████████████████████████████████▊                               | 3101/5000 [07:15<16:40,  1.90it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 4.9510  :  64%|████████████████████████████████████████████████████▍                             | 3200/5000 [07:29<04:12,  7.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n",
      "Early stopping at iteration 3201 because validation F1 did not improve.\n",
      "- OK - Train (449.25 s)\n",
      "\n",
      "- Saving models to ../output/cookies_3_steps_5000_small_weighted/all_models.pth\n",
      "- OK - Saving models (61.16 ms)\n",
      "\n",
      "- Saving map normalization to ../output/cookies_3_steps_5000_small_weighted/map_normalization.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Saving map normalization (1011.50 ms)\n",
      "\n",
      "- Evaluating model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.17it/s]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     - AUC: 99.94%\n",
      "    - Optimal Threshold: 0.0333683\n",
      "    - F1 Score: 0.99\n",
      "    - CONFUSION MATRIX:\n",
      " [[20  0]\n",
      " [ 1 79]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 21.95it/s]\n",
      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 21.95it/s]\n",
      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 21.97it/s]\n",
      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 21.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 21.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- OK - Evaluating model (13.85 s)\n",
      "\n",
      "Dataset               F1 Score\n",
      "------------------------------\n",
      "Anonaly lvl 1 test        1.00\n",
      "Anonaly lvl 2 test        1.00\n",
      "Anonaly lvl 3 test        0.97\n",
      "\n",
      "Anomaly all test          0.99\n",
      "\n",
      "No Anomaly Test           1.00\n",
      "\n",
      "All test                  0.99\n"
     ]
    }
   ],
   "source": [
    "# STEPS = 5000, MODEL TYPE = SMALL, WEIGHT = on\n",
    "model13 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\"})\n",
    "model13.create_model()\n",
    "model13.display_eval_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# STEPS = 10000, MODEL TYPE = SMALL, WEIGHT = on\n",
    "model14 = EfficientAD({**config, \"train_steps\": 10000, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\"})\n",
    "model14.create_model()\n",
    "model14.display_eval_result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.28 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (4.62 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_3_steps_20_medium_weighted\n",
      "- OK - Setting config (0.11 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     Loading weight ../weights/teacher_medium.pth\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (211.90 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.57it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (6.69 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 68.6309  :   5%|████▎                                                                                 | 1/20 [00:03<01:07,  3.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.95\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 42.8470  : 100%|█████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:09<00:00,  2.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Train (9.74 s)\n",
      "\n",
      "- Saving models to ../output/cookies_3_steps_20_medium_weighted/all_models.pth\n",
      "- OK - Saving models (204.05 ms)\n",
      "\n",
      "- Saving map normalization to ../output/cookies_3_steps_20_medium_weighted/map_normalization.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 10.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Saving map normalization (2796.52 ms)\n",
      "\n",
      "- Evaluating model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.14it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     - AUC: 83.69%\n",
      "    - Optimal Threshold: 0.0326394\n",
      "    - F1 Score: 0.89\n",
      "    - CONFUSION MATRIX:\n",
      " [[16  4]\n",
      " [12 68]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:04<00:00,  9.16it/s]\n",
      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.15it/s]\n",
      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.14it/s]\n",
      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- OK - Evaluating model (33.04 s)\n",
      "\n",
      "Dataset               F1 Score\n",
      "------------------------------\n",
      "Anonaly lvl 1 test        0.96\n",
      "Anonaly lvl 2 test        1.00\n",
      "Anonaly lvl 3 test        0.71\n",
      "\n",
      "Anomaly all test          0.92\n",
      "\n",
      "No Anomaly Test           0.89\n",
      "\n",
      "All test                  0.89\n"
     ]
    }
   ],
   "source": [
    "# STEPS = 20, MODEL TYPE = MEDIUM, WEIGHT = on\n",
    "model16 = EfficientAD({**config, \"train_steps\": 20, \"model_type\": \"medium\", \"weight_path\":\"../weights/teacher_medium.pth\"})\n",
    "model16.create_model()\n",
    "model16.display_eval_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.45 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (4.02 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_3_steps_500_medium_weighted\n",
      "- OK - Setting config (10.11 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     Loading weight ../weights/teacher_medium.pth\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (176.04 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.03it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (6.59 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 68.6309  :   0%|▏                                                                                    | 1/500 [00:03<29:31,  3.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.95\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 20.9917  :  20%|████████████████▊                                                                  | 101/500 [00:39<08:24,  1.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.926829268292683\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 14.8370  :  40%|█████████████████████████████████▎                                                 | 201/500 [01:15<06:18,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 15.0840  :  60%|█████████████████████████████████████████████████▉                                 | 301/500 [01:52<04:12,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 12.1250  :  80%|██████████████████████████████████████████████████████████████████▌                | 401/500 [02:29<02:06,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 9.2769  : 100%|████████████████████████████████████████████████████████████████████████████████████| 500/500 [03:02<00:00,  2.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Train (182.58 s)\n",
      "\n",
      "- Saving models to ../output/cookies_3_steps_500_medium_weighted/all_models.pth\n",
      "- OK - Saving models (210.89 ms)\n",
      "\n",
      "- Saving map normalization to ../output/cookies_3_steps_500_medium_weighted/map_normalization.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 10.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Saving map normalization (2843.68 ms)\n",
      "\n",
      "- Evaluating model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:11<00:00,  9.09it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     - AUC: 98.44%\n",
      "    - Optimal Threshold: 0.0600894\n",
      "    - F1 Score: 0.92\n",
      "    - CONFUSION MATRIX:\n",
      " [[20  0]\n",
      " [12 68]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:04<00:00,  9.10it/s]\n",
      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.11it/s]\n",
      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.11it/s]\n",
      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:11<00:00,  9.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- OK - Evaluating model (33.23 s)\n",
      "\n",
      "Dataset               F1 Score\n",
      "------------------------------\n",
      "Anonaly lvl 1 test        1.00\n",
      "Anonaly lvl 2 test        0.95\n",
      "Anonaly lvl 3 test        0.67\n",
      "\n",
      "Anomaly all test          0.91\n",
      "\n",
      "No Anomaly Test           1.00\n",
      "\n",
      "All test                  0.92\n"
     ]
    }
   ],
   "source": [
    "# STEPS = 500, MODEL TYPE = MEDIUM, WEIGHT = on\n",
    "model17 = EfficientAD({**config, \"train_steps\": 500, \"model_type\": \"medium\", \"weight_path\":\"../weights/teacher_medium.pth\"})\n",
    "model17.create_model()\n",
    "model17.display_eval_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.55 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (4.36 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_3_steps_5000_medium_weighted\n",
      "- OK - Setting config (0.12 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     Loading weight ../weights/teacher_medium.pth\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (190.37 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.36it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (6.73 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 68.6309  :   0%|                                                                                  | 1/5000 [00:03<4:59:09,  3.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.95\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 20.9237  :   2%|█▌                                                                              | 101/5000 [00:40<1:43:55,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.926829268292683\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 14.7919  :   4%|███▏                                                                            | 201/5000 [01:16<1:41:43,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9230769230769231\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 15.1827  :   6%|████▊                                                                           | 301/5000 [01:53<1:39:48,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 12.6867  :   8%|██████▍                                                                         | 401/5000 [02:30<1:37:45,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9473684210526315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 11.1644  :  10%|████████                                                                        | 501/5000 [03:06<1:35:23,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9743589743589743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 11.8949  :  12%|█████████▌                                                                      | 601/5000 [03:43<1:33:37,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 9.6371  :  14%|███████████▎                                                                     | 701/5000 [04:20<1:31:20,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 11.0092  :  16%|████████████▊                                                                   | 801/5000 [04:57<1:29:30,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 8.0689  :  18%|██████████████▌                                                                  | 901/5000 [05:34<1:27:04,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9473684210526315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.4727  :  20%|████████████████                                                                | 1001/5000 [06:11<1:25:12,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.5814  :  22%|█████████████████▌                                                              | 1101/5000 [06:48<1:22:49,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.9276  :  24%|███████████████████▏                                                            | 1201/5000 [07:25<1:20:37,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.7713  :  26%|████████████████████▊                                                           | 1301/5000 [08:02<1:19:24,  1.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9743589743589743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 8.5872  :  28%|██████████████████████▍                                                         | 1401/5000 [08:39<1:16:35,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.7772  :  30%|████████████████████████                                                        | 1501/5000 [09:16<1:14:17,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9743589743589743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.7838  :  32%|█████████████████████████▌                                                      | 1601/5000 [09:53<1:12:15,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9473684210526315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.4460  :  34%|███████████████████████████▏                                                    | 1701/5000 [10:29<1:10:02,  1.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9743589743589743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.3359  :  36%|████████████████████████████▊                                                   | 1801/5000 [11:06<1:11:02,  1.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 10.7029  :  38%|██████████████████████████████                                                 | 1901/5000 [11:43<1:06:01,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 10.7263  :  40%|███████████████████████████████▌                                               | 2001/5000 [12:20<1:03:56,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9743589743589743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.4899  :  42%|█████████████████████████████████▌                                              | 2101/5000 [12:57<1:01:49,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.1119  :  44%|████████████████████████████████████                                              | 2201/5000 [13:34<59:39,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9473684210526315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.1965  :  46%|█████████████████████████████████████▋                                            | 2301/5000 [14:11<57:25,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.8960  :  48%|███████████████████████████████████████▍                                          | 2401/5000 [14:48<55:23,  1.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.975609756097561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 8.0248  :  50%|█████████████████████████████████████████                                         | 2500/5000 [15:25<15:25,  2.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n",
      "Early stopping at iteration 2501 because validation F1 did not improve.\n",
      "- OK - Train (925.18 s)\n",
      "\n",
      "- Saving models to ../output/cookies_3_steps_5000_medium_weighted/all_models.pth\n",
      "- OK - Saving models (153.25 ms)\n",
      "\n",
      "- Saving map normalization to ../output/cookies_3_steps_5000_medium_weighted/map_normalization.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 10.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Saving map normalization (2833.80 ms)\n",
      "\n",
      "- Evaluating model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:11<00:00,  9.06it/s]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     - AUC: 98.81%\n",
      "    - Optimal Threshold: 0.0241348\n",
      "    - F1 Score: 0.99\n",
      "    - CONFUSION MATRIX:\n",
      " [[18  2]\n",
      " [ 0 80]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:04<00:00,  9.09it/s]\n",
      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.11it/s]\n",
      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.05it/s]\n",
      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:11<00:00,  9.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- OK - Evaluating model (33.28 s)\n",
      "\n",
      "Dataset               F1 Score\n",
      "------------------------------\n",
      "Anonaly lvl 1 test        1.00\n",
      "Anonaly lvl 2 test        1.00\n",
      "Anonaly lvl 3 test        1.00\n",
      "\n",
      "Anomaly all test          1.00\n",
      "\n",
      "No Anomaly Test           0.95\n",
      "\n",
      "All test                  0.99\n"
     ]
    }
   ],
   "source": [
    "# STEPS = 5000, MODEL TYPE = MEDIUM, WEIGHT = on\n",
    "model18 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"medium\", \"weight_path\":\"../weights/teacher_medium.pth\"})\n",
    "model18.create_model()\n",
    "model18.display_eval_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# STEPS = 10000, MODEL TYPE = MEDIUM, WEIGHT = on\n",
    "model19 = EfficientAD({**config, \"train_steps\": 10000, \"model_type\": \"medium\", \"weight_path\":\"../weights/teacher_medium.pth\"})\n",
    "model19.create_model()\n",
    "model19.display_eval_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# STEPS = 5000, MODEL TYPE = SMALL, WEIGHT = on Cookies 1\n",
    "model20 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\", \"subdataset\": \"cookies_1\"})\n",
    "model20.create_model()\n",
    "model20.display_eval_result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create model for cookies 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.25 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (4.36 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_2_steps_5000_small_weighted\n",
      "- OK - Setting config (0.14 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     Loading weight ../weights/teacher_small.pth\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (77.57 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.45it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 50.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (2.83 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 45.0005  :   0%|                                                                                  | 1/5000 [00:01<2:16:40,  1.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9743589743589743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 21.3039  :   2%|█▋                                                                                | 101/5000 [00:15<41:08,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.95\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 15.4218  :   4%|███▎                                                                              | 201/5000 [00:29<40:05,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9473684210526315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 12.9569  :   6%|████▉                                                                             | 301/5000 [00:43<39:49,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 12.2017  :   8%|██████▌                                                                           | 401/5000 [00:57<38:36,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 12.1251  :  10%|████████▏                                                                         | 501/5000 [01:11<37:37,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 9.4938  :  12%|█████████▉                                                                         | 601/5000 [01:25<37:39,  1.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 9.2203  :  14%|███████████▋                                                                       | 701/5000 [01:39<36:02,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.6707  :  16%|█████████████▎                                                                     | 801/5000 [01:53<35:40,  1.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 8.4446  :  18%|██████████████▉                                                                    | 901/5000 [02:07<34:21,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.4782  :  20%|████████████████▍                                                                 | 1001/5000 [02:20<33:44,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.9326  :  22%|██████████████████                                                                | 1101/5000 [02:35<32:53,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.2532  :  24%|███████████████████▋                                                              | 1201/5000 [02:49<32:35,  1.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.8009  :  26%|█████████████████████▎                                                            | 1301/5000 [03:03<32:28,  1.90it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.8656  :  28%|██████████████████████▉                                                           | 1401/5000 [03:17<30:51,  1.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 8.0031  :  30%|████████████████████████▌                                                         | 1501/5000 [03:31<29:32,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.6937  :  32%|██████████████████████████▎                                                       | 1601/5000 [03:45<29:02,  1.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.6296  :  34%|███████████████████████████▉                                                      | 1701/5000 [03:59<27:46,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.7260  :  36%|█████████████████████████████▌                                                    | 1800/5000 [04:13<07:30,  7.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 1.0\n",
      "Early stopping at iteration 1801 because validation F1 did not improve.\n",
      "- OK - Train (253.17 s)\n",
      "\n",
      "- Saving models to ../output/cookies_2_steps_5000_small_weighted/all_models.pth\n",
      "- OK - Saving models (66.52 ms)\n",
      "\n",
      "- Saving map normalization to ../output/cookies_2_steps_5000_small_weighted/map_normalization.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Saving map normalization (1008.57 ms)\n",
      "\n",
      "- Evaluating model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.33it/s]\n"
     ]
    },
    {
     "data": {
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z5+Pff//Fpk2b4ObmJnWcPMORWSIiIiI99PbtWxw+fFj9uG7durh161ahKmQBjswSfZQQAslKICElFXIh0+m5ElKUOj0+EREVDEFBQejZsyceP36MCxcuwNXVFQBgZFT4SrvC94qJNCCEgOf6q7j2yAhTrpyQOg4RERVyQggsX74ckydPhkKhQLly5aSOJDkWs0TZSFQoce3Rmzw/r6tTcZjJDfP8vERElH/FxMRg4MCB2LdvHwCga9eu8PHxQbFixSTNJTUWs0Q5dOnbZrC0MM2Tc5nJDSGT6XZKAxER6Y/Lly+jZ8+eiIiIgLGxMRYvXoxRo0bx3wqwmCXKMTNjQ5gb8yNDRER57/Tp04iIiECFChWwY8cO1KtXT+pI+Qb/ZSYiIiLK5yZNmgSZTIZhw4bB0tJS6jj5CpfmIiIiIspnzp07h9atWyM+Ph4AYGBggMmTJ7OQzQSLWSIiIqJ8QqVSYcGCBfjyyy8REBCA+fPnSx0p3+M0AyIiIqJ84MWLF+jbty8CAgIAAH369MH06dMlTpX/sZglIiIiktipU6fQu3dvREZGwszMDCtWrMCAAQO4WkEOsJglvSOEQKIib+6UxTtyERGRrv3xxx/o378/VCoVqlevjh07dqBGjRpSx9IbLGZJrwgh0G31RQRHxEgdhYiISCvc3d1RsmRJdOjQAb/99hssLCykjqRXWMySXklUKCUpZJ2LCt6Ri4iItObevXuoXLkyAMDBwQF///037O3tJU6ln1jMkt4K+q4lzI11X2AqFAqcDAzgvCUiIvpkqampmDt3LubPn48dO3bg66+/BgAWsp+AxSzpLfM8uiOXQibAOpaIiD7V06dP0bt3b5w5cwYAcOnSJXUxS7nHYpaIiIhIx44cOYK+ffsiOjoaRYoUwbp16+Dp6Sl1rAKBN00gIiIi0hGFQoGpU6eibdu2iI6ORt26dXHt2jUWslrEYpaIiIhIR86cOYOff/4ZADBq1ChcuHABlSpVkjhVwcJpBkREREQ60qJFC0yfPh1169ZFt27dpI5TIHFkloiIiEhLUlJS8P333+Pp06fqtvnz57OQ1SGOzBIRERFpwcOHD9GzZ09cuXIFZ8+excmTJ7msYx7gyCwRERHRJ9q7dy/q1q2LK1euoFixYpgwYQIL2TzCYpaIiIgol5KTkzF27Fh07doVb968QcOGDREaGorOnTtLHa3Q4DQDIiIiolx4+vQpOnfujODgYADA5MmTMX/+fMjlcomTFS4sZomIiIhyoVixYkhMTETJkiWxadMmtG/fXupIhRKLWSIiIqIcSkpKgrGxMQwMDGBhYYG9e/fC3NwcpUuXljpaocU5s0REREQ5cPfuXTRo0AALFy5Ut1WuXJmFrMRYzBIRERF9hJ+fH1xcXHD9+nUsX74c8fHxUkei/8diloiIiCgLCQkJGDx4MPr06YP4+Hh8+eWXCAoKgoWFhdTR6P+xmCUiIiLKxO3bt9GgQQP4+PhAJpNh1qxZOHbsGBwcHKSORv/BC8CIiIiIPhAbG4vGjRsjJiYGdnZ28PPzg7u7u9SxKBMcmSUiIiL6gKWlJebOnYuWLVsiNDSUhWw+xmKWiIiICMCNGzcQGhqqfjxq1CgcPXoUtra20oWij2IxS0RERIWaEALr1q1D/fr10a1bN8TGxgIAZDIZDAxYKuV3nDNLREREhVZcXByGDRuGbdu2AQAqVaoEhUIhcSrSBH/dICIiokIpNDQULi4u2LZtGwwNDfHTTz/hr7/+QsmSJaWORhrgyCx9MiEEEhXKPDlXQkrenIeIiAouIQRWr16NCRMmIDk5GWXKlMH27dvRqFEjqaNRLrCYpU8ihEC31RcRHBEjdRQiIqIcEULgzz//RHJyMjp27IiNGzdyNFaPsZilT5KoUEpSyLo6FYeZ3DDPz0tERPrPwMAAmzdvxs6dOzFixAjIZDKpI9EnYDFLWhP0XUuYG+dNgWkmN+QPHyIiyhEhBJYvX447d+5g1apVAIBSpUph5MiREicjbWAxS1pjbmwIc2N+SxERUf4RExODgQMHYt++fQCA7t278wYIBQwrDyIiIiqQLl++jJ49eyIiIgLGxsZYvHgxmjdvLnUs0jIuzUVEREQFihACixcvRpMmTRAREYEKFSrgwoULGD16NKeoFUAcmSUiIqICZeDAgfD19QUA9OjRA2vXroWVlZW0oUhnODJLREREBUrPnj1hZmaGVatWYfv27SxkCziOzBIREZFeU6lUuHfvHqpWrQoAaNOmDcLDw2FraytxMsoLHJklIiIivfXixQu0a9cODRs2RHh4uLqdhWzhwWKWiIiI9NLp06dRp04dHD16FCkpKbhx44bUkUgCLGaJiIhIryiVSsydOxfu7u6IjIxEtWrVcOXKFXTq1EnqaCQBzpklIiIivREVFYU+ffrg+PHjAABvb2+sWLECFhYWEicjqbCYJSIiIr2xbNkyHD9+HObm5li1ahX69esndSSSGItZIiIi0huzZs3CkydPMGPGDPXqBVS4sZgtgIQQSFQo8+RcCSl5cx4iIiqcnj59il9//RU//fQTjIyMYGpqii1btkgdi/IRFrMFjBAC3VZfRHBEjNRRiIiIPsmRI0fQt29fREdHw9LSEjNnzpQ6EuVDXM1A15RK4Ny59/9/7tz7xzqUqFBKUsi6OhWHmdwwz89LREQFj0KhwLRp09C2bVtER0ejTp068PT0lDoW5VMcmdWlPXuAceOAV6+AbduA9u2BkiWBZcuArl11fvqg71rC3DhvCkwzuSFkMlmenIuIiAqux48fw9PTExcuXAAAjBw5EosXL4apqanEySi/YjGrK3v2AN26AUIAZmb/a3/69H37rl06L2jNjQ1hbswuJiIi/XD8+HH06NEDr1+/hqWlJdavX4/u3btLHYvyOVY6uqBUvh+RFQIAIAAkK4EEIxPIjVSATAZ8MwVo2x4w1O7IKS/IIiIifWVnZ4fExES4uLjA398fFSpUkDoS6QEWs7pw9izw5AmA94WsZ7d5uHbFCFNG/pF+uznH8j4bERFRPhIfH6++4UGNGjVw/Phx1KtXDyYmJhInI33BC8B0ITJS/b+JchNcc8j7dfB4QRYREeV3+/btQ7ly5dTzYwHAzc2NhSxphCOzumBvn2nzpXWDYBn35n8Nhw4DXzTVSQRekEVERPlVcnIypkyZguXLlwMAfv31VzRq1EjiVKSvJB+ZXblyJZydnWFqagoXFxecPXs22+39/PxQu3ZtmJubw97eHgMGDMCrV6/yKG0ONW0KlC79fm7sf5gpkmGuSIZ5agrM7Wxg3vwLmBsb6eSLhSwREeVH9+/fR+PGjdWF7KRJk7B161aJU5E+k7SY9ff3x/jx4zFjxgyEhISgadOmaNu2LR49epTp9ufOnUO/fv0waNAg/PPPP9i5cyeuXr2KwYMH53HyjzA0fL/8FpChoFU/XrpU6xd/ERER5Wc7d+5E3bp1ERwcjBIlSuDgwYNYtGgR5HK51NFIj0lazC5ZsgSDBg3C4MGDUa1aNSxduhRlypTBqlWrMt3+0qVLKFeuHMaOHQtnZ2c0adIEw4YNQ1BQUB4nz4GuXd8vv2XvkL69dOk8WZaLiIgoP7lx4wa8vLwQFxeHxo0bIzQ0FO3bt5c6FhUAks2ZTUlJQXBwMKZOnZquvVWrVukmgv9Xo0aNMGPGDBw6dAht27bFixcvsGvXrmw/DMnJyUhOTlY/jo2NBfD+7iIKhUILryQbHTtC0bI18OOp9+fctQuKLxq/H5HV9blJa9K+T3T+/UI6wz7Uf+xD/aZQKFCzZk106dIFlStXxuzZs2FkZMT+1CN5/RnU5DySFbPR0dFQKpWwtbVN125ra4uoqKhM92nUqBH8/PzQs2dPJCUlITU1FZ06dcJvv/2W5XkWLFiAOXPmZGgPCAiAubn5p72IHEhWAmlv84l372By9KjOz0m6ERgYKHUE+kTsQ/3HPtQvFy5cQJ06dWBubg6ZTIZ+/frBwMAAAQEBUkejXMqrz2BCQkKOt5V8NYMPL1QSQmR58dKtW7cwduxYzJw5E61bt0ZkZCQmT56M4cOHw8fHJ9N9pk2bhokTJ6ofx8bGokyZMmjVqhUsLS2190KykJCSiilXTgAA3N3dYWXB2/HpG4VCgcDAQHh4eHBel55iH+o/9qF+SUhIwMSJE7FhwwZ0794dGzduxLFjx9C6dWv2n57K689g2l/Sc0KyYtba2hqGhoYZRmFfvHiRYbQ2zYIFC9C4cWNMnjwZAFCrVi1YWFigadOm+OGHH2CfyZJYJiYmma5XJ5fL86Qz5OJ/hblcbsQPsR7Lq+8Z0h32of5jH+Z/t2/fRo8ePXDz5k3IZDJUq1YNRkbvyw32n/7Ls/pJg3NIdgGYsbExXFxcMgxXBwYGZrnWXEJCAgwM0kc2/P8VAcT/3zqWiIiIpLFp0ya4urri5s2bsLW1RWBgIObMmZPh324ibZL0u2vixIlYv349NmzYgNu3b2PChAl49OgRhg8fDuD9FIF+/fqpt+/YsSP27NmDVatW4cGDBzh//jzGjh2L+vXrw8HBIavTEBERkQ7Fx8fD29sb3t7eSEhIQIsWLRAaGooWLVpIHY0KAUnnzPbs2ROvXr3C3LlzERkZiZo1a+LQoUNwcnICAERGRqZbc9bb2xtxcXFYsWIFvvnmGxQrVgzu7u74+eefpXoJREREhV5CQgICAgJgYGCAOXPmYNq0aeq/nBLpmuQXgI0cORIjR47M9DlfX98MbWPGjMGYMWN0nIqIiIhyqlSpUvD394dKpUKzZs2kjkOFDCexEBERkUbi4uLg5eUFPz8/dVvTpk1ZyJIkWMwSERFRjoWGhsLFxQVbt27F6NGjNVpCiUgXWMwSERHRRwkhsGrVKjRs2BBhYWEoXbo0Dh48mCdrthNlR/I5s0RERJS/vX37FkOGDMHOnTsBAB06dICvry9KliwpcTIiFrNERESUjfj4eLi4uOD+/fswMjLCzz//jAkTJmR5t06ivMZpBkRERJQlCwsLfP3113BycsK5c+cwceJEFrKUr7CYJSIionRiYmLw5MkT9eMffvgBISEhaNCggYSpiDLHYpaIiIjULl++jLp166Jbt25QKBQAALlcjuLFi0ucjChzLGaJiIgIQggsXrwYTZo0QUREBF6+fImnT59KHYvoo1jMEhERFXKvXr1Cp06dMGnSJKSmpqJ79+64du0aypUrJ3U0oo9iMUtERFSInT9/HnXq1MHBgwdhYmKCVatWwd/fH1ZWVlJHI8oRLs1FRERUSAkhMGHCBDx58gSVKlXCjh07UKdOHaljEWmEI7NERESFlEwmg5+fHwYNGoTg4GAWsqSXWMwSEREVIqdPn8ayZcvUjytVqoT169ejaNGiEqYiyj1OMyAiIioElEolfvzxR8yePRtCCNSrVw9NmzaVOhbRJ2MxS0REVMBFRUWhT58+OH78OACgf//+qFevnsSpiLSDxSwREVEBdvz4cXh5eeH58+cwNzfHypUr0b9/f6ljEWkN58wSEREVUAsWLICHhweeP3+OmjVr4urVqyxkqcBhMUtERFRA2djYQAiBwYMH4/Lly6hevbrUkYi0jtMMiIiICpB3796hSJEiAICBAweiSpUqaNKkicSpiHSHI7NEREQFQGpqKqZNm4aaNWvi9evXAN6vI8tClgo6FrNERER67vHjx/jyyy/x008/ISIiArt375Y6ElGeYTFLRESkx/766y/UqVMH58+fh6WlJfz9/TFkyBCpYxHlGRazREREeiglJQWTJk1Chw4d8Pr1a7i4uODatWvo0aOH1NGI8hSLWSIiIj00e/ZsLF68GAAwduxYnD9/HhUqVJA4FVHeYzFLRESkhyZNmoTatWtjz549WLZsGUxMTKSORCQJFrNERER6IDk5GZs3b4YQAgBQokQJXLt2DV999ZXEyYikxXVmiYiI8rkHDx6gR48eCA4ORnJysvoCLwMDjkkR8VNARESUj+3atQt169ZFcHAwSpQoAXt7e6kjEeUrLGaJiIjyoaSkJIwaNQrdu3dHbGwsGjVqhNDQUHTo0EHqaET5CotZIiKifCYsLAxubm5YuXIlAGDq1Kk4deoUypQpI3EyovyHc2aJiIjymSdPnuDvv/+GtbU1tmzZgjZt2kgdiSjfYjFLRESUDwghIJPJAADNmzeHr68vWrRoAUdHR4mTEeVvnGZAREQksdu3b6NJkya4d++euq1fv34sZIlygMUsERGRhDZt2gRXV1dcuHABY8eOlToOkd5hMUtERCSB+Ph4eHt7w9vbGwkJCXB3d4evr6/UsYj0DotZIiKiPHbz5k18/vnn2LRpEwwMDDB37lwEBATAzs5O6mhEeocXgBEREeWhy5cvo3nz5khMTIS9vT22bt2KL7/8UupYRHqLxSwREVEeqlevHmrXrg1LS0ts2bIFNjY2Ukci0mssZomIiHTs1q1bqFSpEuRyOeRyOf766y8UK1YMBgac7Uf0qfgpIiIi0hEhBFavXo169ephxowZ6vYSJUqwkCXSEo7MEhER6UBsbCyGDBmCHTt2AHi/lqxSqYShoaHEyYgKFv5aSEREpGXBwcGoV68eduzYASMjIyxatAj79+9nIUukAxyZJSIi0hIhBFasWIFJkyYhJSUFTk5O2L59Oxo2bCh1NKICiyOzREREWvL06VNMnz4dKSkp6NKlC0JCQljIEukYR2aJiIi0pHTp0li3bh1evHiBMWPGQCaTSR2JqMBjMUtERJRLQgj8+uuvqFu3Lpo3bw4A8PT0lDgVUeHCYpaIiCgXXr9+DW9vbxw4cAB2dna4desWihcvLnUsokKHxSwREZGGLly4AE9PTzx+/BgmJiaYOXMmihUrJnUsokKJF4ARERHlkEqlws8//4wvvvgCjx8/RqVKlXDp0iWMGDGC82OJJMKRWSIiohxITEzE119/jcOHDwMAevXqhTVr1qBo0aISJyMq3DgyS0RElAOmpqYoVqwYTE1NsXbtWvj5+bGQJcoHWMwSERFlQalUIj4+HgAgk8mwZs0aXL16FUOGDOG0AqJ8gsUsERFRJp4/f442bdqgT58+EEIAAIoWLYqaNWtKnIyI/otzZomIiD5w4sQJeHl5ISoqCubm5rhz5w6qVasmdSwiygRHZomIiP6fUqnErFmz0LJlS0RFRaFGjRq4evUqC1mifIwjs0RERACePXsGLy8vnDp1CgAwaNAgLF++HObm5tIGI6JssZglIqJCTwiBzp07IygoCBYWFlizZg28vLykjkVEOZCraQapqak4duwY1qxZg7i4OADvf6N99+6dVsMRERHlBZlMhuXLl8PFxQXXrl1jIUukRzQemY2IiECbNm3w6NEjJCcnw8PDA0WLFsXChQuRlJSE1atX6yInERGRVj158gShoaHo0KEDAMDNzQ1Xr17lkltEekbjkdlx48bB1dUVMTExMDMzU7d/9dVXOH78uFbDERER6cKhQ4dQp04d9OjRAzdv3lS3s5Al0j8aj8yeO3cO58+fh7Gxcbp2JycnPH36VGvBiIiItE2hUGDGjBlYtGgRAKBevXrpBmaISP9oXMyqVCoolcoM7U+ePOFt/YiIKN+KiIiAp6cnLl26BAAYM2YMFi1aBBMTE4mTEdGn0HiagYeHB5YuXap+LJPJ8O7dO8yaNQvt2rXTZjYiIiKt2L9/P+rWrYtLly7BysoKu3fvxvLly1nIEhUAGo/M/vrrr2jevDmqV6+OpKQk9O7dG2FhYbC2tsa2bdt0kZGIiOiTXLt2DTExMahfvz62b98OZ2dnqSMRkZZoXMw6ODggNDQU27dvR3BwMFQqFQYNGgQvLy/OOyIionxDCKG+oGvmzJmwsbHBkCFDMlzzQUT6TeNpBmfOnIFcLseAAQOwYsUKrFy5EoMHD4ZcLseZM2d0kZGIiEgju3fvhru7O5KSkgAAhoaGGDVqFAtZogJI42K2efPmeP36dYb2t2/fonnz5loJRURElBtJSUkYPXo0unXrhlOnTuH333+XOhIR6ZjG0wz++2eb/3r16hUsLCy0EoqIiEhTYWFh6NmzJ0JCQgAA3377LcaOHStxKiLStRwXs127dgXwfvUCb2/vdFeAKpVKXL9+HY0aNdJ+QiIioo/Yvn07hgwZgnfv3sHa2hqbN29G27ZtpY5FRHkgx8WslZUVgPcjs0WLFk13sZexsTEaNmyIIUOGaD8hERFRNhYvXoxJkyYBAJo2bYpt27bB0dFR4lRElFdyXMxu3LgRAFCuXDlMmjSJUwqIiChf+Prrr/Hjjz9i5MiRmDVrFoyMNJ5BR0R6TONP/KxZs3SRg4iIKMdCQkJQt25dAO8HWcLCwlCiRAmJUxGRFDRezQAAdu3ahR49eqBhw4aoV69eui9NrVy5Es7OzjA1NYWLiwvOnj2b7fbJycmYMWMGnJycYGJiggoVKmDDhg25eRlERKRn4uPjMXDgQNSrVw+HDh1St7OQJSq8NC5mly9fjgEDBsDGxgYhISGoX78+SpYsiQcPHmg82d7f3x/jx4/HjBkzEBISgqZNm6Jt27Z49OhRlvv06NEDx48fh4+PD+7evYtt27ahatWqmr4MIiLSM48ePUKjRo2wceNGGBgY4O7du1JHIqJ8QONpBitXrsTatWvRq1cvbNq0CVOmTEH58uUxc+bMTNefzc6SJUswaNAgDB48GACwdOlSHD16FKtWrcKCBQsybH/kyBGcPn0aDx48UP8WXq5cOU1fAhER6REhBHx9fTFp0iSkpKTAzs4O27Ztw5dffil1NCLKBzQuZtN+MwYAMzMzxMXFAQD69u2Lhg0bYsWKFTk6TkpKCoKDgzF16tR07a1atcKFCxcy3efPP/+Eq6srFi5ciC1btsDCwgKdOnXCvHnzsryVbnJyMpKTk9WPY2NjAQAKhQIKhSJHWT+FQpGa7v/z4pykXWl9xr7TX+xD/fXu3TuMHj0aW7duBQC0aNECmzZtgo2NDftTj/AzqP/yug81OY/GxaydnR1evXoFJycnODk54dKlS6hduzbCw8MhhMjxcaKjo6FUKmFra5uu3dbWFlFRUZnu8+DBA5w7dw6mpqbYu3cvoqOjMXLkSLx+/TrLebMLFizAnDlzMrQHBATA3Nw8x3lzK1kJpL3NJ06cgImhzk9JOhIYGCh1BPpE7EP9c/HiRWzduhUGBgbo3bs3unbtiqCgIKljUS7xM6j/8qoPExIScrytxsWsu7s7Dhw4gHr16mHQoEGYMGECdu3ahaCgIPWNFTTx4d3EsrrDGACoVCrIZDL4+fmp171dsmQJunXrht9//z3T0dlp06Zh4sSJ6sexsbEoU6YMWrVqBUtLS43zaiohJRVTrpwA8P69s7Iw1fk5SbsUCgUCAwPh4eEBuVwudRzKBfah/mrXrh2EEPDw8EB8fDz7UE/xM6j/8roP0/6SnhMaF7Nr166FSqUCAAwfPhwlSpTAuXPn0LFjRwwfPjzHx7G2toahoWGGUdgXL15kGK1NY29vD0dHR3UhCwDVqlWDEAJPnjxBpUqVMuxjYmKS7m5laeRyeZ50hlz8rzCXy434IdZjefU9Q7rDPsz/YmNjMW3aNMyaNQs2NjYAgJ9//hkKhQKHDh1iH+o59p/+y7P6SYNzaLyagYGBQboFqXv06IHly5dj7NixePnyZY6PY2xsDBcXlwzD1YGBgVneFrdx48Z49uwZ3r17p267d+8eDAwMULp0aQ1fCRER5SfXrl1DvXr1sHLlSgwaNEjqOESkJ3K1zuyHoqKiMGbMGFSsWFGj/SZOnIj169djw4YNuH37NiZMmIBHjx6pR3inTZuGfv36qbfv3bs3SpYsiQEDBuDWrVs4c+YMJk+ejIEDB2Z5ARgREeVvQgisWLECbm5uuH//PsqWLYvp06dLHYuI9ESOi9k3b97Ay8sLpUqVgoODA5YvXw6VSoWZM2eifPnyuHTpksY3L+jZsyeWLl2KuXPnok6dOjhz5gwOHToEJycnAEBkZGS6NWeLFCmCwMBAvHnzBq6urvDy8kLHjh2xfPlyjc5LRET5w5s3b9CtWzeMGTMGKSkp6NSpE0JCQuDm5iZ1NCLSEzmeMzt9+nScOXMG/fv3x5EjRzBhwgQcOXIESUlJOHz4MJo1a5arACNHjsTIkSMzfc7X1zdDW9WqVXk1JBFRAXDnzh20a9cO4eHhkMvlWLRoEcaOHZvlRcBERJnJcTH7119/YePGjWjZsiVGjhyJihUronLlyli6dKkO4xERUUHl4OAAQ0NDODs7w9/fH59//rnUkYhID+W4mH327BmqV68OAChfvjxMTU3Vd+4iIiLKidjYWBQtWhQymQyWlpY4ePAgbG1tUaxYMamjEZGeyvGcWZVKlW6ZBENDQ1hYWOgkFBERFTwXL15EzZo1090pskqVKixkieiT5HhkVggBb29v9ZqtSUlJGD58eIaCds+ePdpNSEREek2lUuGXX37B9OnToVQqsWbNGgwfPpzrjRKRVuS4mO3fv3+6x3369NF6GCIiKlhevnyJ/v374/DhwwAAT09PrFmzhoUsEWlNjovZjRs36jIHEREVMGfOnEGvXr3w7NkzmJqaYvny5Rg8eDBXKyAirdL4drZEREQfExkZiVatWiE5ORlVqlTBjh07UKtWLaljEVEBxGKWiIi0zt7eHnPmzME///yDlStXokiRIlJHIqICisUsERFpxcmTJ2FjY4MaNWoAAKZMmQIAnFZARDqV46W5iIiIMqNUKjF79my0aNECPXr0QHx8PID3RSwLWSLSNY7MEhFRrkVGRsLLywsnT54EADRs2JAFLBHlqVyNzG7ZsgWNGzeGg4MDIiIiAABLly7F/v37tRqOiIjyr8DAQNSpUwcnT56EhYUFtmzZAh8fH5ibm0sdjYgKEY2L2VWrVmHixIlo164d3rx5A6VSCQAoVqwYli5dqu18RESUz6SmpuK7775D69at8eLFC9SqVQtBQUFcf5yIJKFxMfvbb79h3bp1mDFjBgwNDdXtrq6uuHHjhlbDERFR/iOTyXDu3DkIITBs2DBcunQJVatWlToWERVSGs+ZDQ8PR926dTO0m5iYqCf9ExFRwSOEgEwmg6GhIbZu3Ypz586hR48eUsciokJO45FZZ2dnhIaGZmg/fPgwqlevro1MRESUjygUCkyZMgXjx49Xtzk4OLCQJaJ8QeOR2cmTJ2PUqFFISkqCEAJXrlzBtm3bsGDBAqxfv14XGYmISCKPHj2Cp6cnLl68CAAYOHAgateuLXEqIqL/0biYHTBgAFJTUzFlyhQkJCSgd+/ecHR0xLJly+Dp6amLjEREJIE///wT3t7eiImJgZWVFXx8fFjIElG+k6t1ZocMGYIhQ4YgOjoaKpUKNjY22s5FREQSSUlJwbfffqteoebzzz+Hv78/nJ2dpQ1GRJQJjefMzpkzB/fv3wcAWFtbs5AlIipAhBDo2LGjupCdMGECzp07x0KWiPItjYvZ3bt3o3LlymjYsCFWrFiBly9f6iIXERFJQCaTYdiwYShevDj279+PJUuWwNjYWOpYRERZ0riYvX79Oq5fvw53d3csWbIEjo6OaNeuHbZu3YqEhARdZCQiIh1KSkpKt054165d8eDBA3Tq1EnCVEREOZOr29nWqFEDP/74Ix48eICTJ0/C2dkZ48ePh52dnbbzERGRDv37779o1KgR3N3d8fTpU3V7sWLFpAtFRKSBXBWz/2VhYQEzMzMYGxtDoVBoIxMREeUBf39/1KtXDyEhIRBCIDw8XOpIREQay1UxGx4ejvnz56N69epwdXXFtWvXMHv2bERFRWk7HxERaVliYiKGDx8OT09PxMXFoUmTJggNDUWTJk2kjkZEpDGNl+Zyc3PDlStX8Nlnn2HAgAHqdWaJiCj/u3v3Lnr06IHr169DJpNh+vTpmD17NoyMcrVSIxGR5DT+6dW8eXOsX78eNWrU0EUeIiLSoWXLluH69euwsbHBH3/8AQ8PD6kjERF9Eo2L2R9//FEXOYiIKA8sWrQIqampmDNnDuzt7aWOQ0T0yXJUzE6cOBHz5s2DhYUFJk6cmO22S5Ys0UowIiL6dP/88w/WrFmDpUuXwsDAABYWFli7dq3UsYiItCZHxWxISIh6pYKQkBCdBiIiok8nhICvry9GjRqFxMRElC9fHuPHj5c6FhGR1uWomD158mSm/09ERPnPu3fvMHLkSGzZsgUA0KpVK/Tu3VviVEREuqHx0lwDBw5EXFxchvb4+HgMHDhQK6GIiCh3rl+/DldXV2zZsgUGBgaYP38+Dh8+DBsbG6mjERHphMbF7KZNm5CYmJihPTExEZs3b9ZKKCIi0py/vz8aNGiAu3fvwtHREadOncL06dNhYPDJ98chIsq3cryaQWxsLIQQEEIgLi4Opqam6ueUSiUOHTrE3/yJiCRUsWJFqFQqtG3bFps3b4a1tbXUkYiIdC7HxWyxYsUgk8kgk8lQuXLlDM/LZDLMmTNHq+GIiCh7b968QbFixQAALi4uuHjxIurUqcPRWCIqNHJczJ48eRJCCLi7u2P37t0oUaKE+jljY2M4OTnBwcFBJyGJiCg9IQRWrlyJ6dOn4+TJk6hXrx4AqP9LRFRY5LiYbdasGQAgPDwcZcuWhUwm01koIiLK2ps3bzBkyBDs2rULAODr68silogKrRwVs9evX0fNmjVhYGCAt2/f4saNG1luW6tWLa2FIyKi9K5evYqePXsiPDwccrkcCxcuxLhx46SORUQkmRwVs3Xq1EFUVBRsbGxQp04dyGQyCCEybCeTyaBUKrUekoiosBNCYNmyZZgyZQoUCgWcnZ3h7++Pzz//XOpoRESSylExGx4ejlKlSqn/n4iI8tbu3bsxYcIEAMDXX3+N9evXqy/8IiIqzHJUzDo5OWX6/0RElDe6du2KTp06oVWrVhg5ciSvWyAi+n+5umnCX3/9pX48ZcoUFCtWDI0aNUJERIRWwxERFVYqlQrr1q1DQkICAMDAwAD79u3DqFGjWMgSEf2HxsXsjz/+CDMzMwDAxYsXsWLFCixcuBDW1tbqP4EREVHuRUdHo2PHjhg6dCjGjBmjbmcRS0SUUY6X5krz+PFjVKxYEQCwb98+dOvWDUOHDkXjxo3x5ZdfajsfEVGhcvbsWfTq1QtPnz6FqakpGjRoACEEC1kioixoPDJbpEgRvHr1CgAQEBCAli1bAgBMTU2RmJio3XRERIWESqXCjz/+iObNm+Pp06eoUqUKLl++jKFDh7KQJSLKhsYjsx4eHhg8eDDq1q2Le/fuoX379gCAf/75B+XKldN2PiKiAu/Fixfo27cvAgICAAB9+vTBqlWrUKRIEYmTERHlfxqPzP7+++9wc3PDy5cvsXv3bpQsWRIAEBwcjF69emk9IBFRQadQKHDt2jWYmZnBx8cHmzdvZiFLRJRDGo/MFitWDCtWrMjQPmfOHK0EIiIqDP47D9bR0RE7d+5EqVKlUKNGDYmTERHpF42LWeD9fcF9fHxw+/ZtyGQyVKtWDYMGDYKVlZW28xERFThRUVHw8vLC6NGj8dVXXwEAL6AlIsoljacZBAUFoUKFCvj111/x+vVrREdH49dff0WFChVw7do1XWQkIiowjh07htq1a+PEiRMYO3YsUlJSpI5ERKTXNC5mJ0yYgE6dOuHhw4fYs2cP9u7di/DwcHTo0AHjx4/XQUQiIv2XmpqK7777Dq1atcKLFy9Qq1YtHDt2DMbGxlJHIyLSaxpPMwgKCsK6detgZPS/XY2MjDBlyhS4urpqNRwRUUHw9OlT9OrVC2fPngUADBs2DL/++qv6BjRERJR7GhezlpaWePToEapWrZqu/fHjxyhatKjWghERFQQvX75EnTp1EB0djaJFi2Lt2rXw9PSUOhYRUYGh8TSDnj17YtCgQfD398fjx4/x5MkTbN++HYMHD+bSXEREHyhVqhR69uyJunXrIjg4mIUsEZGWaTwy+8svv0Amk6Ffv35ITU0FAMjlcowYMQI//fST1gMSEembR48eQS6Xw97eHgCwePFiCCFgamoqcTIiooJH45FZY2NjLFu2DDExMQgNDUVISAhev36NX3/9FSYmJrrISESkNw4cOIA6deqgV69e6l/4TUxMWMgSEelIjovZhIQEjBo1Co6OjrCxscHgwYNhb2+PWrVqwdzcXJcZiYjyvZSUFHzzzTfo1KkTYmJikJCQgJiYGKljEREVeDkuZmfNmgVfX1+0b98enp6eCAwMxIgRI3SZjYhIL4SHh6Np06ZYsmQJgPdLGJ47dw6lSpWSOBkRUcGX4zmze/bsgY+Pj/rihT59+qBx48ZQKpUwNDTUWUAiovxsz549GDhwIN6+fYvixYvD19cXnTp1kjoWEVGhkeOR2cePH6Np06bqx/Xr14eRkRGePXumk2BERPmdQqHA999/j7dv38LNzQ0hISEsZImI8liOi1mlUpnhTjVGRkbqCxyIiAobuVwOf39/TJs2DadPn4aTk5PUkYiICp0cTzMQQsDb2zvdigVJSUkYPnw4LCws1G179uzRbkIionxkx44dePHiBUaPHg0AqFmzJn788UeJUxERFV45Lmb79++foa1Pnz5aDUNElF8lJiZiwoQJWLNmDQwNDdG4cWPUrVtX6lhERIVejovZjRs36jIHEVG+dffuXfTo0QPXr1+HTCbD1KlT8dlnn0kdi4iIkIs7gBERFSZ//PEHhg8fjvj4eNjY2OCPP/6Ah4eH1LGIiOj/aXwHMCKiwmLkyJHo27cv4uPj0bx5c4SGhrKQJSLKZ1jMEhFloWrVqpDJZJg9ezYCAwNhb28vdSQiIvoApxkQEf3H69evUaJECQDAmDFj0KxZM9SuXVviVERElBWOzBIRAXj37h369++PBg0aIDY2FgAgk8lYyBIR5XO5Kma3bNmCxo0bw8HBAREREQCApUuXYv/+/VoNR0SUF27cuIHPP/8cmzdvxoMHD3Dy5EmpIxERUQ5pXMyuWrUKEydORLt27fDmzRsolUoAQLFixbB06VJt5yMi0hkhBNatW4f69evjzp07cHR0xKlTp9C5c2epoxERUQ5pXMz+9ttvWLduHWbMmAFDQ0N1u6urK27cuKHVcEREuhIXFwcvLy8MHToUSUlJaNu2LUJDQ9G0aVOpoxERkQY0LmbDw8MzveuNiYkJ4uPjtRKKiEjXvvnmG2zbtg2GhoZYuHAhDh48CGtra6ljERGRhjQuZp2dnREaGpqh/fDhw6hevbrGAVauXAlnZ2eYmprCxcUFZ8+ezdF+58+fh5GREerUqaPxOYmIfvjhBzRs2BBnz57F5MmTYWDA62GJiPSRxj+9J0+ejFGjRsHf3x9CCFy5cgXz58/H9OnTMXnyZI2O5e/vj/Hjx2PGjBkICQlB06ZN0bZtWzx69Cjb/d6+fYt+/fqhRYsWmsYnokIqPj4ePj4+6sc2Nja4cOEC3NzcJExFRESfSuN1ZgcMGIDU1FRMmTIFCQkJ6N27NxwdHbFs2TJ4enpqdKwlS5Zg0KBBGDx4MID3KyIcPXoUq1atwoIFC7Lcb9iwYejduzcMDQ2xb98+TV8CERUywcHBmDhxIp4/fw5LS0v07t0bwPult4iISL/l6qYJQ4YMwZAhQxAdHQ2VSgUbGxuNj5GSkoLg4GBMnTo1XXurVq1w4cKFLPfbuHEj7t+/jz/++AM//PDDR8+TnJyM5ORk9eO09SMVCgUUCoXGuTWlUKSm+/+8OCdpV1qfse/0jxACK1aswNSpU6FQKODk5IRy5cqxL/UQP4f6jf2n//K6DzU5zyfdAexTLpaIjo6GUqmEra1tunZbW1tERUVluk9YWBimTp2Ks2fPwsgoZ9EXLFiAOXPmZGgPCAiAubm55sE1lKwE0t7mEydOwMQw280pHwsMDJQ6Amng3bt3+O2333D58mUAQMOGDTF69Gi8fPkShw4dkjgd5RY/h/qN/af/8qoPExIScrytxsWss7Nztn+ae/DggUbH+/BYQohMj69UKtG7d2/MmTMHlStXzvHxp02bhokTJ6ofx8bGokyZMmjVqhUsLS01ypobCSmpmHLlBADA3d0dVhamOj8naZdCoUBgYCA8PDwgl8uljkM5cOXKFYwbNw4REREwNjbGggULUL58ebRq1Yp9qKf4OdRv7D/9l9d9mPaX9JzQuJgdP358uscKhQIhISE4cuSIRheAWVtbw9DQMMMo7IsXLzKM1gLv14QMCgpCSEgIRo8eDQBQqVQQQsDIyAgBAQFwd3fPsJ+JiQlMTEwytMvl8jzpDLn4X2EulxvxQ6zH8up7hj7d27dvERERgQoVKmDHjh347LPPcOjQIfZhAcA+1G/sP/2XZ/WTBufQuJgdN25cpu2///47goKCcnwcY2NjuLi4IDAwEF999ZW6PTAwMNO771haWma4KcPKlStx4sQJ7Nq1C87Ozjk+NxEVPP/9q067du2wdetWtG/fHpaWlpynR0RUgGltYcW2bdti9+7dGu0zceJErF+/Hhs2bMDt27cxYcIEPHr0CMOHDwfwfopAv3793gc1MEDNmjXTfdnY2MDU1BQ1a9aEhYWFtl4KEemZc+fOoXbt2oiIiFC39erVK0+mEhERkbQ+6QKw/9q1axdKlCih0T49e/bEq1evMHfuXERGRqJmzZo4dOgQnJycAACRkZEfXXOWiAovlUqFn3/+Gd9//z2USiW+++47bNmyRepYRESUhzQuZuvWrZvuAi0hBKKiovDy5UusXLlS4wAjR47EyJEjM33O19c3231nz56N2bNna3xOItJ/L168QN++fREQEAAA6NOnD1atWiVxKiIiymsaF7NdunRJ99jAwAClSpXCl19+iapVq2orFxFRlk6dOoXevXsjMjISZmZm+P333+Ht7c2bIBARFUIaFbOpqakoV64cWrduDTs7O11lIiLK0uHDh9GhQweoVCpUr14dO3bsQI0aNaSORUREEtHoAjAjIyOMGDEi3R21iIjyUvPmzVGrVi0MGDAAV65cYSFLRFTIaTzNoEGDBggJCVFfpEVEpGuXL1+Gq6srDA0NYWpqijNnzqBo0aJSxyIionxA42J25MiR+Oabb/DkyRO4uLhkWBKrVq1aWgtHRIVbamoq5syZg/nz52PmzJnqCz5ZyBIRUZocF7MDBw7E0qVL0bNnTwDA2LFj1c/JZDL1guVKpVL7KYmo0Hn69Cl69+6NM2fOAACeP3+e5e2uiYio8MpxMbtp0yb89NNPCA8P12UeIiIcOXIEffv2RXR0NIoUKYJ169bB09NT6lhERJQP5biYFUIAAOfKEpHOKBQKzJw5Ez/99BOA9+ta+/v7o1KlShInIyKi/Eqj1Qz45z0i0qUHDx5g6dKlAIBRo0bhwoULLGSJiChbGl0AVrly5Y8WtK9fv/6kQERUeFWpUgVr1qyBubk5unXrJnUcIiLSAxoVs3PmzIGVlZWushBRIZOSkoLvvvsOX331Fdzc3AAA/fr1kzgVERHpE42KWU9PT9jY2OgqCxEVIg8fPoSnpycuX76MHTt24M6dOzA1NZU6FhER6Zkcz5nlfFki0pa9e/eibt26uHz5MooVK4Zly5axkCUiolzJcTGbtpoBEVFuJScnY+zYsejatSvevHmDhg0bIjQ0FJ07d5Y6GhER6akcTzNQqVS6zEFEBVxMTAw8PDwQHBwMAJg8eTLmz58PuVwucTIiItJnGt/OlogoN4oVK4bSpUvj4cOH2LRpE9q3by91JCIiKgBYzBKRziQlJSE1NRVFihSBTCbDhg0bkJCQgNKlS0sdjYiICgiNbppARJRT9+7dQ8OGDTF06FD1nPsSJUqwkCUiIq1iMUtEWrd161a4uLjg77//xrFjx/D06VOpIxERUQHFYpaItCYhIQFDhgyBl5cX3r17hy+//BKhoaEcjSUiIp1hMUtEWnH79m00aNAA69evh0wmw6xZs3Ds2DE4ODhIHY2IiAowXgBGRJ8sNTUVHTt2xP3792FnZwc/Pz+4u7tLHYuIiAoBjswS0SczMjLC2rVr0bp1a4SGhrKQJSKiPMNilohy5caNGzh48KD6sbu7Ow4fPgxbW1sJUxERUWHDYpaINCKEwPr161G/fn306tULYWFh6udkMpmEyYiIqDBiMUtEORYXF4c+ffpgyJAhSEpKQpMmTVCsWDGpYxERUSHGYpaIciQ0NBQuLi7YunUrDA0N8fPPP+Ovv/5CqVKlpI5GRESFGFczIKKPWr16NcaPH4/k5GSUKVMG27dvR6NGjaSORURExJFZIvq4+/fvIzk5GR07dkRISAgLWSIiyjc4MktEmVKpVDAweP/77o8//ojatWvDy8uLF3kREVG+wpFZIkpHCIFly5bB3d0dCoUCACCXy9GnTx8WskRElO+wmCUitZiYGHTt2hXjx4/H6dOnsW3bNqkjERERZYvTDIgIAHD58mX07NkTERERMDY2xuLFi9G3b1+pYxEREWWLI7NEhZxKpcLixYvRpEkTREREoEKFCrhw4QJGjx7NaQVERJTvsZglKuSmTJmCSZMmITU1FT169MC1a9fg4uIidSwiIqIcYTFLVMgNGTIE1tbWWL16NbZv3w5LS0upIxEREeUY58wSFTIqlQoXLlxAkyZNAABVqlTBw4cPYWFhIXEyIiIizXFklqgQefHiBdq1a4dmzZrh1KlT6nYWskREpK84MktUSJw+fRq9evVCZGQkzMzMEBkZKXUkIiKiT8aRWaICTqlUYt68eXB3d0dkZCSqVauGK1euoFevXlJHIyIi+mQcmSUqwKKiotCnTx8cP34cAODt7Y0VK1ZwWgERERUYLGaJCrDDhw/j+PHjMDc3x6pVq9CvXz+pIxEREWkVi1miAszb2xsPHjxA7969Ua1aNanjEBERaR3nzBIVIM+ePUOfPn0QExMDAJDJZJg3bx4LWSIiKrA4MktUQBw5cgR9+/ZFdHQ0AOCPP/6QOBEREZHucWSWSM+lpqZi2rRpaNu2LaKjo1GnTh3MmjVL6lhERER5giOzRHrs8ePH6NWrF86fPw8AGDlyJBYvXgxTU1OJkxEREeUNFrNEeurSpUto3749Xr9+DUtLS/j4+KBbt25SxyIiIspTLGaJ9FTlypVhYWGB8uXLw9/fH+XLl5c6EhERUZ5jMUukR168eIFSpUpBJpOhRIkSOH78OMqWLQsTExOpoxEREUmCF4AR6Ym9e/eiSpUq2LBhg7qtUqVKLGSJiKhQYzFLlM8lJydj7Nix6Nq1K968eQM/Pz8IIaSORURElC+wmCXKx+7fv4/GjRvjt99+AwBMmjQJR48ehUwmkzgZERFR/sA5s0T51M6dOzF48GDExsaiRIkS2Lx5M9q3by91LCIionyFxSxRPnTv3j14enpCpVKhcePG2LZtG8qUKSN1LCIionyHxSxRPlS5cmXMnDkTycnJmDt3LoyM+FElIiLKDP+FJMontm3bBldXV1SqVAkAeEtaIiKiHOAFYEQSS0hIwODBg9G7d2/07NkTSUlJUkciIiLSGxyZJZLQ7du30aNHD9y8eRMymQwdO3aEXC6XOhYREZHeYDFLJJFNmzZh5MiRSEhIgK2tLfz8/NCiRQupYxEREekVFrNEeSwhIQEjRozA5s2bAQAtWrTAH3/8ATs7O4mTERER6R/OmSXKY0ZGRrhz5w4MDAwwb948HD16lIUsERFRLnFkligPCCEghICBgQGMjY3h7++PiIgINGvWTOpoREREeo0js0Q6FhcXhz59+mDatGnqtnLlyrGQJSIi0gKOzBLpUGhoKHr06IGwsDAYGRlhxIgRKFeunNSxiIiICgyOzBLpgBACq1atQsOGDREWFobSpUvj1KlTLGSJiIi0jCOzRFr29u1bDBkyBDt37gQAdOjQAb6+vihZsqTEyYiIiAoeFrNEWqRSqdCsWTP8/fffMDIyws8//4wJEyZAJpNJHY2IiKhA4jQDIi0yMDDA5MmT4eTkhHPnzmHixIksZImIiHSIxSzRJ4qJiUFoaKj6sZeXF27duoUGDRpIF4qIiKiQYDFL9AkuX76MunXrol27dnj58qW63dzcXMJUREREhQeLWaJcEEJg8eLFaNKkCSIiImBmZoYXL15IHYuIiKjQ4QVgRBp69eoVvL29cfDgQQBA9+7dsW7dOlhZWUmcjIiIqPCRfGR25cqVcHZ2hqmpKVxcXHD27Nkst92zZw88PDxQqlQpWFpaws3NDUePHs3DtFTYnT9/HnXq1MHBgwdhYmKClStXwt/fn4UsERGRRCQtZv39/TF+/HjMmDEDISEhaNq0Kdq2bYtHjx5luv2ZM2fg4eGBQ4cOITg4GM2bN0fHjh0REhKSx8mpsFq1ahWePHmCSpUq4dKlSxgxYgRXKyAiIpKQpNMMlixZgkGDBmHw4MEAgKVLl+Lo0aNYtWoVFixYkGH7pUuXpnv8448/Yv/+/Thw4ADq1q2bF5GpkFu5ciVsbW0xe/ZsFC1aVOo4REREhZ5kxWxKSgqCg4MxderUdO2tWrXChQsXcnQMlUqFuLg4lChRIsttkpOTkZycrH4cGxsLAFAoFFAoFLlIrhmFIjXd/+fFOUl7zpw5A39/f7Rr1w4KhQJmZmb46aefAIB9qUfS+op9pr/Yh/qN/af/8roPNTmPZMVsdHQ0lEolbG1t07Xb2toiKioqR8dYvHgx4uPj0aNHjyy3WbBgAebMmZOhPSAgIE+WT0pWAmlv84kTJ2BiqPNTkhYolUrs2rUL/v7+UKlUMDU15XSCAiAwMFDqCPSJ2If6jf2n//KqDxMSEnK8reSrGXxYIAghclQ0bNu2DbNnz8b+/fthY2OT5XbTpk3DxIkT1Y9jY2NRpkwZtGrVCpaWlrkPnkMJKamYcuUEAMDd3R1WFqY6Pyd9mqioKHh7e+PEiff95uXlhcaNG8PDwwNyuVzidJQbCoUCgYGB7EM9xj7Ub+w//ZfXfZj2l/SckKyYtba2hqGhYYZR2BcvXmQYrf2Qv78/Bg0ahJ07d6Jly5bZbmtiYgITE5MM7XK5PE86Qy7+V5jL5Ub8EOdzx48fh5eXF54/fw5zc3OsXLkSvXv3xqFDh/Lse4Z0h32o/9iH+o39p//yrH7S4BySrWZgbGwMFxeXDMPVgYGBaNSoUZb7bdu2Dd7e3ti6dSvat2+v65hUiCxbtgweHh54/vw5atasiaCgIPTv31/qWERERJQNSacZTJw4EX379oWrqyvc3Nywdu1aPHr0CMOHDwfwforA06dPsXnzZgDvC9l+/fph2bJlaNiwoXpU18zMjOt80if7/PPPYWBggAEDBmDZsmW8JS0REZEekLSY7dmzJ169eoW5c+ciMjISNWvWxKFDh+Dk5AQAiIyMTLfm7Jo1a5CamopRo0Zh1KhR6vb+/fvD19c3r+NTAfD8+XP1tJZGjRrh5s2bqFq1qsSpiIiIKKckvwBs5MiRGDlyZKbPfVignjp1SveBqFBITU3F999/j99++w2XL19GjRo1AICFLBERkZ6RvJglymuPHz9Gr169cP78eQDAgQMH1MUsERER6RcWs1So/PXXX+jXrx9ev34NS0tLrFu3Ltt1iomIiCh/k2w1A6K8pFAoMGnSJHTo0AGvX7+Gi4sLrl27xkKWiIhIz7GYpULBx8cHixcvBgCMHTsW58+fR4UKFSRORURERJ+K0wyoUBg8eDCOHj2Kfv364auvvpI6DhEREWkJR2apQEpJScGiRYuQnJwMADAyMsLevXtZyBIRERUwHJmlAufBgwfo2bMngoKC8OjRI/z2229SRyIiIiId4cgsFSi7du1C3bp1ERQUhBIlSqB169ZSRyIiIiIdYjFLBUJSUhJGjhyJ7t27IzY2Fo0bN0ZoaCg6dOggdTQiIiLSIRazpPfu378PNzc3rFq1CgAwdepUnDx5EmXKlJE4GREREeka58yS3jMwMEB4eDisra2xZcsWtGnTRupIRERElEdYzJJeUiqVMDQ0BAA4Oztj7969qFy5MhwdHSVORkRERHmJ0wxI79y+fRv16tXDkSNH1G3NmzdnIUtERFQIsZglvbJ582a4urri+vXrmDx5MlQqldSRiIiISEIsZkkvxMfHY8CAAejfvz8SEhLg7u6OwMBAGBjwW5iIiKgwYyVA+d7Nmzfx+eefw9fXFwYGBpg7dy4CAgJgZ2cndTQiIiKSGC8Ao3ztwYMHqF+/PhITE2Fvb4+tW7fiyy+/lDoWERER5RMsZilfK1++PDw9PfHs2TNs3rwZNjY2UkciIiKifITFLOU7f//9NxwcHFCqVCkAwKpVqyCXyzk/loiIiDJgdUD5hhACq1evRoMGDdCvXz/1SgUmJiYsZImIiChTrBAoX3j79i08PT0xYsQIJCcnw9DQEAkJCVLHIiIionyOxSxJLjg4GC4uLtixYweMjIywaNEi/PnnnyhSpIjU0YiIiCif45xZkowQAitWrMCkSZOQkpICJycnbN++HQ0bNpQ6GhEREekJjsySZOLj47Fs2TKkpKSgc+fOCAkJYSFLREREGuHILEmmSJEi8Pf3x7lz5zB27FjIZDKpIxEREZGeYTFLeUYIgaVLl8LMzAzDhw8HALi4uMDFxUXiZERERKSvWMxSnnj9+jW8vb1x4MABGBsbw8PDAxUqVJA6FhEREek5FrOkcxcuXICnpyceP34MExMT/PrrryhfvrzUsYiIiKgA4AVgpDMqlQo///wzvvjiCzx+/BiVKlXCpUuXMGLECM6PJSIiIq3gyCzphEqlQpcuXXDgwAEAQK9evbBmzRoULVpU4mRERERUkHBklnTCwMAAbm5uMDU1xbp16+Dn58dCloiIiLSOI7OkNUqlEtHR0bC1tQUAfPvtt+jevTsqVqwocTIiIiIqqDgyS1rx/PlztGnTBi1atEBCQgKA96OzLGSJiIhIl1jM0ic7ceIEateujWPHjiE8PBzXrl2TOhIREREVEixmKdeUSiVmzZqFli1b4vnz56hRowauXr2KJk2aSB2NiIiICgnOmaVcefbsGby8vHDq1CkAwKBBg7B8+XKYm5tLG4yIiIgKFRazlCtjxozBqVOnYGFhgTVr1sDLy0vqSERERFQIsZilXFm+fDnevn2L33//HVWqVJE6DhERERVSnDNLOfLkyRP8/vvv6seOjo44duwYC1kiIiKSFEdm6aMOHTqEfv364dWrV3B0dESXLl2kjkREREQEgCOzlA2FQoEpU6agffv2ePXqFerVq4fPPvtM6lhEREREahyZpUxFRETA09MTly5dAvD+gq9FixbBxMRE4mRERERE/8NiljI4ePAg+vbtizdv3sDKygobNmxA165dpY5FRERElAGLWcogOTkZb968Qf369bF9+3Y4OztLHYmIiIgoUyxmCQCQmpoKI6P33w5ff/01du/ejQ4dOsDY2FjiZESkT1QqFVJSUrR+XIVCASMjIyQlJUGpVGr9+KRb7D/9p4s+NDY2hoHBp1++xWKWsGvXLkyfPh2nTp2Cg4MDAHBaARFpLCUlBeHh4VCpVFo/thACdnZ2ePz4MWQymdaPT7rF/tN/uuhDAwMDODs7f/LAGYvZQiwpKQnffPMNVq5cCQBYtGgRfv31V4lTEZE+EkIgMjIShoaGKFOmjFZGW/5LpVLh3bt3KFKkiNaPTbrH/tN/2u5DlUqFZ8+eITIyEmXLlv2kApnFbCEVFhaGnj17IiQkBADw7bffYt68eRKnIiJ9lZqaioSEBDg4OMDc3Fzrx0+bvmBqaspiSA+x//SfLvqwVKlSePbsGVJTUyGXy3N9HBazhdD27dsxZMgQvHv3DtbW1ti8eTPatm0rdSwi0mNpc+g4z56Icirt54VSqWQxSzm3efNm9O/fHwDQtGlTbNu2DY6OjhKnIqKCgvMhiSintDb3VitHIb3x9ddfo0aNGvjuu+9w4sQJFrJERESk11jMFgKBgYHqq4stLCwQFBSEefPmqZfiIiIiyq2UlBRUrFgR58+flzpKgfHixQuUKlUKT58+lTqKXmAxW4DFx8djwIABaNWqFRYvXqxuNzU1lTAVEVH+4e3tDZlMBplMBiMjI5QtWxYjRoxATExMhm0vXLiAdu3aoXjx4jA1NcVnn32GxYsXZ7rm5smTJ9GuXTuULFkS5ubmqF69Or755puPFichISHo3r07bG1tYWpqisqVK2PIkCG4d++e1l6ztq1duxZOTk5o3LhxhueGDh0KQ0NDbN++PcNz3t7e6NKlS4b20NBQyGQyPHz4UN0mhMDatWvRoEEDFClSBMWKFYOrqyuWLl2KhIQEbb6cdGJiYtC3b19YWVnByspKfXfM7Dx//hze3t7qiyHbtGmDsLAw9fMPHz5Uf899+LVz504AgI2NDfr27YtZs2bp7LUVJCxmC6h//vkH9evXh6+vLwwMDKBQKKSORESUL7Vp0waRkZF4+PAh1q9fjwMHDmDkyJHpttm7dy+aNWuG0qVL4+TJk7hz5w7GjRuH+fPnw9PTE0II9bZr1qxBy5YtYWdnh927d+PWrVtYvXo13r59m25g4UMHDx5Ew4YNkZycDD8/P9y+fRtbtmyBlZUVvv/++1y/Pl3//P/tt98wePDgDO0JCQnw9/fH5MmTsWHDhk86R9++fTF+/Hh07twZJ0+eRGhoKL7//nvs378fAQEBn3Ts7PTu3RuhoaE4cuQIjhw5gtDQUPTt2zfL7YUQ6NKlCx48eID9+/cjJCQETk5OaNmyJeLj4wEAZcqUQWRkZLqvOXPmwMLCIt3F2AMGDICfn1+mv1jRB0Qh8/btWwFAvH37Nk/OF5+sEE7fHhRO3x4Ub94l6Px8KpVK+Pj4CDMzMwFA2NnZiZMnT+r8vAVZSkqK2Ldvn0hJSZE6CuUS+1D3EhMTxa1bt0RiYqIQ4v3Povhkhda+4hKTxbPn0SIuMfmj26pUqhzn7t+/v+jcuXO6tokTJ4oSJUqoH797906ULFlSdO3aNcP+f/75pwAgtm/fLoQQ4vHjx8LY2FiMHz8+0/PFxMRk2h4fHy+sra1Fly5dst1v48aNwsrKKt1ze/fuFf/953zWrFmidu3awsfHRzg7OwuZTCZWr14tHBwchFKpTLdvx44dRb9+/dK9nnr16gkTExPh7OwsZs+eLRQKRaaZhBAiODhYGBgYZPpvqq+vr2jYsKF48+aNMDMzE3///Xe682f23gshREhIiAAgwsPDhRBC+Pv7CwBi3759GbZVqVTizZs3Web7FLdu3RIAxKVLl9RtFy9eFADEnTt3Mt3n7t27AoC4efOmui01NVWUKFFCrFu3Lstz1alTRwwcODBDe7ly5YSPj88nvArtUSqVIiYmJsP30Kf48OfGf2lSr3HSZAHy7t07DB8+HH5+fgCAVq1aYcuWLbCxsZE4GREVNokKJarPPCrJuW/NbQ1z49z98/bgwQMcOXIk3TJBAQEBePXqFSZNmpRh+44dO6Jy5crYtm0bevbsiZ07dyIlJQVTpkzJ9PjFihXLtP3o0aOIjo7WeL+s/Pvvv9ixYwd2794NQ0NDODo6YuzYsTh58iRatGgB4P2f0I8ePYoDBw6oM/Tp0wfLly9H06ZNcf/+fQwdOhQAsvxz95kzZ1C5cmVYWlpmeM7Hxwd9+vSBlZUV2rZtCz8/PyxYsECj1wEAfn5+qFKlCjp37pzhOZlMBisrqyz3LVKkSLbHbtq0KQ4fPpzpcxcvXoSVlRUaNGigbmvYsCGsrKxw4cIFVKlSJcM+ycnJANJP5zM0NISxsTHOnTuX6Qh2cHAwQkND8fvvv2d4rn79+jh79iwGDhyY7eso7FjMFiD37t3Djh07YGhoiHnz5uHbb7/l4tRERB9x8OBBFClSBEqlEklJSQCAJUuWqJ9Pm69arVq1TPevWrWqepuwsDBYWlrC3t5eowxpcyqrVq2qcf7MpKSkYMuWLShVqpS6rU2bNti6dau6mN25cydKlCihfjx//nxMnTpVvXxj+fLlMW/ePEyZMiXLYvbhw4fq26B/+HouXbqEPXv2AAC8vLwwduxYzJ8/X+N/l8LCwjItHHMiNDQ02+fNzMyyfC4qKirTwSAbGxtERUVluk/VqlXh5OSEadOmYc2aNbCwsMCSJUsQFRWFyMjITPfx8fFBtWrV0KhRowzPOTo6qm9uRFljMVuA1KtXD2vWrEGlSpXQpEkTqeMQUSFmJjfErbmttXY8lUqFuNg4FLUs+tFiyExuqNGxmzdvjlWrViEhIQHr16/HvXv3MGbMmAzbif/Mi/2wPW29zP/+vyayOnZuOTk5pStkgfcF5dChQ7Fy5UqYmJjAz88Pnp6eMDR8/34FBwfj6tWrmD9/vnqftAI/ISEh0zu7JSYmZnpRsY+PD1q3bg1ra2sAQLt27TB48GAcO3YMbdq00ei15PY9BYCKFSvmar80mZ03uzxyuRy7d+/GoEGDUKJECRgaGqJly5ZZ3pgoMTERW7duzXJOtJmZmU4vcCsoOGynx2JjY9GvX790v7UNGDCAhSwRSU4mk8Hc2EirX2bGhjnaTtPCx8LCAhUrVkStWrWwfPlyJCcnY86cOernK1euDAC4fft2pvvfuXMHlSpVUm/79u3bLEfhspJ2jjt37mS7nYGBQYbCN7MLvCwsLDK0dezYESqVCn/99RceP36Ms2fPok+fPurnVSoV5syZg9DQUPXXjRs3EBYWluUqONbW1hkuUFIqldi8eTP++usvGBkZwcjICEWKFEFMTEy6C8EsLS3x9u3bDMdMWy0gbfpA5cqVs3zvP6ZIkSLZfmV390s7Ozs8f/48Q/vLly9ha2ub5X4uLi4IDQ3FmzdvEBkZiSNHjuDVq1dwdnbOsO2uXbuQkJCAfv36ZXqs169fZ/ilhDJiMaunrl27hnr16mHLli3w8vLKdGkYIiLS3KxZs/DLL7/g2bNnAN5ff1CiRIlMVyL4888/ERYWhl69egEAunXrBmNjYyxcuDDTY2e1rFOrVq1gbW390f1KlSqFuLg49ZXxwMf/lJ7GzMwMXbt2hZ+fH7Zt24bKlSvDxcVF/Xy9evVw9+5dVKxYMcNXVqPhdevWxZ07d9IV2IcOHUJcXBxCQkLURfG1a9fg6+uL/fv349WrVwDe/0n+5s2b6qkdaa5evYpSpUqhePHiAN6vKHDv3j3s378/w/mFEJkWxP99b7L7Wr9+fZb7urm54e3bt7hy5Yq67fLly3j79m2mUwI+ZGVlhVKlSiEsLAxBQUGZzvn18fFBp06dsixYb968ibp16370XIWe1i5J0xP6vpqBSqUSv/32mzA2NhYARNmyZcWFCxe0kJSywivh9R/7UPeyuypZG3RxJbUQWV9R7+LiIkaNGqV+vHPnTmFoaCiGDBki/v77bxEeHi7Wr18vihcvLrp165ZuBYXff/9dyGQyMXDgQHHq1Cnx8OFDce7cOTF06FAxceLELLPs27dPyOVy0bFjRxEYGCjCw8PF1atXxeTJk0XPnj2FEEK8evVKWFhYiLFjx4qwsDDh5+cnHBwcMl3NIDMBAQHCxMREVKlSRcybNy/dc0eOHBFGRkZi1qxZ4ubNm+LWrVti+/btYsaMGVlmjo6OFsbGxuLGjRvqts6dO6vzplEqleL169fC0dFRLF26VAghxJs3b4SdnZ3o1q2buHr1qvj333/Fli1bRPHixcXChQvV+6pUKtGzZ09hZmYmfvzxR3H16lXx8OFDceDAAeHu7i727t2bZb5P1aZNG1GrVi1x8eJFcfHiRfHZZ5+JDh06pNumSpUqYs+ePerHO3bsECdPnhT3798X+/btE05OTpmuhBEWFiZkMpk4fPhwpueOj48XZmZm4syZM9p9UbmUn1czYDGrY9osZmNiYkTXrl0FAAFAdOrUSbx69UpLSSkrLIT0H/tQ9wpaMevn5yeMjY3Fo0eP1G1nzpwRbdq0EVZWVsLY2FhUr15d/PLLLyI1NTXD/oGBgaJ169aiePHiwtTUVFStWlVMmjRJPHv2LNs8V69eFV27dhWlSpUSJiYmomLFimLo0KEiLCxMvc3evXtFxYoVhampqejQoYNYu3ZtjovZ1NRUYW9vLwCI+/fvZ3j+yJEjolGjRsLMzExYWlqK+vXri7Vr12ab2dPTU0ydOlUIIURUVJQwMjISO3bsSLdNWv+NHj1afPbZZ+r2sLAw8fXXXwtHR0dhYWEhPvvsM7FixYoM/axUKsWqVavE559/LszNzYWlpaVwcXERy5YtEwkJulv28tWrV8LLy0sULVpUFC1aVHh5eWVYXg2A2Lhxo/rxsmXLROnSpYVcLhdly5YV3333nUhOTs5w7GnTponSpUtn+T29detWUaVKFW2+nE+Sn4tZmRBannWez8XGxsLKygpv377NdCkRbUtISVUvT/P39+6wssj6ysnsPHnyBE2bNsXDhw8hl8uxaNEijB07NteT4innFAoFDh06hHbt2qVbrof0B/tQ95KSkhAeHg5nZ2ed3GVQpVIhNjYWlpaWXKUln7lx4wZatmyJf//9F0WLFs10G/af5urXr4/x48ejd+/eUkcBoJs+zO7nhib1Glcz0BMODg6oVKkSZDIZ/P398fnnn0sdiYiICJ999hkWLlyIhw8f4rPPPpM6ToHw4sULdOvWTT0Xm7LHYjYfe/36NUxNTWFubg4DAwNs3boVRkZGGi+eTUREpEtpa9OSdtjY2GR5Aw3KiGP9+dSFCxdQp04djBs3Tt1mbW3NQpaIiIjoP1jM5jMqlQoLFy7EF198gcePH+PUqVNZLuVCREREVNixmM1HXr58iQ4dOuDbb7+FUqmEp6cngoODORpLRERElAXOmc0nzp49C09PTzx79gympqZYtmwZhgwZwtUKiIiIiLLBYjYfSEhIQPfu3fH8+XNUqVIFO3bsQK1ataSORURERJTvcZpBPmBubo4NGzagb9++CAoKYiFLRERElEMcmZXIyZMnkZiYiHbt2gEA2rVrp/5/IiIiIsoZjszmMaVSidmzZ6NFixbw8vLCo0ePpI5EREQ6NHv2bNSpUyffnufLL7/E+PHjtZ7nY8qVK4elS5d+0jG8vb3RpUuXbLeR6vVR3pG8mF25cqX6NmYuLi44e/ZsttufPn0aLi4uMDU1Rfny5bF69eo8SvrpoqIi4eHhgTlz5kAIga5du8La2lrqWEREhdrjx48xaNAgODg4wNjYGE5OThg3bhxevXql8bFkMhn27duXrm3SpEk4fvy4ltLm3qlTpyCTybjcow7cuHEDzZo1g5mZGRwdHTF37lwIIbLdJyYmBn379oWVlRWsrKzQt2/fTPvG19cXtWrVgqmpKezs7DB69GiNz52cnIwZM2bAyckJJiYmqFChAjZs2JBpru3bt0Mmk2X4JeGnn36Cu7s7rKysYGNjgy5duuDu3bvq5xUKBb799lt89tlnsLCwgIODA/r164dnz55l+z5og6TTDPz9/TF+/HisXLkSjRs3xpo1a9C2bVvcunULZcuWzbB9eHg42rVrhyFDhuCPP/7A+fPnMXLkSJQqVQpff/21BK8g5xLDr6FJwwGIjn4JCwsLrF69Gn369JE6FhFR/qJUAmfPApGRgL090LQpYGios9M9ePAAbm5uqFy5MrZt2wZnZ2f8888/mDx5Mg4fPoxLly6hRIkSn3SOIkWKoEiRIlpKnD8oFArI5XKpY+QLsbGx8PDwQPPmzXH16lXcu3cP3t7esLCwwDfffJPlfr1798aTJ09w5MgRAMDQoUPRt29fHDhwQL3NkiVLsHjxYixatAgNGjRAUlISHjx4oPG5e/TogefPn8PHxwcVK1bEixcvkJqamiFTREQEJk2ahKZNm2Z47vTp0xg8eDCaNm0KlUqFGTNmoFWrVrh16xYsLCyQkJCAa9eu4fvvv0ft2rURExOD8ePHo1OnTggKCsrVe5tjQkL169cXw4cPT9dWtWpVMXXq1Ey3nzJliqhatWq6tmHDhomGDRvm+Jxv374VAMTbt281D5wL75JShKVbDwHIBABRq1YtcefOnTw5N2lHSkqK2Ldvn0hJSZE6CuUS+1D3EhMTxa1bt0RiYmLuD7J7txClSwsB/O+rdGkhdu8WSqVSxMTECKVSqb3QQog2bdqI0qVLi4SEhHTtkZGRwtzcPN2/UU5OTmLu3LmiV69ewsLCQtjb24vly5enex6A+svJyUkIIcSsWbNE7dq11dv1799fdO7cWcyfP1/Y2NgIKysrMXv2bKFQKMSkSZNE8eLFhaOjo/Dx8UmXacqUKaJSpUrCzMxMODs7i++++y7d9/SH5/mv8PDwdNkAiP79+wshhGjWrJkYM2aMmDx5sihevLiwtbUVs2bNSrc/ALFq1SrRqVMnYW5uLmbOnCmEEOLPP/8U9erVEyYmJsLZ2Vn9Ov6bqUyZMsLY2FjY2dmJ0aNHp3u/5s+fLwYMGCCKFCkiypQpI9asWZPuvNevXxfNmzcXpqamokSJEmLIkCEiLi4uw3uZ5t27d6Jv377CwsJC2NnZiV9++UU0a9ZMjBs3LtP3RRtWrlwprKysRFJSkrptwYIFwsHBQahUqkz3uXXrlgAgLl26pG67ePGiAKCuEV6/fi3MzMzEsWPHPunchw8fFlZWVuLVq1fZvo7U1FTRuHFjsX79+gzvqxAiw2fwxYsXAoA4ffp0lse8cuWKACAiIiIyfT67nxua1GuSjcympKQgODgYU6dOTdfeqlUrXLhwIdN9Ll68iFatWqVra926NXx8fLL8LTE5ORnJycnqx7GxsQDe/1apUCg+9WV8VGpqKlRJ7wAI9PceiOXLfoWZmVmenJu0I62v2Gf6i32oewqFAkIIqFQqqFQqzQ+wZw9kPXoAQuC/q2uLp0+Bbt0gduwAWrZUn0MbXr9+jaNHj+KHH36AiYlJuuPa2Nigd+/e8Pf3x4oVK9Rrfi9atAjTpk3DzJkzERAQgAkTJqBy5crw8PDA5cuXYWdnBx8fH7Rp0waGhoZQqVTqP/mmHV8IgRMnTsDR0RGnTp3C+fPnMWTIEFy4cAFffPEFLl68iB07dmD48OFo0aIFypQpA+D9CO+GDRvg4OCAGzduYNiwYShSpAgmT56sPu5/z/Nfjo6O2LlzJ7p3747bt2/D0tISZmZm6m03bdqECRMm4OLFi7h48SIGDhwINzc3eHh4qI8xa9YszJ8/H4sXL4ahoSEOHz6MPn36YOnSpWjatCnu37+P4cOHQwiBmTNnYteuXfj111+xdetWVK9eHQ8ePMC///6bLt/ixYsxd+5cTJ06Fbt378aIESPQpEkTVK1aFQkJCWjTpg0aNGiAy5cv48WLFxg6dChGjRqFjRs3ql/zf78nJk2ahJMnT2L37t2ws7PDjBkzEBwcjNq1a2f5fXP27Fm0b98+2++VadOmYdq0aZk+l9ZvcrlcfQ4PDw9MmzYNDx48gLOzc4Z9zp8/DysrK3z++efqferXrw8rKyucO3cOlSpVwtGjR6FSqfD48WNUq1YNcXFxcHNzwy+//KL+nsjJuffv3w9XV1f8/PPP+OOPP2BhYYGOHTti7ty5MDMzU2eaM2cOrK2tMWDAAJw5cybDZy3t+yutPSYmBgBQrFixLN/bmJgYyGQyWFpaZrpN2udDoVDA8IO/wGjy81qyYjY6OhpKpRK2trbp2m1tbREVFZXpPlFRUZlun5qaiujoaNjb22fYZ8GCBZgzZ06G9oCAAJibm3/CK8iZZCVQwn0wzMq7ol3Hejh58qTOz0m6ERgYKHUE+kTsQ90xMjKCnZ0d3r17h5SUFM12ViphOW5chkIWAGRCQMhkkI0fD/z9N+Li4rQVGaGhoRBCwMnJST3Q8V/Ozs6IiYnBgwcPUKpUKahUKtSvXx8jRowAAPTr1w+nTp3CL7/8ggYNGsDExAQAYGJiov73JTY2FsnJyVAqlekGU4oVK4Z58+bBwMAA3bp1w8KFCxEXF4dRo0YBAEaOHImff/4Zx44dU0+jGzNmjDpbs2bNMHLkSGzfvh3Dhg0DgAzn+ZCpqSkAwMzMLF2+1NRUVK9eXX2RVJcuXfDbb7/h8OHDaNCggXr/r7/+Gt26dVM/njdvHsaNG4evvvoKAGBtbY2pU6di9uzZGD9+PMLCwmBjY4P69etDLpfDxcUFLi4u6nwqlQotW7aEl5cXAGD48OH49ddfceTIETg4OGDTpk1ISEjAb7/9BgsLC5QtWxY//fQTevXqhRkzZsDGxgYKhQKpqamIjY3Fu3fvsGHDBqxatUqd+7fffkONGjWQkpKS5ftSuXJlnDlzJtPn0hQvXjzL/Z8+fYqyZcumez7t/b1//z5KliyZYZ+IiAhYW1tnOKa1tTUiIiIQGxuL27dvQ6VS4ccff8SCBQtgaWmJ+fPnw8PDA+fOnYOxsXGOzh0WFoZz587B0NAQmzdvxqtXrzBp0iQ8f/4cK1asAABcunQJPj4+OHPmDGJjY9O9rx+Ki4uDEALjxo1Dw4YNM5w/TVJSEr799lv190xm26SkpCAxMRFnzpzJMO0hISEh0/c7M5IvzfXhHa6EENne9Sqz7TNrTzNt2jRMnDhR/Tg2NhZlypRBq1atYGlpmdvYOSaEgLt7Mk6cMED71i1hbGys83OSdikUCgQGBsLDw4NzxPQU+1D3kpKS8PjxYxQpUkRdNOXYqVMwyOYiEZkQkD19CqOLF2HWtq3W7oxoYWEB4H1xl9m/B2nFqaWlJSwtLWFgYICmTZum2/aLL77AsmXL0rV9eDwTExMYGhqq2+RyOWrWrJnuVuX29vaoUaNGuv1KliyJd+/eqdt27dqF5cuX499//8W7d++QmpqqzpbZeT6UVuQULVo03TZGRkaoVatWujZHR0e8ffs2XZubm1u6x3///TdCQkKwZMkSdZtSqURSUhKMjIzQp08frFmzBvXq1UPr1q3x5Zdfonv37urPoIGBAVxcXNId097eHnFxcbC0tMTDhw9Rp06ddANVHh4eUKlUePbsGSpWrAi5XA4jIyNYWloiPDwcKSkpcHd3Vx/T0tISVapUgbGxcZbvi6WlZYaBMk0YGhpmOH7aL11FihTJ9LympqaZ9pVMJlN//xgbG0OhUGD58uXqv0rXqlULDg4OCA4ORuvWrXN0bplMBplMhu3bt8PKygrA+/e+R48eWLNmDVJTUzFixAisXbtWPYr83/c1jRACcXFxKFq0KMaMGYPbt2/jzJkzmb4+hUKB/v37QyaTYe3atVm+90lJSTAzM8MXX3yR4edGVr88ZEayYtba2hqGhoYZRmFfvHiR5TeVnZ1dptsbGRll+psP8P7DnfYD6b/kcnme/aNmJZPBxBAwNjbmP6R6LC+/Z0g32Ie6o1QqIZPJYGBgAAMDDRfKef48R5vJoqLU59CGypUrQyaT4c6dO5ke8+7duyhevDhsbGzUBfSH508rFP7b9uF7kLZvWptMJoOxsXGGbTJrE0LAwMAAly5dQu/evTFnzhy0bt0aVlZW2L59OxYvXpzuuP89z4fS2jProw/PbWBgoD53mqJFi6Z7rFKpMGfOHHTt2jXDuczNzVGkSBHcvXsXgYGBCAwMxKRJk7By5UqcPn1a/TnM7jWnPf4wF/C+gDQwMEj3/v/39X/4+rL7vjl79izatm2b6XNppk+fjunTp2f6nL29PZ4/f57u+NHR0ernMjtvZvsAwMuXL2FnZwcDAwM4ODgAAGrWrKneztbWFtbW1njy5AkMDAxydG4HBwc4OjqiePHi6m1q1KgBIQSePXuG+Ph4PHz4EJ07d1Y/nzYlwNjYGHfv3kWFChXUbePGjcOBAwdw5syZTC/WVygU8PT0xMOHD3HixIl0v7R9KK3fMvvZrMnPasmKWWNjY7i4uCAwMFD9Jwrg/Z8B//uG/pebm1u6q/yA99MFXF1d+Q8UEZG+ymSKWGaEnZ1WT1uyZEl4eHhg5cqVmDBhQrr5g1FRUfDz80O/fv3SjQRfunQp3TEuXbqEqlWrqh/L5XIolUqt5gTez7F0cnLCjBkz1G0REREaHSPtL4PaylevXj3cvXsXFStWzHIbMzMzdOrUCR06dEC/fv1Qv3593LhxA/Xq1fvo8atXr45NmzYhPj5ePYp+/vx5GBgYoHLlyhm2TxupvXTpkrrIiomJwb1799CsWbMsz+Pq6orQ0NBss2S3ooWbmxumT5+OlJQU9XscEBAABwcHlCtXLst93r59iytXrqB+/foAgMuXL+Pt27do1KgRAKBx48YA3v9SVbp0aQDv53lHR0fDyckpx+du3Lgxdu7ciXfv3qlX1bh37x4MDAxQunRpyGQy3LhxI12+7777DnFxcVi2bJl6fq4QApMnT8ahQ4dw6tSpTOcCKxQK9OjRA2FhYTh58mSWA43aJuk6sxMnTsT69euxYcMG3L59GxMmTMCjR48wfPhwAO+nCPTr10+9/fDhwxEREYGJEyfi9u3b2LBhA3x8fDBp0iSpXgIREX2qpk2B0qWBrKYPyGQQZcog1c1N66desWIFkpOT0bp1a5w5cwaPHz/GkSNH4OHhAUdHR8yfPz/d9ufPn8fChQtx7949/P7779i5cyfGjRunfr5cuXI4fvw4oqKi1BfIaEPFihXx6NEjbN++Hffv38fy5cuxd+9ejY7h5OQEmUyGgwcP4uXLl3j37t0nZZo5cyY2b96M2bNn459//sHt27fh7++P7777DsD79VF9fHxw8+ZNPHjwAP7+/jAzM1MXYh/j5eUFU1NT9O/fHzdv3sTJkycxZswY9O3bN9O/4BYpUgSDBg3C5MmTcfz4cdy8eRPe3t4fHck3MzNDxYoVs/3Krpjt3bs3TExM4O3tjZs3b2Lv3r348ccfMXHiRPUvQleuXEHVqlXx9OlTAEC1atXQpk0bDBkyBJcuXcKlS5cwZMgQdOjQAVWqVAHw/i8HnTt3xrhx43DhwgXcvHkT/fv3R9WqVdG8efMcn7t3794oWbIkBgwYgFu3buHMmTOYPHkyBg4cCDMzM5iamqJmzZrpvooVK4aiRYuiZs2a6iJ59OjR2LFjB/744w8ULVoUUVFRiIqKQmJiIoD3F7x369YNQUFB8PPzg1KpVG+j8Tx6DUlazPbs2RNLly7F3LlzUadOHZw5cwaHDh1Sf6NHRkamu0OWs7Oz+jeCOnXqYN68eVi+fHm+X2OWiIiyYWgILFv2/v8/LGj//7FYskQn681WqlQJQUFBqFChAnr27IkKFSpg6NChaN68OS5evJihiPnmm28QHByMunXrYt68eVi8eDFat26tfn7x4sUIDAxEmTJlULduXa3l7Ny5MyZMmIDRo0ejTp06uHDhAr7//nuNjuHo6Ig5c+Zg6tSpsLW1zbD4vqZat26NgwcPIjAwEJ9//jkaNmyIJUuWqP8NL1asGNatW4fGjRur/43fv39/jkfrzM3NcfToUbx+/Rqff/45unXrhhYtWqgvWsrMokWL8MUXX6BTp05o2bIlmjRpAhcXl096nR9jZWWFwMBAPHnyBK6urhg5ciQmTpyY7nqdhIQE3L17N90V+n5+fvjss8/QqlUrtGrVCrVq1cKWLVvSHXvz5s1o0KAB2rdvj2bNmkEul+PIkSPqv0bn5NxFihRBYGAg3rx5A1dXV3h5eaFjx45Yvny5Rq9z9erViI2Nhbu7O+zt7dVf/v7+AIAnT57gzz//xJMnT9RzndO+slqlSltkIu0KqkIiNjYWVlZWGSa265JCocChQ4fQrl07TofQQ+w//cc+1L2kpCSEh4er7+iYK3v2AOPGAU+e/K+tTBlg6VKounRBbGys+kIsKZQrVw7jx4/nrVFzQaVSSd5/9Gl00YfZ/dzQpF6TfDUDIiIiAEDXrkDnzpnfAUxLa8sSUcHDYpaIiPIPQ0Pgyy+lTkFEeoTFLBERUQ48fPhQ6ghElAlOXCEiIiIivcViloiItKaQXVNMRJ9AWz8vWMwSEdEnM/z/ZbN0vZ4kERUcaT8vDD9x2T3OmSUiok9mZGQEc3NzvHz5EnK5XOvLL6lUKqSkpCApKYlLO+kh9p/+03YfqlQqvHz5Eubm5jAy+rRylMUsERF9MplMBnt7e4SHh2t8m9WcEEIgMTERZmZm6W4vS/qB/af/dNGHBgYGKFu27Ccfj8UsERFphbGxMSpVqqSTqQYKhQJnzpzBF198wRtf6CH2n/7TRR8aGxtrZZSXxSwREWmNgYFB7u8Alg1DQ0OkpqbC1NSUxZAeYv/pv/zch5y4QkRERER6i8UsEREREektFrNEREREpLcK3ZzZtAV6Y2Nj8+ycCoUCCQkJiI2NzXfzTOjj2H/6j32o/9iH+o39p//yug/T6rSc3Fih0BWzcXFxAIAyZcpInISIiIiIshMXFwcrK6tst5GJQnbvQZVKhWfPnqFo0aJ5ttZdbGwsypQpg8ePH8PS0jJPzknaw/7Tf+xD/cc+1G/sP/2X130ohEBcXBwcHBw+unxXoRuZNTAwQOnSpSU5t6WlJT/Eeoz9p//Yh/qPfajf2H/6Ly/78GMjsml4ARgRERER6S0Ws0RERESkt1jM5gETExPMmjULJiYmUkehXGD/6T/2of5jH+o39p/+y899WOguACMiIiKigoMjs0RERESkt1jMEhEREZHeYjFLRERERHqLxSwRERER6S0Ws1qwcuVKODs7w9TUFC4uLjh79my2258+fRouLi4wNTVF+fLlsXr16jxKSlnRpA/37NkDDw8PlCpVCpaWlnBzc8PRo0fzMC1lRtPPYZrz58/DyMgIderU0W1A+ihN+zA5ORkzZsyAk5MTTExMUKFCBWzYsCGP0tKHNO0/Pz8/1K5dG+bm5rC3t8eAAQPw6tWrPEpLHzpz5gw6duwIBwcHyGQy7Nu376P75Jt6RtAn2b59u5DL5WLdunXi1q1bYty4ccLCwkJERERkuv2DBw+Eubm5GDdunLh165ZYt26dkMvlYteuXXmcnNJo2ofjxo0TP//8s7hy5Yq4d++emDZtmpDL5eLatWt5nJzSaNqHad68eSPKly8vWrVqJWrXrp03YSlTuenDTp06iQYNGojAwEARHh4uLl++LM6fP5+HqSmNpv139uxZYWBgIJYtWyYePHggzp49K2rUqCG6dOmSx8kpzaFDh8SMGTPE7t27BQCxd+/ebLfPT/UMi9lPVL9+fTF8+PB0bVWrVhVTp07NdPspU6aIqlWrpmsbNmyYaNiwoc4yUvY07cPMVK9eXcyZM0fb0SiHctuHPXv2FN99952YNWsWi1mJadqHhw8fFlZWVuLVq1d5EY8+QtP+W7RokShfvny6tuXLl4vSpUvrLCPlXE6K2fxUz3CawSdISUlBcHAwWrVqla69VatWuHDhQqb7XLx4McP2rVu3RlBQEBQKhc6yUuZy04cfUqlUiIuLQ4kSJXQRkT4it324ceNG3L9/H7NmzdJ1RPqI3PThn3/+CVdXVyxcuBCOjo6oXLkyJk2ahMTExLyITP+Rm/5r1KgRnjx5gkOHDkEIgefPn2PXrl1o3759XkQmLchP9YxRnp6tgImOjoZSqYStrW26dltbW0RFRWW6T1RUVKbbp6amIjo6Gvb29jrLSxnlpg8/tHjxYsTHx6NHjx66iEgfkZs+DAsLw9SpU3H27FkYGfHHoNRy04cPHjzAuXPnYGpqir179yI6OhojR47E69evOW82j+Wm/xo1agQ/Pz/07NkTSUlJSE1NRadOnfDbb7/lRWTSgvxUz3BkVgtkMlm6x0KIDG0f2z6zdso7mvZhmm3btmH27Nnw9/eHjY2NruJRDuS0D5VKJXr37o05c+agcuXKeRWPckCTz6FKpYJMJoOfnx/q16+Pdu3aYcmSJfD19eXorEQ06b9bt25h7NixmDlzJoKDg3HkyBGEh4dj+PDheRGVtCS/1DMckvgE1tbWMDQ0zPCb54sXLzL8tpLGzs4u0+2NjIxQsmRJnWWlzOWmD9P4+/tj0KBB2LlzJ1q2bKnLmJQNTfswLi4OQUFBCAkJwejRowG8L4yEEDAyMkJAQADc3d3zJDu9l5vPob29PRwdHWFlZaVuq1atGoQQePLkCSpVqqTTzPQ/uem/BQsWoHHjxpg8eTIAoFatWrCwsEDTpk3xww8/8K+UeiA/1TMcmf0ExsbGcHFxQWBgYLr2wMBANGrUKNN93NzcMmwfEBAAV1dXyOVynWWlzOWmD4H3I7Le3t7YunUr53hJTNM+tLS0xI0bNxAaGqr+Gj58OKpUqYLQ0FA0aNAgr6LT/8vN57Bx48Z49uwZ3r17p267d+8eDAwMULp0aZ3mpfRy038JCQkwMEhfghgaGgL43+ge5W/5qp7J80vOCpi05Uh8fHzErVu3xPjx44WFhYV4+PChEEKIqVOnir59+6q3T1vKYsKECeLWrVvCx8eHS3NJTNM+3Lp1qzAyMhK///67iIyMVH+9efNGqpdQ6Gnahx/iagbS07QP4+LiROnSpUW3bt3EP//8I06fPi0qVaokBg8eLNVLKNQ07b+NGzcKIyMjsXLlSnH//n1x7tw54erqKurXry/VSyj04uLiREhIiAgJCREAxJIlS0RISIh6ebX8XM+wmNWC33//XTg5OQljY2NRr149cfr0afVz/fv3F82aNUu3/alTp0TdunWFsbGxKFeunFi1alUeJ6YPadKHzZo1EwAyfPXv3z/vg5Oapp/D/2Ixmz9o2oe3b98WLVu2FGZmZqJ06dJi4sSJIiEhIY9TUxpN+2/58uWievXqwszMTNjb2wsvLy/x5MmTPE5NaU6ePJntv235uZ6RCcHxfCIiIiLST5wzS0RERER6i8UsEREREektFrNEREREpLdYzBIRERGR3mIxS0RERER6i8UsEREREektFrNEREREpLdYzBIRERGR3mIxS0QEwNfXF8WKFZM6Rq6VK1cOS5cuzXab2bNno06dOnmSh4gor7CYJaICw9vbGzKZLMPXv//+K3U0+Pr6pstkb2+PHj16IDw8XCvHv3r1KoYOHap+LJPJsG/fvnTbTJo0CcePH9fK+bLy4eu0tbVFx44d8c8//2h8HH3+5YKI8g6LWSIqUNq0aYPIyMh0X87OzlLHAgBYWloiMjISz549w9atWxEaGopOnTpBqVR+8rFLlSoFc3PzbLcpUqQISpYs+cnn+pj/vs6//voL8fHxaN++PVJSUnR+biIqfFjMElGBYmJiAjs7u3RfhoaGWLJkCT777DNYWFigTJkyGDlyJN69e5flcf7++280b94cRYsWhaWlJVxcXBAUFKR+/sKFC/jiiy9gZmaGMmXKYOzYsYiPj882m0wmg52dHezt7dG8eXPMmjULN2/eVI8cr1q1ChUqVICxsTGqVKmCLVu2pNt/9uzZKFu2LExMTODg4ICxY8eqn/vvNINy5coBAL766ivIZDL14/9OMzh69ChMTU3x5s2bdOcYO3YsmjVrprXX6erqigkTJiAiIgJ3795Vb5Ndf5w6dQoDBgzA27dv1SO8s2fPBgCkpKRgypQpcHR0hIWFBRo0aIBTp05lm4eICjYWs0RUKBgYGGD58uW4efMmNm3ahBMnTmDKlClZbu/l5YXSpUvj6tWrCA4OxtSpUyGXywEAN27cQOvWrdG1a1dcv34d/v7+OHfuHEaPHq1RJjMzMwCAQqHA3r17MW7cOHzzzTe4efMmhg0bhgEDBuDkyZMAgF27duHXX3/FmjVrEBYWhn379uGzzz7L9LhXr14FAGzcuBGRkZHqx//VsmVLFCtWDLt371a3KZVK7NixA15eXlp7nW/evMHWrVsBQP3+Adn3R6NGjbB06VL1CG9kZCQmTZoEABgwYADOnz+P7du34/r16+jevTvatGmDsLCwHGciogJGEBEVEP379xeGhobCwsJC/dWtW7dMt92xY4coWbKk+vHGjRuFlZWV+nHRokWFr69vpvv27dtXDB06NF3b2bNnhYGBgUhMTMx0nw+P//jxY9GwYUNRunRpkZycLBo1aiSGDBmSbp/u3buLdu3aCSGEWLx4sahcufL/tXN3IU22cRjAr81ttJ4lpAeVZBtOHvTEcNAX0UFlGJMWg6RyYAcJWVpgENFJC8IgpFUE6YkoykA72EDQiLQyrCBzRJ+EkuwgEYmkKEtb/jt48aHHzdc0eXs3rt/Z/bF7980N42J7/pOpqamE69vtdrl8+bLWBiDhcFg3x+/3y/r167X2iRMnZMeOHVr71q1bYrFY5MOHD390TgCiKIosX75cAAgA8Xg8CefPmO8+RESGhobEYDDIu3fvdP07d+6UM2fO/Ov6RJS6TH83ShMRLa3t27ejvr5eayuKAgC4e/cuLly4gFevXuHTp0+IxWL49u0bvnz5os351cmTJ1FRUYHW1lYUFRWhtLQUTqcTADAwMIChoSEEg0Ftvohgenoaw8PDyM/PT7i3jx8/wmazQUQwMTEBl8uFUCgEi8WC169f6wq4AGDr1q24evUqAKC0tBRXrlxBTk4Odu/eDbfbjT179sBkWvzHuM/nw5YtWzAyMoKsrCwEg0G43W6sXLnyj865YsUKRCIRxGIx9Pb2oq6uDg0NDbo5C70PAIhEIhARqKqq65+cnPxPngUmov8nhlkiSimKoiA3N1fXF41G4Xa7UVlZifPnzyMjIwN9fX04fPgwvn//nnCdc+fOoaysDJ2dnbh58yb8fj/a2trg9XoxPT2NI0eO6J5ZnbFu3bo59zYT8oxGI1atWhUX2gwGg64tIlpfdnY23rx5g9u3b6O7uxvHjh1DXV0dent7dT/fL8TGjRvhdDrR1taGo0ePIhwOo6mpSRtf7DmNRqN2B3l5eRgdHcX+/ftx//59AIu7j5n9pKWlYWBgAGlpaboxm822oLMTUepgmCWilPfkyRPEYjFcunQJRuM/pQI3btyY93WqqkJVVdTU1ODgwYNoamqC1+uFy+XCy5cv40LzfH4NebPl5+ejr68P5eXlWt/Dhw91335arVZ4PB54PB5UVVUhLy8Pz58/h8vlilvPbDb/1r8klJWVIRgMYu3atTAajSgpKdHGFnvO2WpqahAIBBAOh+H1en/rPiwWS9z+CwsL8ePHD4yNjWHbtm1/tCciSh0sACOilOd0OhGLxXDt2jW8ffsWra2tcT97/+rr16+orq7GvXv3EI1G8eDBA/T392vB8vTp03j06BGqqqrw9OlTDA4OoqOjA8ePH1/0Hk+dOoXm5mY0NDRgcHAQgUAAoVBIK3xqbm5GY2MjXrx4oZ3BarXCbrcnXM/hcKCnpwejo6MYHx+f8319Ph8ikQhqa2uxb98+LFu2TBtbqnOmp6ejoqICfr8fIvJb9+FwOPD582f09PTg/fv3mJiYgKqq8Pl8KC8vRygUwvDwMPr7+3Hx4kV0dXUtaE9ElEL+5gO7RERL6dChQ7J3796EY4FAQNasWSNWq1WKi4ulpaVFAMj4+LiI6AuOJicn5cCBA5KdnS0Wi0WysrKkurpaV/T0+PFj2bVrl9hsNlEURQoKCqS2tnbOvSUqaJrt+vXrkpOTI2azWVRVlZaWFm0sHA7Lpk2bJD09XRRFkc2bN0t3d7c2PrsArKOjQ3Jzc8VkMondbheR+AKwGRs2bBAAcufOnbixpTpnNBoVk8kk7e3tIjL/fYiIVFZWSmZmpgAQv98vIiJTU1Ny9uxZcTgcYjabZfXq1eL1euXZs2dz7omIUptBROTvxmkiIiIiosXhYwZERERElLQYZomIiIgoaTHMEhEREVHSYpglIiIioqTFMEtERERESYthloiIiIiSFsMsERERESUthlkiIiIiSloMs0RERESUtBhmiYiIiChpMcwSERERUdL6CVJDO6JLOLSUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     - AUC: 96.88%\n",
      "    - Optimal Threshold: 0.0606422\n",
      "    - F1 Score: 0.90\n",
      "    - CONFUSION MATRIX:\n",
      " [[20  0]\n",
      " [15 65]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 22.29it/s]\n",
      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.24it/s]\n",
      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.25it/s]\n",
      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- OK - Evaluating model (13.67 s)\n",
      "\n",
      "Dataset               F1 Score\n",
      "------------------------------\n",
      "Anonaly lvl 1 test        1.00\n",
      "Anonaly lvl 2 test        0.97\n",
      "Anonaly lvl 3 test        0.46\n",
      "\n",
      "Anomaly all test          0.90\n",
      "\n",
      "No Anomaly Test           1.00\n",
      "\n",
      "All test                  0.90\n"
     ]
    }
   ],
   "source": [
    "# STEPS = 5000, MODEL TYPE = SMALL, WEIGHT = on Cookies 2\n",
    "model21 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\", \"subdataset\": \"cookies_2\"})\n",
    "model21.create_model()\n",
    "model21.display_eval_result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create model for cookies 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Setting seed to 42\n",
      "- OK - Setting seed to 42 (0.60 ms)\n",
      "\n",
      "- Setting datasets path\n",
      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
      "- OK - Setting datasets path (5.18 ms)\n",
      "\n",
      "- Setting config\n",
      "     Output folder path: ../output/cookies_1_steps_10000_small_weighted\n",
      "- OK - Setting config (0.12 ms)\n",
      "\n",
      "- Prepare teacher, student & autoencoder\n",
      "     Loading weight ../weights/teacher_small.pth\n",
      "     Training\n",
      "- OK - Prepare teacher, student & autoencoder (74.60 ms)\n",
      "\n",
      "- Normalizing teacher\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.11it/s]\n",
      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Normalizing teacher (2.82 s)\n",
      "\n",
      "- Train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 29.0107  :   0%|                                                                                 | 1/10000 [00:01<4:27:46,  1.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 16.2858  :   1%|▊                                                                              | 101/10000 [00:15<1:23:02,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.7878787878787878\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 11.0156  :   2%|█▌                                                                             | 201/10000 [00:29<1:22:09,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 10.6710  :   3%|██▍                                                                            | 301/10000 [00:43<1:21:19,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 10.1724  :   4%|███▏                                                                           | 401/10000 [00:57<1:20:11,  2.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 9.2264  :   5%|████                                                                            | 501/10000 [01:11<1:20:38,  1.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 8.6771  :   6%|████▊                                                                           | 601/10000 [01:25<1:18:50,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 9.7556  :   7%|█████▌                                                                          | 701/10000 [01:38<1:18:12,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 8.3746  :   8%|██████▍                                                                         | 801/10000 [01:53<1:17:51,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 9.5946  :   9%|███████▏                                                                        | 901/10000 [02:06<1:15:53,  2.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 8.4501  :  10%|███████▉                                                                       | 1001/10000 [02:20<1:15:38,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 8.2370  :  11%|████████▋                                                                      | 1101/10000 [02:34<1:14:31,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 9.9462  :  12%|█████████▍                                                                     | 1201/10000 [02:48<1:13:50,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 8.4517  :  13%|██████████▎                                                                    | 1301/10000 [03:02<1:15:27,  1.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 9.5347  :  14%|███████████                                                                    | 1401/10000 [03:16<1:13:10,  1.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 9.7705  :  15%|███████████▊                                                                   | 1501/10000 [03:30<1:10:52,  2.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.7871  :  16%|████████████▋                                                                  | 1601/10000 [03:44<1:11:44,  1.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.9733  :  17%|█████████████▍                                                                 | 1701/10000 [03:58<1:09:43,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.5294  :  18%|██████████████▏                                                                | 1801/10000 [04:12<1:18:35,  1.74it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.6900  :  19%|███████████████                                                                | 1901/10000 [04:26<1:07:50,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8571428571428571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 9.6443  :  20%|███████████████▊                                                               | 2001/10000 [04:40<1:06:58,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 8.9753  :  21%|████████████████▌                                                              | 2101/10000 [04:54<1:06:31,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.7862  :  22%|█████████████████▍                                                             | 2201/10000 [05:07<1:05:50,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.0736  :  23%|██████████████████▏                                                            | 2301/10000 [05:21<1:04:17,  2.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9230769230769231\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.8569  :  24%|██████████████████▉                                                            | 2401/10000 [05:35<1:04:27,  1.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.9773  :  25%|███████████████████▊                                                           | 2501/10000 [05:49<1:04:05,  1.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9230769230769231\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.5698  :  26%|████████████████████▌                                                          | 2601/10000 [06:03<1:02:38,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.9230769230769231\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.0992  :  27%|█████████████████████▎                                                         | 2701/10000 [06:17<1:00:54,  2.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.8597  :  28%|██████████████████████▏                                                        | 2801/10000 [06:31<1:00:27,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.6128  :  29%|███████████████████████▍                                                         | 2901/10000 [06:45<59:38,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.6677  :  30%|████████████████████████▎                                                        | 3001/10000 [06:59<58:52,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.8616  :  31%|████████████████████████▍                                                      | 3101/10000 [07:13<1:00:03,  1.91it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 7.1846  :  32%|█████████████████████████▉                                                       | 3201/10000 [07:27<57:01,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.9880  :  33%|██████████████████████████▋                                                      | 3301/10000 [07:41<56:16,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.6562  :  34%|███████████████████████████▌                                                     | 3401/10000 [07:55<55:38,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.5604  :  35%|████████████████████████████▎                                                    | 3501/10000 [08:08<54:08,  2.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.4236  :  36%|████████████████████████████▍                                                  | 3601/10000 [08:23<1:01:09,  1.74it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.95\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.4188  :  37%|█████████████████████████████▉                                                   | 3701/10000 [08:36<52:52,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.6183  :  38%|██████████████████████████████▊                                                  | 3801/10000 [08:50<52:06,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.4557  :  39%|███████████████████████████████▌                                                 | 3901/10000 [09:04<51:20,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.8281  :  40%|████████████████████████████████▍                                                | 4001/10000 [09:18<50:06,  2.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.3562  :  41%|█████████████████████████████████▏                                               | 4101/10000 [09:32<49:33,  1.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 4.9683  :  42%|██████████████████████████████████                                               | 4201/10000 [09:46<49:08,  1.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.6889  :  43%|██████████████████████████████████▊                                              | 4301/10000 [10:00<47:50,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.5786  :  44%|███████████████████████████████████▋                                             | 4401/10000 [10:14<47:31,  1.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 6.2688  :  45%|████████████████████████████████████▍                                            | 4501/10000 [10:28<45:47,  2.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.7612  :  46%|█████████████████████████████████████▎                                           | 4601/10000 [10:41<45:10,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.4185  :  47%|██████████████████████████████████████                                           | 4701/10000 [10:55<44:18,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 4.9782  :  48%|██████████████████████████████████████▉                                          | 4801/10000 [11:09<43:35,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.3264  :  49%|███████████████████████████████████████▋                                         | 4901/10000 [11:23<44:11,  1.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 4.9857  :  50%|████████████████████████████████████████▌                                        | 5001/10000 [11:37<41:55,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.918918918918919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       Current loss: 5.9820  :  51%|█████████████████████████████████████████▎                                       | 5100/10000 [11:51<11:23,  7.17it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Validation 0.8888888888888888\n",
      "Early stopping at iteration 5101 because validation F1 did not improve.\n",
      "- OK - Train (711.43 s)\n",
      "\n",
      "- Saving models to ../output/cookies_1_steps_10000_small_weighted/all_models.pth\n",
      "- OK - Saving models (65.02 ms)\n",
      "\n",
      "- Saving map normalization to ../output/cookies_1_steps_10000_small_weighted/map_normalization.pth\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- OK - Saving map normalization (1013.09 ms)\n",
      "\n",
      "- Evaluating model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.14it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     - AUC: 96.81%\n",
      "    - Optimal Threshold: 0.0233271\n",
      "    - F1 Score: 0.90\n",
      "    - CONFUSION MATRIX:\n",
      " [[20  0]\n",
      " [14 66]] \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 22.32it/s]\n",
      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.35it/s]\n",
      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.50it/s]\n",
      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- OK - Evaluating model (13.67 s)\n",
      "\n",
      "Dataset               F1 Score\n",
      "------------------------------\n",
      "Anonaly lvl 1 test        1.00\n",
      "Anonaly lvl 2 test        1.00\n",
      "Anonaly lvl 3 test        0.46\n",
      "\n",
      "Anomaly all test          0.90\n",
      "\n",
      "No Anomaly Test           1.00\n",
      "\n",
      "All test                  0.90\n"
     ]
    }
   ],
   "source": [
    "# STEPS = 10000, MODEL TYPE = SMALL, WEIGHT = on Cookies 1\n",
    "model22 = EfficientAD({**config, \"train_steps\": 10000, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\", \"subdataset\": \"cookies_1\"})\n",
    "model22.create_model()\n",
    "model22.display_eval_result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}