{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9988ed04",
   "metadata": {},
   "source": [
    "# Prune Dataset to extract representative subset\n",
    "\n",
    "In this notebook example, we will demonstrate the process of creating a pruned dataset, which is a representative subset of the original dataset. By working with a smaller, yet representative, subset of the training data, we can observe how the model's performance is affected in terms of accuracy and convergence speed. Through this, we can check accuracy drop and convergence time with subset of training data through [OpenVINO™ Training Extensions](https://github.com/openvinotoolkit/training_extensions). This analysis provides valuable insights into the relationship between dataset size and model performance. It helps us understand the scalability and generalization capabilities of the model, shedding light on the efficiency of training algorithms and the potential benefits of working with a pruned dataset. It also allows us to assess the impact of dataset size on model performance, providing guidance for resource allocation and model development in practical scenarios.\n",
    "\n",
    "## Prerequisite \n",
    "### Download Caltech-101 dataset\n",
    "This is [a download link for caltech101 dataset in Kaggle](https://www.kaggle.com/datasets/imbikramsaha/caltech-101?resource=download). Please download using this link and extract to your workspace directory. Then, you will have a `caltech-101` directory with images in imagenet format as follows.\n",
    "```bash\n",
    "caltech-101\n",
    "├── accordion\n",
    "│   ├── image_0001.jpg\n",
    "│   ├── image_0002.jpg\n",
    "│   ├── ...\n",
    "├── airplanes\n",
    "│   ├── image_0001.jpg\n",
    "│   ├── image_0002.jpg\n",
    "│   ├── ...\n",
    "│ ...\n",
    "└── yin_yang\n",
    "    ├── image_0001.jpg\n",
    "    ├── image_0002.jpg\n",
    "    ├── ...\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "db31c638",
   "metadata": {},
   "source": [
    "### Install OpenVINO™ Training Extensions\n",
    "For more details, please see this [OpenVINO™ Training Extensions installation guide](https://openvinotoolkit.github.io/training_extensions/latest/guide/get_started/quick_start_guide/installation.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c209d88",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install otx"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b8269823",
   "metadata": {},
   "source": [
    "## Prune dataset using Datumaro Python API\n",
    "\n",
    "In this section, we utilize the Dataumaro Python API to prune entire dataset. We import the caltech-101 dataset and apply dataset prune."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff87df2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-08 01:40:37.166610: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-07-08 01:40:37.725001: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dataset\n",
       "\tsize=9144\n",
       "\tsource_path=caltech-101\n",
       "\tmedia_type=<class 'datumaro.components.media.Image'>\n",
       "\tannotated_items_count=9144\n",
       "\tannotations_count=9144\n",
       "subsets\n",
       "\tdefault: # of items=9144, # of annotated items=9144, # of annotations=9144, annotation types=['label']\n",
       "infos\n",
       "\tcategories\n",
       "\tlabel: ['BACKGROUND_Google', 'Faces', 'Faces_easy', 'Leopards', 'Motorbikes', 'accordion', 'airplanes', 'anchor', 'ant', 'barrel', 'bass', 'beaver', 'binocular', 'bonsai', 'brain', 'brontosaurus', 'buddha', 'butterfly', 'camera', 'cannon', 'car_side', 'ceiling_fan', 'cellphone', 'chair', 'chandelier', 'cougar_body', 'cougar_face', 'crab', 'crayfish', 'crocodile', 'crocodile_head', 'cup', 'dalmatian', 'dollar_bill', 'dolphin', 'dragonfly', 'electric_guitar', 'elephant', 'emu', 'euphonium', 'ewer', 'ferry', 'flamingo', 'flamingo_head', 'garfield', 'gerenuk', 'gramophone', 'grand_piano', 'hawksbill', 'headphone', 'hedgehog', 'helicopter', 'ibis', 'inline_skate', 'joshua_tree', 'kangaroo', 'ketch', 'lamp', 'laptop', 'llama', 'lobster', 'lotus', 'mandolin', 'mayfly', 'menorah', 'metronome', 'minaret', 'nautilus', 'octopus', 'okapi', 'pagoda', 'panda', 'pigeon', 'pizza', 'platypus', 'pyramid', 'revolver', 'rhino', 'rooster', 'saxophone', 'schooner', 'scissors', 'scorpion', 'sea_horse', 'snoopy', 'soccer_ball', 'stapler', 'starfish', 'stegosaurus', 'stop_sign', 'strawberry', 'sunflower', 'tick', 'trilobite', 'umbrella', 'watch', 'water_lilly', 'wheelchair', 'wild_cat', 'windsor_chair', 'wrench', 'yin_yang']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datumaro as dm\n",
    "from datumaro.components.algorithms.hash_key_inference.prune import Prune\n",
    "\n",
    "dataset = dm.Dataset.import_from(\"caltech-101\", format=\"imagenet\")\n",
    "dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fc57a21d",
   "metadata": {},
   "source": [
    "### Generate the validation report\n",
    "\n",
    "Before pruning entire dataset, we first generate the validation report of dataset to confirm the statistics of dataset labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "680a5c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datumaro.plugins.validators import ClassificationValidator\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "validator = ClassificationValidator()\n",
    "reports = validator.validate(dataset)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a3b3205d",
   "metadata": {},
   "source": [
    "The label distribution in the entire dataset is as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc2ebecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats = reports[\"statistics\"]\n",
    "\n",
    "label_stats = stats[\"label_distribution\"][\"defined_labels\"]\n",
    "label_name, label_counts = zip(*[(k, v) for k, v in label_stats.items()])\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.hist(label_name, weights=label_counts, bins=len(label_name))\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "75c55551",
   "metadata": {},
   "source": [
    "### Prune dataset\n",
    "We support various prune methods in [Datumaro](https://github.com/openvinotoolkit/datumaro). We will try each method and compare the results by examining the train and validation reports. We will start by checking using the `random` method. For all methods, we will use a ratio of `0.5`."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0a4bef84",
   "metadata": {},
   "source": [
    "The random method involves randomly selecting dataset items from the entire dataset to create a subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f768c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "prune = Prune(dataset, cluster_method=\"random\")\n",
    "random_result = prune.get_pruned(0.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "612bbd8b",
   "metadata": {},
   "source": [
    "When creating a subset using the random method, as shown below, we can observe that the label distribution changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4dac0a1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_reports = validator.validate(random_result)\n",
    "\n",
    "random_stats = random_reports[\"statistics\"]\n",
    "\n",
    "label_stats = random_stats[\"label_distribution\"][\"defined_labels\"]\n",
    "label_name, label_counts = zip(*[(k, v) for k, v in label_stats.items()])\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.hist(label_name, weights=label_counts, bins=len(label_name))\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b41de300",
   "metadata": {},
   "source": [
    "In this case, we use `cluster_random` method. For detail information about each method, please refer [prune](https://openvinotoolkit.github.io/datumaro/latest/docs/command-reference/context_free/prune)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d7c6f2d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "prune = Prune(dataset, cluster_method=\"cluster_random\")\n",
    "cluster_random_result = prune.get_pruned(0.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d8a40abb",
   "metadata": {},
   "source": [
    "When creating a subset using the cluster random method, as shown below, we can observe that the label distribution changes. In the case of this dataset, the distribution of each class did not change significantly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "49872bb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_random_reports = validator.validate(cluster_random_result)\n",
    "\n",
    "cluster_random_stats = cluster_random_reports[\"statistics\"]\n",
    "\n",
    "label_stats = cluster_random_stats[\"label_distribution\"][\"defined_labels\"]\n",
    "label_name, label_counts = zip(*[(k, v) for k, v in label_stats.items()])\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.hist(label_name, weights=label_counts, bins=len(label_name))\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9294ef04",
   "metadata": {},
   "source": [
    "We use `query_clust` method. For detail information about each method, please refer [prune](https://openvinotoolkit.github.io/datumaro/latest/docs/command-reference/context_free/prune)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b87ee075",
   "metadata": {},
   "outputs": [],
   "source": [
    "prune = Prune(dataset, cluster_method=\"query_clust\")\n",
    "query_clust_result = prune.get_pruned(0.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bd547e06",
   "metadata": {},
   "source": [
    "When creating a subset using the query clust method, as shown below, we can observe that the label distribution changes. In the caltech-101 dataset, when the datasets included in each class were small, there is a tendency for the ratio of classes with more data to increase compared to the datasets of those classes. This tendency may vary for different datasets, so we recommend comparing different methods directly on your own data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "444e92de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query_clust_reports = validator.validate(query_clust_result)\n",
    "\n",
    "query_clust_stats = query_clust_reports[\"statistics\"]\n",
    "\n",
    "label_stats = query_clust_stats[\"label_distribution\"][\"defined_labels\"]\n",
    "label_name, label_counts = zip(*[(k, v) for k, v in label_stats.items()])\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.hist(label_name, weights=label_counts, bins=len(label_name))\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d4a840ce",
   "metadata": {},
   "source": [
    "We use `centroid` method. For detail information about each method, please refer [prune](https://openvinotoolkit.github.io/datumaro/latest/docs/command-reference/context_free/prune)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aca13c0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "prune = Prune(dataset, cluster_method=\"centroid\")\n",
    "centroid_result = prune.get_pruned(0.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d42bbd0b",
   "metadata": {},
   "source": [
    "When creating a subset using the centroid method, as shown below, we can observe that the label distribution changes. In this case, we can see that the proportion of motorbike, which had a large amount of dataset, has decreased. This illustrates the tendency of certain classes to have a reduced proportion of their data within the overall dataset. It is possible to identify such trends where the contribution of data from specific classes decreases within the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0ba59505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "centroid_reports = validator.validate(centroid_result)\n",
    "\n",
    "centroid_stats = centroid_reports[\"statistics\"]\n",
    "\n",
    "label_stats = centroid_stats[\"label_distribution\"][\"defined_labels\"]\n",
    "label_name, label_counts = zip(*[(k, v) for k, v in label_stats.items()])\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.hist(label_name, weights=label_counts, bins=len(label_name))\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "483914c2",
   "metadata": {},
   "source": [
    "We use `entropy` method. For detail information about each method, please refer [prune](https://openvinotoolkit.github.io/datumaro/latest/docs/command-reference/context_free/prune)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc889f6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "prune = Prune(dataset, cluster_method=\"entropy\")\n",
    "entropy_result = prune.get_pruned(0.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5323b439",
   "metadata": {},
   "source": [
    "When creating a subset using the entropy method, as shown below, we can observe that the label distribution changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d30f8181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "entropy_reports = validator.validate(entropy_result)\n",
    "\n",
    "entropy_stats = entropy_reports[\"statistics\"]\n",
    "\n",
    "label_stats = entropy_stats[\"label_distribution\"][\"defined_labels\"]\n",
    "label_name, label_counts = zip(*[(k, v) for k, v in label_stats.items()])\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.hist(label_name, weights=label_counts, bins=len(label_name))\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8e4b2bbc",
   "metadata": {},
   "source": [
    "Finally, we use `ndr` method. For detail information about each method, please refer [prune](https://openvinotoolkit.github.io/datumaro/latest/docs/command-reference/context_free/prune)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "38f5855d",
   "metadata": {},
   "outputs": [],
   "source": [
    "prune = Prune(dataset, cluster_method=\"ndr\")\n",
    "ndr_result = prune.get_pruned(0.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "734f42eb",
   "metadata": {},
   "source": [
    "When creating a subset using the ndr method, as shown below, we can observe that the label distribution changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "01f61373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DByIlJeWffxsiIiIiIiKiD+y9Au3o6Gh0794dhw8fxo4dO5CcnIw6derg1atXyjZ9+/bFxo0bsXLlSkRHR+PevXto0qSJ8npqairq16+PpKQkHDp0CAsXLkRUVBRGjBhhum9FRERERERE9IG8V9fxrVu3avwdFRUFDw8PHD9+HFWqVMHz588xb948LF26FDVq1AAALFiwAP7+/jh8+DDKly+P7du348KFC9i5cyc8PT1RokQJjB49GoMHD8aoUaNgZWVlum9HRERERERE9B/7R5OhPX/+HACQLVs2AMDx48eRnJyMWrVqKdsULlwYPj4+iImJAQDExMQgMDAQnp6eyjbBwcF48eIFzp8/r3M/7969w4sXLzQeRERERERERB+jvx1op6WloU+fPqhYsSKKFi0KAEhISICVlRVcXFw0tvX09ERCQoKyTcYgW/26+jVdxo8fD2dnZ+WRO3fuv5tsIiIiIiIion/V3w60u3fvjnPnzmH58uWmTI9OQ4cOxfPnz5XH7du3//V9EhEREREREf0df2t5rx49emDTpk3Yt28fcuXKpTyfI0cOJCUl4dmzZxp3tR88eIAcOXIo2xw9elTj89Szkqu3ycza2hrW1tZ/J6lERERERERE/6n3uqMtIujRowfWrl2L3bt3I2/evBqvly5dGpaWlti1a5fy3OXLlxEfH4+goCAAQFBQEM6ePYuHDx8q2+zYsQNOTk4ICAj4J9+FiIiIiIiI6IN7rzva3bt3x9KlS7F+/Xo4OjoqY6qdnZ1ha2sLZ2dndOzYEf369UO2bNng5OSEnj17IigoCOXLlwcA1KlTBwEBAWjTpg0mTZqEhIQEDBs2DN27d+ddayIiIiIiIvrkvVegPWvWLABAtWrVNJ5fsGAB2rVrBwCIiIiAmZkZmjZtinfv3iE4OBgzZ85UtjU3N8emTZvQrVs3BAUFwd7eHqGhoQgPD/9n34SIiIiIiIjoI/BegbaIGN3GxsYGM2bMwIwZM/RukydPHmzZsuV9dk1ERERERET0SfhH62gTERERERERkSYG2kREREREREQmxECbiIiIiIiIyIQYaBMRERERERGZEANtIiIiIiIiIhNioE1ERERERERkQgy0iYiIiIiIiEyIgTYRERERERGRCTHQJiIiIiIiIjIhBtpEREREREREJsRAm4iIiIiIiMiEGGgTERERERERmRADbSIiIiIiIiITYqBNREREREREZEIMtImIiIiIiIhMiIE2ERERERERkQkx0CYiIiIiIiIyIQbaRERERERERCbEQJuIiIiIiIjIhBhoExEREREREZkQA20iIiIiIiIiE2KgTURERERERGRCDLSJiIiIiIiITIiBNhEREREREZEJMdAmIiIiIiIiMiEG2kREREREREQmxECbiIiIiIiIyIQYaBMRERERERGZEANtIiIiIiIiIhNioE1ERERERERkQgy0iYiIiIiIiEyIgTYRERERERGRCTHQJiIiIiIiIjIhBtpEREREREREJsRAm4iIiIiIiMiEGGgTERERERERmRADbSIiIiIiIiITYqBNREREREREZEIMtImIiIiIiIhMiIE2ERERERERkQkx0CYiIiIiIiIyIQbaRERERERERCbEQJuIiIiIiIjIhBhoExEREREREZkQA20iIiIiIiIiE2KgTURERERERGRCDLSJiIiIiIiITIiBNhEREREREZEJMdAmIiIiIiIiMiGLD50AIiIiIiIi0s13yGaDr9+cUP8/Sgm9D97RJiIiIiIiIjIhBtpEREREREREJsRAm4iIiIiIiMiEGGgTERERERERmRADbSIiIiIiIiITYqBNREREREREZEIMtImIiIiIiIhM6L0D7X379qFBgwbw8vKCSqXCunXrNF4XEYwYMQI5c+aEra0tatWqhbi4OI1tnj59ilatWsHJyQkuLi7o2LEjEhMT/9EXISIiIiIiIvoYvHeg/erVKxQvXhwzZszQ+fqkSZMQGRmJ2bNn48iRI7C3t0dwcDDevn2rbNOqVSucP38eO3bswKZNm7Bv3z506dLl738LIiIiIiIioo+Exfu+oV69eqhXr57O10QE06ZNw7Bhw9CwYUMAwKJFi+Dp6Yl169ahefPmuHjxIrZu3Ypjx46hTJkyAICff/4ZX375JX766Sd4eXn9g69DRERERERE9GGZdIz2jRs3kJCQgFq1ainPOTs7o1y5coiJiQEAxMTEwMXFRQmyAaBWrVowMzPDkSNHdH7uu3fv8OLFC40HERERERER0cfIpIF2QkICAMDT01PjeU9PT+W1hIQEeHh4aLxuYWGBbNmyKdtkNn78eDg7OyuP3LlzmzLZRERERERERCbz3l3HP4ShQ4eiX79+yt8vXrxgsJ2B75DNHzoJRERERERE9P+Z9I52jhw5AAAPHjzQeP7BgwfKazly5MDDhw81Xk9JScHTp0+VbTKztraGk5OTxoOIiIiIiIjoY2TSQDtv3rzIkSMHdu3apTz34sULHDlyBEFBQQCAoKAgPHv2DMePH1e22b17N9LS0lCuXDlTJoeIiIiIiIjoP/feXccTExNx9epV5e8bN27g1KlTyJYtG3x8fNCnTx+MGTMGBQoUQN68eTF8+HB4eXmhUaNGAAB/f3/UrVsXnTt3xuzZs5GcnIwePXqgefPmnHGciIiIiIiIPnnvHWjHxsaievXqyt/qsdOhoaGIiorCoEGD8OrVK3Tp0gXPnj1DpUqVsHXrVtjY2CjvWbJkCXr06IGaNWvCzMwMTZs2RWRkpAm+DhEREREREdGH9d6BdrVq1SAiel9XqVQIDw9HeHi43m2yZcuGpUuXvu+uiYiIiIiIiD56Jh2jTURERERERPS/joE2ERERERERkQkx0CYiIiIiIiIyIQbaRERERERERCbEQJuIiIiIiIjIhBhoExEREREREZkQA20iIiIiIiIiE2KgTURERERERGRCDLSJiIiIiIiITIiBNhEREREREZEJMdAmIiIiIiIiMiEG2kREREREREQmxECbiIiIiIiIyIQYaBMRERERERGZEANtIiIiIiIiIhNioE1ERERERERkQhYfOgFEHxPfIZsNvn5zQv3/KCVERERERPSp4h1tIiIiIiIiIhNioE1ERERERERkQuw6TvQ/iF3kiehTwLyKiIg+VbyjTURERERERGRCDLSJiIiIiIiITIhdx4nogzDWJRRgt1AiIiIi+jTxjjYRERERERGRCTHQJiIiIiIiIjIhBtpEREREREREJsRAm4iIiIiIiMiEOBkaERHRR4QTBRIREX36GGgTvQdWgImI6L9mrOxhuUNE9PFh13EiIiIiIiIiE+IdbSIiov9QVnrGEBHR54G9If93MdAmIiIiDZ9LxZBdrsnUPpdrg7KO+Qj9Xew6TkRERERERGRCvKNNRERssf8fw+7r6Xh3kujjxeuTPnUMtIk+MixYso7B4eeFv6fpMJAmIiL6sBhoE9Fni4Eb0cfrc2kMYD5DnyKet0T/PgbaREREREREH8jn0vBImhhoE/3HmJl+PD6Wbvq8s0BERET0eWGgTUT0L/ovGlb+VwL1/5XvSURERJ8+BtpE9Lcw6KGPzcfSQ4HoY8Nrg4jov8dAm4iIiIiIiPTiDZb3x0CbiP4VHIv+8fgYuq8DLIT/13ws5x397zBFPmSKc+rfDkiY335cmA9l3f9asM5Amz4ZLFjoQ/hfKxSISBMr0USfLpbh9CEx0CYA/7wiwSCY6PP2X9zlIaLPG4OeTwfrdUT/HANtIiIiE2KDAn2KGATTp4j5LX3MGGgTfYL+VypEn0IB+imkkf7P/8q1Q0QfL+ZD9F9jD4UPg4E2kYl9DoHX5/AdPif8PYjoY8d8ijJjg8L/FuYB2hho02eFmToR/S9ghYb+F/2vnPf/K9+T6HPHQJv+Myw4iOhjx8Y6MrXPpezjcm2fFv5e9L74e5oeA22izxAzSyIiIm0sH+lzxPP648RAm4iIiIiI/lMfQ3D4MaSBPl8MtOl/CjNUIvonmIcQERFRVjDQJiIiIvqEsQGIiOjjY/ahE0BERERERET0OeEdbSIiInpvvItKRESkH+9oExEREREREZkQA20iIiIiIiIiE2KgTURERERERGRCHzTQnjFjBnx9fWFjY4Ny5crh6NGjHzI5RERERERERP/YBwu0V6xYgX79+mHkyJE4ceIEihcvjuDgYDx8+PBDJYmIiIiIiIjoH/tgs45PnToVnTt3Rvv27QEAs2fPxubNmzF//nwMGTJEY9t3797h3bt3yt/Pnz8HALx48eK/S/DflPbu9YdOAhERERER0UftU4jt1GkUEaPbfpBAOykpCcePH8fQoUOV58zMzFCrVi3ExMRobT9+/HiEhYVpPZ87d+5/NZ1ERERERET073Oe9qFTkHUvX76Es7OzwW0+SKD9+PFjpKamwtPTU+N5T09PXLp0SWv7oUOHol+/fsrfaWlpePr0KbJnzw6VSvWvp/fvevHiBXLnzo3bt2/Dycnpb23zT1//WD7jc9nHp5JOHovPbx+fSjp5LP7bfXwq6eSx+Pz28amkk8fiv93Hp5LOz2Ufn0o6TbGPj4WI4OXLl/Dy8jK67QfrOv4+rK2tYW1trfGci4vLh0nM3+Dk5GT0hDG2zT99/WP5jM9lH59KOnksPr99fCrp5LH4b/fxqaSTx+Lz28enkk4ei/92H59KOj+XfXwq6TTFPj4Gxu5kq32QydDc3Nxgbm6OBw8eaDz/4MED5MiR40MkiYiIiIiIiMgkPkigbWVlhdKlS2PXrl3Kc2lpadi1axeCgoI+RJKIiIiIiIiITOKDdR3v168fQkNDUaZMGZQtWxbTpk3Dq1evlFnIPwfW1tYYOXKkVrf399nmn77+sXzG57KPTyWdPBaf3z4+lXTyWPy3+/hU0slj8fnt41NJJ4/Ff7uPTyWdn8s+PpV0mmIfnyKVZGVu8n/JL7/8gsmTJyMhIQElSpRAZGQkypUr96GSQ0RERERERPSPfdBAm4iIiIiIiOhz80HGaBMRERERERF9rhhoExEREREREZkQA20iIiIiIiIiE2KgTURERP+at2/ffugkEBER/ecYaBORySQnJ6NDhw64ceOGztdFBPHx8QYr3q9evfq3kkc6GPvN/gtZOS/o05KWlobRo0fD29sbDg4OuH79OgBg+PDhmDdv3gdOHdHnISUlBeHh4bhz586HTso/kpqain379uHZs2cfOikGfQzlpSlk5XssWrQI796903o+KSkJixYt+jeT91nhrOOUJS9evMDu3btRqFAh+Pv7Izk5Gba2tjh16hSKFi2q8z3h4eEYMGAA7OzsNJ5/8+YNJk+ejBEjRvyttDx79gwuLi4A0guZcePGoUOHDsiVK5fWtsnJyejatSuGDx+OvHnz/q39/V0Z02lKFy5cQHx8PJKSkjSeDwkJydL7r127hgULFuDatWuYPn06PDw88Oeff8LHxwdFihTBrl27sGvXLjx8+BBpaWka750/fz6ePXuGVatW4dq1axg4cCCyZcuGEydOwNPTE97e3nB2dsapU6d0Hu+0tDTY2Njg/PnzKFCggM70OTg44Ntvv0WHDh1QqVIlg98lKSkJN27cQL58+WBhYaFzG3UWp1KpsnJ4DO5jy5YtWX5fxt8jNjYWFy9eBAD4+/ujTJkyWvvQdbx9fHyytK9nz57h6NGjOj9jxIgR6NChA9q1a6f38wz9ZgBgbm6O+/fvw8PDQ+P5J0+ewMPDAyNHjjSYvszXemJiokY609LS4OHhYfC8WLBgAZo1a6aVn+iirnTmypULkZGRBrfNGNx/9913Brd1cnLCnj17UL16dZ2vz5gxA927d8fixYsxe/Zs3LhxAzExMciTJw+mTZuGvHnzomHDhgDSf7N58+Yp50WRIkXQoUMHODs7G/1+ppCcnIy6deti9uzZeo+5LlnN18LDw7Fw4UKEh4ejc+fOOHfuHPz8/LBixQpMmzYNMTEx/yD1/y0Rwe3bt+Hh4QEbG5ssv+/fKgOy6u3bt++V3r8jNTUVZ8+exdatW9GnTx/Y2NgYveZ69er1r6bpv/J3z4uMUlJSsHTpUgQHB8PT01PnNvv378evv/6Ka9euYdWqVfD29sbixYuRN29eVKpUCY6Ojjh79ix8fX11vv/27dtQqVRKHeno0aNYunQpAgIC0KVLl/dKr74yV1/+/OLFiyx/toeHBy5evGiwrmasfrJw4UK4ubmhfv36AIBBgwZhzpw5CAgIwLJly5AnTx7ExcVhz549Oj/j2rVr6NixI6pUqaI3DcbKy2XLlqFFixY6Xxs4cCAmT55stB5lCr6+vgbL/n9a7r99+xaFCxfGpk2b4O/vrzcdhuonjRo10vkelUoFa2trWFlZGfmWHz8G2v+SlJQU7N27F9euXUPLli3h6OiIe/fuwcnJCatXrzaYEcyfPx8dOnRAnjx59H7+1q1b4eDgoAQiM2bMwG+//YaAgADMmDEDiYmJBjPW1NRUREVF6c2w3NzcUKVKFfTo0QNv3rxB8eLFcfPmTYgIli9fjqZNm8LPzw9r165F8eLFdabR2EWamppq9DhOnDgRvr6+aNasGQDg22+/xerVq5EjRw5s2bIFxYsXN1rI6MpMIiMj0aVLF5NVCrKSTiD9bm10dLTOQNnR0dFgIHH9+nU0btwYZ8+ehUql0gogX7x4gQkTJuj9Ta9fv47o6GjUq1cPFStWxL59+3Dx4kX4+flhwoQJiI2NRWBgIMLDw1GmTBnkzJlTKzgNCwtDrVq14OzsjJs3b+Ly5cvw8/PDsGHDEB8fj0WLFiE0NBQlSpRA3759dX6PIkWKYN68eShfvrzO19etW4eoqChs2bJFKSjatm0LLy8vZZvXr1+jZ8+eWLhwIQDgypUr8PPzQ8+ePeHt7Y0hQ4Zg3rx5iIiIQFxcHACgQIEC6NOnDzp16qT8FoaO17lz57T2kT9/foiIclwy/g4ZfwsgvQJ6584dtGjRAgcPHlQq3M+ePUOFChWwfPlyvHnzBh06dMChQ4eU92VMR+PGjXUeI7U1a9Zg48aNaNWqFRITE+Hk5KSRBpVKhREjRiAqKgrnzp1D9erV0bFjRzRu3BjW1tbKdsZ+MzMzMyQkJGhdy/fu3UO+fPlQuHBhjeeTk5Nx48YNWFhYIF++fDhx4gRu3LiBHj16YO/evRrBrfp4Fi5c2OB54enpiTdv3uCbb75Bx44dUaFCBY3X09LSMGbMGEyZMgWJiYkA0q8plUqlEew8evQIr1+/Vp7766+/NL6nIampqXB1dcXOnTtRunRpjdemT5+O4cOHY+LEiRgxYgT69OmDsWPHKgFmVFQUFi5ciD179iA2NhbBwcGwtbVF2bJlAQAHDx7Eu3fv8OuvvxqstABAsWLFABguZ3bv3q3zvSqVCjY2NsifPz/Kli2LQ4cO6Q20s5Kv6aswNmnSBPPmzUPNmjXh6OiI06dPw8/PD5cuXUJQUJDGcQeA48ePK40OAQEBKFWqlMFjoI+hhjVjDV6vX7/WmTcXLVrUaANhVssAQ4HC7t27jTaKpaWl4erVqzrfX6VKFaSlpWHs2LGYPXs2Hjx4oOSNw4cPh6+vL8aOHYtjx44he/bsGu999uwZSpUqhejoaIN1hz59+iAwMBAdO3ZEamoqqlatquRfa9euRcOGDQ0GSiqVSundoPb27VutY+7k5GSwvOzVq5fRY/G+UlNTMXToUGTLli1LDXo9evQwel6oP3fdunUajWohISEwNzcHANjZ2eHixYs663yrV69GmzZt0KpVKyxevBgXLlyAn58ffvnlF2zZsgVbtmxBw4YN0aRJE4SGhurcf+XKldGlSxe0adMGCQkJKFSoEIoUKYK4uDj07NkT/fv3N1p3MFbmRkRE6MyfzczMjDZyq8uAkiVLYuLEiahZs6bO7cLCwgzWT9auXYtChQph1qxZqFGjBmJiYlCrVi1ERERg06ZNsLCwQL169dCtWze4ubkhR44cWuWlj48PtmzZgjx58qB9+/YIDQ3VCnyNlZcuLi5YtmwZ6tWrp/F83759sXz5cmzbts1oPUpNX55UpEgRg3X43bt3Y9q0aQbL/qyU+w8ePIC7u7vG86dPn0b16tXx9OlTeHt7Y+fOnXrLLGP1k2fPnhk8P3LlyoV27dph5MiRRsvnj5aQyd28eVMKFy4sdnZ2Ym5uLteuXRMRkV69eknXrl2lYMGCsmvXLhEROXTokNjZ2cmvv/4qDRo0kMaNG0vx4sXF3NxcatSoIUuWLJG3b99q7aNo0aKyefNmERE5c+aMWFtby9ChQ6V8+fLSrl07qVSpkixatEhERO7fvy9OTk4SFBQkbm5uEhYWJt27dxd7e3v59ttvpXfv3tKnTx+Nh6enp5w6dUpERJYsWSL58+eXV69eycyZM6VEiRIiIjJ37lz58ssv5cmTJzqPg0qlkocPH2o937RpU7GxsZG+ffsaffj6+srBgwdFRGT79u3i4uIi27Ztk44dO0rt2rVFRCQkJESioqL0/h5t27aVqVOnajzn6+srjx8/Vv6v75E3b14REUlJSZG5c+dKixYtpGbNmlK9enWNR1bSeeLECcmRI4c4OTmJubm5uLu7i0qlEnt7e8mbN694eHiIo6OjdOjQQfmsjL766itp2LChPHr0SBwcHOTChQuyf/9+KVu2rOzbt0+aN28uOXPmlEGDBklERIRMmzZN4yEiUr58eZkyZYqIiDg4OCjn5pEjR8Tb21ty5MihnDe61KxZUwYOHKj1/oMHD0qePHlERGT06NHi4uIiTZs2lXHjxsn06dM1Hhs2bJBKlSrJ2bNn9e5HROThw4cyZcoUCQwMFAsLC6lfv76sXr1akpOTpVevXlK6dGnZv3+/2NvbK+lYt26dlChRQoYPHy729vYyZMgQWb9+vaxfv16GDBkiDg4OMnz4cBERo8fL2D527NghpUqVkq1bt8rz58/l+fPnsnXrVilTpoxs375dRESCg4OlXLlycunSJeV7Xbp0SYKCgiQ4OFgqVKggVapUkS1btsjJkyfl1KlTEhISojzatWtn8CEiUqBAAendu7e8evXK4PE8fvy49OzZU9zc3MTV1VW6d+8ux48fN/ibNWnSRJo0aSJmZmYyduxYjd9x6tSp0qhRIyU/yOz58+fSuHFj5XyqUKGCBAUFyfLly2XPnj2yd+9ejYex8yI5OVnWrFkjISEhYmlpKYUKFZIJEybI/fv3RURkyJAh4u7uLjNnzpTTp0/L6dOnZcaMGeLu7i4//PCDiKTnZRUrVtT4PRYtWiSBgYHy448/SlRUlOTIkUPrvMmZM6eSx/z222/i7u4uFy9eVD7jp59+EicnJ9m3b5/4+/vL2rVrRUTzGjl79qxkz55dREQqVaok7dq1k+TkZOUzVCqVqFQqAaD8P+PDzMxM+VfEeDmT8T36Psfb21t69+6t95wxlq+dPn1a3N3dJX/+/GJhYaGk4ccffxRzc3O5efOm1nE4f/682NvbK/t48OCBVK9eXVQqlbi6uoqrq6uoVCqpUaOGPHz4UBITE2XYsGESFBQk+fLlk7x582o81ObOnStFihQRKysrsbKykiJFishvv/0mIiK3b9+WSpUqae2jYsWKcvv2bXn48KHUr19fzMzMdD5ERAICAiQmJuZvHysRkVGjRomZmZmULVtWGjZsKI0aNdJ4REREKOV/rVq1ZNmyZRrlf0xMjOTNm1fv7yoiEhYWJn5+fvL777+Lra2tctyXL18u5cuXF5VKJQ8ePNBKf0JCglhZWRmtO3h7e8uxY8dERGTt2rXi5eUlly9flmHDhkmFChX0Hp/MXr16Jd27dxd3d3edx9xYeZmVYyEisnLlSvnmm2+kXLlyUrJkSY1H7969Ze7cuSKSXsZXrFhRuf5y5Mghvr6+Ym9vr3XeqNOQlfMiLi5OChYsKHZ2dsp+7ezspFChQnL16lUREalataqsW7dO5/tLlCghCxcuFBHN6+jEiRPi6ekpIiKzZs2SHDlySP/+/WXp0qVK3qV+uLi4KHne9OnTld9p27Ztkjdv3izVHYyVh/ry59WrV2vl9foef/75p5QoUUI2btwo9+7dU8pV9cNY/URExNbWVm7duiUiIoMGDZI2bdqIiMi5c+fEzc1NfHx8ZMKECQY/Q133KFasmFhYWEjdunVl5cqVkpSUJCLG6zibNm0SZ2dn2b9/v/KZPXr0EC8vL7l48WKW6lHG8iRjdfiM9JX9+r6Ht7e35MqVS8zMzCQwMFDjmilWrJg4OjrKN998IyIiY8eOldDQUI2yLCNj9ZOFCxdKrly5ZNiwYbJhwwbZsGGDDBs2THLnzi2//vqrjBkzRlxcXGTs2LEGf7OPGQPtf0HDhg2ldevW8u7dO42LaM+ePZI/f36jGYFIeiaqvjBcXFzku+++k6NHjyr7sLe3lxs3boiIyMiRI6Vp06Yikn5BeXp6Gs1Ys2fPrgTqutjY2Eh8fLyIiLRp00YGDx4sIiK3bt1SKkklSpQQBwcHsba2loIFCyoXorm5uZibm4uZmZm4uLgoBZSrq6s4OTkJAPHy8pJq1aoZfFSvXl0jHb169ZIuXbqIiMjly5fFxcVFRIwXMsYyxawwlqllJZ1Vq1aVzp07S2pqqnJexMfHS5UqVZQA0lAgkT17djl9+rSIiDg5OSm/765du6REiRLi7OwsBw4cMPg97O3t5fr16yKimcHfuHFDrK2tJVu2bErhr4uTk5Pyesb337x5U6ytrUXEeMOFi4uLWFlZiZmZmdjY2GicH66urjr3GxkZKdbW1qJSqcTd3V2cnZ1lz549WumIi4sTR0dHcXNzk6VLl2p9ztKlS5Vgx9jx8vHxUSpQuvZRpEgRjYJUbd++fVK4cGERSb+OTpw4obVNbGys2Nraip2dnUbA9nfY2dkpacuKpKQkmTZtmlhbW4uZmZkUL15c3NzcdP5eFhYWYmFhISqVSnLnzq3xWsGCBaVOnTpy+PBhvfs6c+aMUnGwt7fXCHAze5/zIiEhQX766ScJDAwUS0tLadCggbi6uioBbkbr1q0TLy8vERHx8/PT+3v4+vpKjRo1dJ43S5YskapVqyp/T5w4Uby9veXGjRsyYcIEcXJyUs4lGxsbnQHmlStXxMbGRtkm8+9+8+ZN2bFjh1hbW8vNmzcNPkSMlzM7d+6UcuXKyc6dO+XFixfy4sUL2blzpwQFBcnmzZvlwIEDki1bNrG0tJTSpUtLly5dtBo6jeVrhiqMVlZWsnjxYq3XwsLCpFKlSsr3/vbbb6VMmTJy4cIF5bnz589LmTJlpHnz5lkKAow1rBlr8GrZsqVUrFhRjh07Jvb29rJ9+3ZZvHixFCpUSDZt2iQiYrQhKCtlQFYCBRH9FePixYvLN998IxcuXJC//vpLnj17pvEQEcmXL5/s3LlT67jPmDFDCRoXLVqkUUauWbNGunfvLgULFjRad7C2tpbbt2+LiEjnzp2Vhprr16+Lo6Oj1ndJS0uTtLQ0ree///578ff3l1WrVomtra3Mnz9fRo8eLbly5ZLff//daHmZlWMxffp0cXBwkB49eoiVlZV07dpVatWqJc7OzvLDDz8YbTTQ1TB36dIlqVy5svz+++9ZOi/q1asndevW1bgh8fjxY6lbt658+eWXIiKyYsUK8fPzk59//lkOHTqkNBSePn1abGxslLpext/z2rVrSpmrq2EuY6NDxvpigwYNlEDz1q1bYmNjk6W6g7HyMCNd+fO6deskNTXV4D4yp1v9UP9trH4iIuLu7q7k8SVKlFCut6tXr4q9vb04Ojq+V3l5/Phx6dGjh9jY2Iibm5v06dNHcuXKZfTmzJIlS8TV1VViY2OlW7duyrklkrV6lLE8yVgdXpfMZb+VlZXOst/FxUVcXFxEpVLJgAEDZNSoUcpj3LhxsnTpUnn37p2IiDRq1EgcHR0lZ86cUqdOHWncuLHGw1j9pEaNGrJixQqt51esWCE1atQQkfTG8EKFCr3Xd/2YMND+F2TLlk3JmDMHM7a2tkYzgoySkpJk9erV8tVXX4mlpaUEBgbKtGnTxMXFRc6fPy8iIhUrVpRff/1VYx/GMtacOXMqF70uBQoUkBUrVkhiYqK4u7srd+BPnTqlBCoZL76Mj0aNGknDhg1FpVLJ9OnTJSoqSnksXbpUDh06lOVjmTNnTuUuQcGCBeWPP/4QkfTCTp25GytksnLH2hhjmVpW0uns7KycF87OzkrF8vDhw1qZiK6Cyt7eXsmc/fz8ZPfu3SKSft7Y2tqKr6+vRmVVF29vbyWdGc/NNWvWiJ+fnwwaNEjCw8P1vj/juZvx/du3b5dcuXIZ3LdaxvNB1yPjMZg4caL4+/uLnZ2dtGrVSnbv3i2LFi1S7kZlTsepU6fEyclJnJ2d5cqVK1r7vnz5sjg7O4uIGD1eGe8G6dqHjY2NzsqVunIkkn4dHTlyRGubI0eOSL58+aRMmTI6g/X30bhxY50FVWZJSUmyYsUKqVu3rpibm0vFihVl/vz5Eh4eLp6entKiRQu9761WrZo8ffr0vdO2f/9+JcioVq2a7NixQ++2WT0v1A4fPixdunQRa2tr8fX1FQDi7e2tNMCoXbp0Sfk9bG1tNRos1Y4cOSK2trZia2ur97yxtbXVeG7QoEGSPXt2cXFx0bij5e/vr9ydynjeREZGSsmSJUVExMPDQ7Zt26a1n61bt4qHh4feY5SRsXKmSJEiOnvGHDhwQAICAkREpHjx4mJtba23odNYvmaowmhhYSHOzs4yYcIEsbOzk8mTJ0unTp3EyspK6fEhkl7p1PebODs7ZykIMNawZqzBK0eOHMp16ujoqJSN69evV/IZYw1BWSkDshIoZJS5YmxmZibjxo3TGbiq6WvoUfeW0HUH2MrKSgoWLCgbN240Wnfw8fGRbdu2SUpKiuTOnVtpiDh37pxyrYsY7mEgIpI7d27lWnV0dJS4uDgRSa9U16tXz2h5aWdnp7xHn0KFCinnRcZjMXz4cOnevbvRRgNjDXMixs8LOzs7OXPmjNZnnDp1SqnvGerBAkDJNzN+h4ULF4q/v7/B769WtmxZGTx4sOzbt09sbGyU3ooxMTHi7e2dpbqDsfIwM3X+bGlpKV5eXuLg4CBeXl4yd+5cjYaEjA9jd7yN1U9E0gPUUqVKSceOHcXOzk7pubh+/XopUqSIdOjQQWbNmpWl43bv3j2ZMGGCFCpUSOzt7aVt27ZSs2ZNsbCw0OolqcuMGTPE2tpacuXKpXGuZqUeZSxPMlaHz+jvlv1RUVHy5s0bg59trNedsfqJjY2NzjL3ypUrSpl7/fp1rfL3U6J79iD6R9LS0nSOP75z5w4cHR1Rq1YtdOrUCSVLlsSVK1fw5ZdfAgDOnz+vNc5YRJCcnIykpCSICFxdXfHLL7/g5cuXaNmyJZo2bYqjR49ixYoVANLHzeTKlQuurq6YPXs26tevjx07dmD06NEA0sdUZs+eHX379sX06dPxyy+/6Bwf0adPH7Rq1QoODg7w8fFBtWrVAAD79u1DYGAgABidACk6OhoVKlSApaWl0WN29epVXLt2DVWqVIGtra0yZqdJkyZo2bIlChQogCdPnihjXk6ePIn8+fMrx/ufunPnDjZs2KBzLMzUqVNhZWWl7E+XrKTT0tJSGWPi4eGB+Ph4+Pv7w9nZGbdv39b4PE9PT1SqVAlXrlzBlStXcPbsWbx9+xZBQUH4448/UK5cOUyaNAlWVlaYM2cO/Pz8MGTIEIwYMQILFy7UO76sefPmGDx4MFauXAmVSoW0tDQcPHgQAwYMQNu2bfH06VPMmTMHO3fuRLFixbR+u5CQEISHh+OPP/4AkD7GJj4+HoMHD0bTpk2zdKz1jSNTW7NmDRYsWIBt27YhICAA33//PVq3bq0xzvbnn3/WmExJfQ7PnTsXQUFBKFCgAGbNmoWpU6dqfPacOXPQqlUrAMDo0aMNHq8yZcpg8+bN6Nmzp859vH79Gv369cPixYuVSWwePHiAgQMHKuNuJ0+ejJ49e2LGjBnKeNDY2Fj07t0bP/30E5ycnDBo0CCMGzcOgYGBsLS0RKVKlZR9qcfw6XPixAnUr18fAwcOxIULF5TPyChXrlxYsGABli1bBjMzM7Rt2xYREREa46obN26ML774Qu9+9uzZYzAdmec5EBHcv38fixcvVq6FuXPn4rvvvsPdu3dRtGhRrXQaOy+A9OO7ePFiLFiwANevX0ejRo2wadMm1KpVC1988QXS0tIQGhqKW7duKe/55ZdflPGxNWvWRNeuXTF37lxlDPDx48fRrVs31KpVC5cvX8Zvv/2GSZMmaey3W7ducHBw0Pie3t7esLOzQ5UqVXD06FEcPXoUANCvXz90794db9++hYjg6NGjWLZsGcaPH4+5c+cCAJo1a4aOHTvip59+UsYyTp8+HVFRUahQoQI2bNhg8DiEhIQYLWeuXbsGJycnrdednJyUMbLr16+Hv7+/3t+3R48eBvO1O3fu6Jzs6MqVK8iRIweWLl2K8PBw2NvbY8SIEShVqhQ2btyI2rVrK9umpaXpLCMsLS2RlpaG7NmzI1u2bAaPR3JystZ4awAoXbo0UlJSkDt3biQnJ2u9npqaCi8vLzx8+FCZf8DV1RWPHj1CwYIFERgYiBMnTgAApk2bZjANx44dM1oGdOrUCUuXLsXw4cONfp+1a9diwYIF2LFjB8qXL4+OHTti9OjRmDRpEs6ePYulS5fqfG9AQAD279+vNd531KhR2LFjB+7cuYNjx47Bzc1N5/uLFClisO7Qvn17fPvtt8oY2Vq1agEAjhw5ouQpI0aMwNSpU9GzZ08EBQUBAGJiYtC3b1/Ex8cjPDwcT58+hZ+fH4D0c/Lp06cAgEqVKqFbt26wtbU1WF6WK1cOV69eNVgux8fHK9eXra0tXr58CQBo06YNypcvD09PT1y4cAE5c+bE1q1bMWvWLADp42LVc8ykpKRofW5qaioePHgAwPh50bdvX2W/GSUmJioTPRma+XnGjBno3bs35s+fD5VKhXv37iEmJgYDBgzQeR7pmgBv4sSJaNy4MSZPnozQ0FAlP9ywYQPKli2LJk2aGK07GCsPAd35c0pKChISEiAiSExMRKdOnXTWOVUqlc78rF+/fsr/09LSDNZPpk6dihkzZmDYsGG4ffs2Vq9ercxFcPz4cbRo0QJmZmYYPnw4Dh8+rLO87NatGzZs2IAFCxZg+/btKFasGPr06YOWLVsq+enatWvRoUMHdO/eXZkYbtCgQTqPm7u7O0qVKoWZM2cqz2WlHvXq1SuDedLo0aMN1uGB9PrBPyn7s1ImL1iwQOu5jOXXo0ePDNZPcufOjXnz5mHChAkaz8+bNw+5c+cGkD6vk6urq9G0fKw4Gdq/oFmzZnB2dsacOXPg6OiIM2fOwN3dHQ0bNoSPjw8iIiKUjKBbt26oW7cugPTA1crKCj/++COOHz+uXCDW1tZo27YtOnXqpBQqYWFhGDduHAoXLoxevXqhY8eOANIz9dTUVDRp0gSNGzfGixcvEBoaivnz5wMAfvjhB1y6dAkigj179iBbtmwoUqSI1sm/Zs0axMbG4vbt26hduzYcHBwAAJs3b4aLiwsqVqyobJtxEpsiRYqgZMmSymvGJit58uQJvv32W+zZswcqlQpxcXHw8/NDhw4d4OrqigkTJmD69Om4ffs22rVrp3x2REQEHB0dlYmt1P7OLKu7du1CSEiIMlFP0aJFlYnfSpUqhd27d2PKlCm4fv263kwtOTnZaDrr1KmDdu3aoWXLlujcuTPOnDmDXr16YfHixfjrr79w5MgRnQVVx44dUatWLaxfvx5RUVE4ceIEdu3aha+++gpXrlxB9uzZsWLFCvTv3x/Xrl2DiMDX11frNz1x4gSSkpLQvXt3REVFITU1FRYWFkhNTUXLli0RFRWlVJp0UalUWLt2Lb7++mvExsbi5cuX8PLyUibJOnHiBDw8PDQKRl2mTp1qcObzChUqoHnz5ujUqZPeAmDXrl348ssv0aFDB0RFRaFr165Yu3Yt7t27h2+//RbZs2dHVFQUfHx8lMm1jhw5ggsXLiB79uzKRD9Xr17Ve7wiIyNRr149tG7dWtnHhQsXcOjQIURHR8PZ2RmNGzfGlStXlALh9u3bKFCgANatW4f8+fPD1dUVr1+/RkpKijJDq/r/9vb2OifikvSeRlCpVEYbs4xNEKI+V2vXro2OHTuiUaNGOoOaV69eoX379qhWrZrOxqbJkycbnHglc0XRzMwM7u7uqFGjBoYOHQpHR0ccPnwYLVu2xM2bNzXSp/6uqampBs+LIUOGYNu2bShYsCA6deqEtm3bagRg0dHR+PLLL/H69Wt06NABQHrl/vbt29iyZQsqV66MR48eITQ0FFu3blWOQ0pKCoKDgxEVFYXY2Fg0bdoU+fPnR7ly5QCkTwZ17tw5eHh4wNbW1uDvoZ7oacmSJRg1ahSuXbsGAPDy8kJYWJiSTyclJWHgwIGYPXu2UpFXH1NjEwepj5WxciYuLg6Ojo5YtGiRMpnNo0eP0LZtW7x69Qr79u3Dzp070b17d2zevFlnQ6exfO3w4cN48uQJ/vjjD2TLlg1nzpyBubk5GjVqhCpVqhgNQgCgYcOGePbsGZYtW6ZMeHj37l20atUKrq6uaNq0KdavX28wCOjZsycsLS21GtYGDBiAN2/eoE6dOhg3bpxWg1fPnj0xePBgjB07FmPGjEFwcDBCQkLg4uKC8ePHIzIyUpnozZislAG9e/fGokWLUKxYMZ2BQuvWrbUqxpUrV1YmF1NPOHfr1i0cPnxY6/3FihXD+vXrERoaiqFDhyI8PBxhYWG4fPkyFi1ahE2bNmk0cugqL/fu3Wuw7rBmzRqsWrUKt2/fxjfffKPkpQsXLoSLiwsaNmwId3d3REZGas28vGzZMvTs2ROPHz9GsWLF8PPPP6Nq1aqoVasWSpQogZ9++gmRkZGYNGkSAgICDJaXQ4YMwbBhwzBw4ECdFfhixYrBz88Pq1evRsmSJVGmTBl07twZXbt2xfbt29G8eXP06tUL06ZNQ86cOfH69WtcuXIF1tbWmD9/Pn777Te4ubnh7t27Wg1zXbp0gbe3t9EGMQBo27YtTpw4gXnz5ikNsEeOHEHnzp1RunRpREVFGXy/iGDcuHEYP348Xr9+DQCwtrbGgAEDlEaQ1NRUjBs3TucEeHny5EGdOnXg6uqKlJQUjYDl5s2bsLOzQ3BwsNG6w4EDBwyWh6NGjdKZP2ds9Hz8+DHKlCmjUQZklCdPHmWG9evXr2PlypVo3bo1Hjx4ABsbG4MrMqhUKr0TQGZkbKK+Fy9eIC0tDS1atEDnzp1RokQJre3u3buHQoUK4c2bNwDSGxY7duyIuLg4WFtbG1wtxFA9KigoCFu2bIG9vT2++OILrTzp0aNHuHbtGu7evYvg4GDs3r3bYB3e3Nxcq+zv168fRo8eDXt7e/Tr1w/JycnYvXs3goODtdI6efJkRERE4I8//tBZL1A3jmWW1UnL1Mfim2++QeHChZX6XmxsLC5duoRVq1bhq6++wqxZsxAXF6eVv38qGGj/C+7cuYPg4GCICOLi4lCmTBnExcXBzc0N+/bt05q5N7PAwEBcunQJderUQefOndGgQQOtO1uPHz+Gh4eHwbu5qampePHihc6MdfDgwQbToG6lMrR80sOHD9G8eXPs3btXY0bl6tWrY/ny5cpMuLdu3ULm00xdWWzbti0ePnyIuXPnwt/fX5mZdtu2bejXrx/Onz9vMJ3q76mvkPH19UXHjh0N3rFWFyBhYWHK7LgeHh5o1aoV6tati27duqFx48ZGGyaMUWeq1atXx8OHD9G2bVtlxt/58+dj2LBhBgMJ9THPkSOH8rs/ffoUrq6uUKlUCAsLM7j/jEFbfHw8zp07h8TERJQsWfK9lvcB0o/ZmTNnkJiYiFKlSmHs2LFYu3YtXFxc9C59BEAJHg3NfL5o0aIszfh67do1TJgwAadPn0ZiYiKePn0KHx8f2Nvb632PuoBv166d0c8fOXKk1j5KlSqFwYMHK706RAQ7duzApUuXAKTPZFyrVi0lWFLP0KqP+n0AlAa3jKpWrWo0ncbcunXL4AoGgPHGpoCAAERFRaF+/fo6Z3uNiIgwmo6AgAD4+/tj0KBB8PT01PqMmzdvGjwvnJ2d0alTJ+XuiS53797F+PHjcf/+fQDpv8f333+vMWM9kF4xUh/7woULo2DBgsprt2/fxqxZszR+0++++05pTHkfr1+/RmJiot48//Xr10oQly9fviyd92rGypm//voLDRs2xI0bNzQagvz8/LB+/XoULFgQixYtwsSJE3Hx4kWdDZ1TpkwxmIbnz58brDAmJydj1apVuH79OgYMGKBzCZvbt28jJCQE58+fV9J5/fp12NraIl++fLC0tDTYIHbixAn07NkTixYtQu7cuTUa1uLj49G2bVv89ttvSElJ0bh7nrHBS71WrLW1NXbs2IG6devi6dOnsLKyQlRUlDKTuLGlEY0xljdGR0drVYzVszZnLENFx8oHGe8I7t+/H+Hh4Rr51ogRI1CnTh2js5KrZxPXV3cwVn8B0mddPnbsmFa5cuXKFZQtWxbPnj1DREQEzM3N0atXL+zcuRMNGjRQevBNnToVFStWNFheZmzQz3gMMx6LTp06IXfu3Bg5ciRmzJiBgQMHomLFioiNjVVmxTfUaFChQgWDDXPqY2HovPD29kZoaCg2btyo8RkhISGIiopSgkdjSwImJSXh6tWrSExMREBAgHLzAzC8lF5ERAROnDhhcGb0rNYdDJWHHTt2NJo/iwji4+P1lkdZmWHdmH379hl83dhs9IsXL8Y333xj8IZN7969cfDgQUybNg1169bFmTNnlHx11KhROHnypNF0AukrTWQ8lhlvdPz+++9ISUlBu3btcPz4cdStWxePHz+Gubk5KlWqZHS52gULFugs+6tXr57lulqlSpUwd+5c9O/fH8OGDcOPP/6ImzdvIioqCmPGjMGQIUNQsmRJg43D6h5Bhty4cQO//vorrly5AgAoVKgQunbtqncloU8NA+1/SUpKCpYvX64RjLRq1Uq5I5K51S7juoh79uxBhw4dtJYUeJ/1CJ2cnAwu/ZIxk9YlK8snNWvWDNevX8eiRYuUqf0vXLiA0NBQ5M+fHxcvXkTBggURFhams3Lu7OyMHDlyYNu2bcoyXepA+/r16yhWrBhmzJhhdE1EY+u1jhkzxmAQcezYMZw6dQr58uWDq6srDhw4gCJFiuD06dNo2LAhbt68ifbt2xs8XoYyLCC9VdsYQwWVet3ykydPwsnJyWjg9DELCgrCN998g379+im/uZubG2JjY9G6dWuN4FMXXd1h6Z8pW7aswcam4cOHY9GiRcowl7/D3t4ep0+f1tvVU9d54efnh6NHj6Jx48YoXLjwe6/5/LkzVs6kpaVh+/btGhWY2rVrK3ccdDV0njt3Dubm5hg0aBDGjx9vcP/qteJ1VRjPnDmT5SVsRAQ7d+5Urv3Y2FilW7ExI0eONJr/JiQkKP8fMmSIwW1DQ0Px+vVrXLp0CT4+PkoXa2NLI6qPhT5ZKQN0VYwz3hE0Jivlwj9d3zw8PNzg6yNGjDDaw2DGjBla77t16xaOHz+O/PnzK8vXGWLsuOTJkwdpaWlIS0tTbhIsX75cCda7du2a5TV6DTXMGTsvVq1aBQCIi4vTaLzLmA/OmjXL6JKAhuTPnx+//vqr3qX0vLy8DC6d+F+7cOGCzpseI0eORN++fdG2bVuN73Hy5EnUq1dP4zoG0uvEu3fvRuHChZXu0LrupmasexpaVlZdzzp16hSKFi2qd7s8efJgxYoVKF++vEY6r169ilKlSuH27dtITU3VulHy9OlTWFhYYN26dWjWrJnGEptA+o2t5cuX68wrdOVJ/7Z8+fIhMjIS9evXh6Ojo1JPrlevHhwdHfHHH3+81w2e/1UMtD+A9221S01NxdmzZ1GqVCmj3QrVrl+/jrp16yI+Ph7v3r1TAuXevXvj3bt3mD17NoD0roSXL18GkF4JU3cxzEqLnbOzM3bu3KnVvffo0aOoU6cOkpOTDVasgfR1bk+cOIECBQpoZFjqdWbd3NwMrom4Zs0ao4VMgQIFDAYRYWFh2LNnD/z9/REQEIAJEyYgJCQEp0+fRsWKFZU1eQ3JPH4kOTkZr1+/hpWVFezs7PR2sXkfxtYtV8vclX/x4sVZ3sfUqVMRGxurt6vQmjVrDK4Dq+5maIiDgwPOnj2LvHnzKr9H/vz5lTHjWVnLeMuWLTA3N9fq7rRt2zakpaVprV+py+3bt7XWi12wYAEKFy5stGEFyNo6r/Hx8QY/Q93FLONamZUrV8aGDRvg6uqKqlWr6rzm3759C2tra/z1118614EfMmSI8r7MhXlmT58+1ShEdTU2JSUlYe/evRoVzMzp+fnnn7Fnzx6d58WJEyfQoEEDtGvXTu9Yfl3nhZ+fH27evInChQvD0dHR4JrPZ86c0fm86v+vG+3j4wNra2ut3i2PHj1Sth04cKDBY1WsWDGlW7o+48ePx4ABA5RrJHMRm5qaqnMN9+fPn8PR0RFmZmbo06ePwX306tXL4OtZpauhM3/+/Dh8+DBq1KihdFXVRaVSYcGCBXorjCVLlkT9+vUxadIkjd/z0KFDWkMIPgWGGoKaNGmCV69eaWz/b5QB7ysxMVHrWixVqpTB8tLFxcVgPSNz993k5GTcuHEDFhYWyJcvX5Z6GGTslfAhu4NmpdHAGGPnxZ07d4x+RkBAAMaNG4dGjRppfMa5c+dQtWpVfP/99wbXuLa1tcWlS5eQJ08ejfdfuHABZcuWxbJlyzBp0iTMmjXLYABpaBjgiRMnYGlpqfTmWr9+PRYsWICAgACMGjUKVlZW2LVrFyIiIjTWqu/Tp49yp/b69eto3Lgxzp49q9FLI2NZdeHCBfj6+mrdeAkICEBISAiqVKmCHj164M2bNyhevLhy02T58uVo2rQpnj9/rvGdkpOTcfLkSQwfPhxjx47F5s2bdX53dTkxZ84crF692uCdbzs7O6UxJGM6T58+jSpVqqBChQpo0KABvv/+e433zZ49Gxs2bMC2bdtw//59rd4hT548gYeHh8HGALUbN24gJSVFqzx0dnaGSqXKUvdtY3mSvb09Ll68CB8fH+TMmRObN29GqVKlcP36dZQsWVLrWOvSq1cv5M+fX6vM+uWXX3D16lVMmzYNz549w9GjR3We31lpoPzYcTI0E8nKWB21MWPGYPbs2Wjbti2WL1+uPF+xYkWMGTMGffr0UbripKamomrVqjh06BCsra0xfvx4lCxZEjdv3sSQIUPQrl07jclGFi5ciPHjx6N3794oU6YMTp8+rUwGAaRPfNC5c2e8evVKKQzVJ7a5uTnatm2Ln3/+GevWrVNa7DIWukWKFFG6OhqbxCYrk5VUrlwZixYtUsYaqYOtSZMmoXr16tiyZYvy/nXr1qFp06bo0qULKlasqEzQdvfuXZ37SEtLQ3JyMi5evIhly5YBACwsLPDmzRs4ODggPDwcDRs2RPny5XHgwAH4+/vjyy+/RP/+/XH27FmsWbMmyy3AGcfaqsXFxaFbt25K5f3BgwdaFfCMlXBj4xl//PFH/PDDD1i8eLHOyYH0deV3dnbW6O5+4sQJpKSkoFChQgDSW+vNzc1RunRppTU1ODgY27dvR506dXDlyhU8ePAAjRs3RlhYGMLDw1GmTBmdvRTUDAXrLi4uuH//vkbXpz179mD//v2YOXMmxo8fb/DcBtIDycyTZwDpd8aGDBmCevXqGW0waNmyJbp06YI2bdogISEBtWrVUiat6du3r97vpu6aGBsbq4wJfvXqFbJly4bHjx8r3St79eoFX19fg5XWhIQEtG/fHn/++afG51evXh0qlUpvw8WAAQMwatQoALq7bWfsPv/TTz/p3X/G7dXHKGfOnLh27ZrSFfbx48cICwszOPFKx44dsX37dnz99dcoW7aszm0aNGiAvn374uzZszrHVOo6L4D0yaS8vb0REhKic9IUtRIlSij7zVyBA9LzpSpVquDgwYMavVtOnTqlbLNkyRK9x0jdHTXztZ6cnIxz587h2bNnqFGjBtq1a4f4+HgMHz5c7zXSqVMnREdHo02bNso24eHh6NKlC+zt7Q12xVepVEqlJS4uTm/jxogRIxAdHY2ffvpJqfgGBARg4MCBqFy5MoD0sfmZu6unpaUhNjYW1tbWRhsZzc3NUbduXa0K48uXL3HhwgWd5aK3t7fWnSlD6Tx27JhSnmR05MgRmJub65wETZfU1FSsW7dOI5AICQmBubk5RASrVq3SeyzXrFmjdwIyDw8PPH78GG/fvtV6LS4uDhUrVlQatps0aaK1zaZNm5T/G+tppq4Y67sjGBISghs3bqBHjx7Yu3evRprU+Za+iT3V5WXmRh51oLJ161YMHDhQZ4+AFy9eoF27dmjcuDEA4Ny5c8qYZnV9wc3NDW/fvsXWrVuV96lUKo3uoZMmTVKuF3Wjvz4nTpzAtWvXMG3aNI3zpnfv3siXLx+A9MbpqlWrYvbs2RqNQY8fP0bZsmV1NhpcvnwZtra2yJ8/P549e2YwDVOnTjV6XqSmphqc32L37t24ceOGzq7w1tbWePbsGebNm6eRV2SmbwK8VatWoWTJkmjbti1ev36N4sWLw8rKSmuuiUuXLhkcBuju7o6uXbtiyJAhCAwMxPXr19GsWTM0adIEK1euxOvXr1GwYEH07t0bX3/9NXr37g0AOHz4ML788ktERESge/fu6N27N/LmzYtdu3Yhb968OHr0KJ48eYL+/fvjp59+QmhoKK5evarVZfjAgQPw8/PDvn378OOPPwJIn5BMRPDs2TMsXLgQY8aMQdOmTXWO465duzasrKzQr18/ODk54cSJE0hNTdWqAxUuXBivXr1CzZo1lcl8dTE2MdyRI0d0NiBVq1YNP/74o8bQj4z69OmjjKM25vjx4+jQoYNWoN28eXNER0dj6NChRj8DMFxXy5UrF+7fvw8fHx/ky5cP27dvV3qBGmvAV1u9erXOcqBChQqYMGECatasiVatWiExMRFOTk4ax0WlUjHQpv/TqFGjLG2nUqlgbW2ts7XM2dkZz549w6pVq9C6dWsAwMaNG3Hjxg1cunQJixcvxsqVK9GnTx+Eh4dj6tSpGpONhISEIDAwEHPmzMHZs2dx6NAhra5Rvr6+uHv3Lvr164fo6Ghs3LhRmdjswIED6NWrF/r3749Hjx7pHIv16tUr5UKoUaMGevfurTWJTd++fVGzZk20bdsW/fv3R0JCgt7JSiZNmoSaNWsiNjYWSUlJGDRoEM6fP4+nT5/i4MGD2LdvH548eQIfHx9s375dyYBsbGyUiSiMFTJxcXEGg4ipU6cqFcqwsDAkJiZixYoViI+PV8b7/J1xKAUKFMCECROU7tC6KuBdunRRtjdWuXZ2dsbVq1fh5eWFPHnyaI1FLlCgAF6+fInz589rdeXPnTs3li1bhqlTp8LR0RELFy5U7sL/9ddfaN++PSpXroxx48YphaKjoyOmT5+OvHnzomvXrsiZMydmz56NqKgotGnTRm9ajQXrumY+t7CwwIIFC9C1a1csWrTI4LkdGhqKuLg4BAQEaO27cOHCuHr1qtE0AOmVQfXkNH/88QcCAwMxbtw4HDt2DFOmTNFoBNOlb9++aNCgAWbPng1nZ2dlcqLWrVsrFY3MY7XUldapU6di7Nix6NOnD549e4YjR46gWrVqWLt2LR48eIAxY8ZgypQpypCJzDLOBqprxtqME57s27cPFSpU0JpjISNjjU0HDhzAnj178Oeff+qco2DXrl3YsmWLxiSJmX333XcAdN9FUqlU6Nu3r8EZ8R8/foz58+dj586dKF26tNb5v3btWgwePFhj1vejR49iypQpGDlyJFJSUtC+fXuULl0aMTExcHR0xOrVq/H27Vv07t0bVatWNXheZ9xPZmlpaejWrRvy5cuH0aNHY//+/Ton0VH7888/sXnzZo3jpT5nMv9fn99++w3dunWDm5sbcuTIoVVB8fPzQ/v27dGkSRMlMD9w4ABq1qyJqKgotGzZ0mhDpzH6Kox37txRJhbK7MqVKxpB1O+//24wndOmTcOgQYO0Au27d+9i4sSJOHLkiNEeFX/88Qe+/PJL3L17V6lcjx8/Hrlz58bmzZsRGRmJX3/9FdWrV9c5fwBgvCFIlwIFCqBcuXIYMmQImjRpojMIyPi9Mk/umZmxO4Kpqalo3bo1RATz58/X+V369etnsLzUd+7NmDEDsbGxOl9zcnJCWFgYGjRogDZt2ujt6pz52D169Ah9+vRRgru//voLlpaWsLe3R+vWrTFz5kwEBAQoDa6HDx/G+fPn8f3332Pbtm0ICQlBiRIllOvo4MGDKFKkiDKz/c2bN2FhYaH0EsqRI4dynG7duqXzzmGVKlXg5OSEZs2aGeyhpT6uxs6L3r17K/NbFC1aVOe5lTdvXpw6dUrrN9m6dStUKhVWrlxpMG8dMWIEQkNDcffuXaSlpWHNmjUaE+Ddu3dP73uB9MkE9dUdevXqhWXLluHKlStKnrZy5UpUrVoVS5cuxcGDB9G8eXOICCIiItCjRw/lc3v16oWKFSti3Lhx6N69O2JiYrB79264ubnBzMwMZmZmqFSpEsaPH49evXqhc+fOBmdYHzRokHKTYevWrWjatCns7OyUlTcM8fT0xOXLlzFmzBhky5YNCxYsUIahPX/+HJ06dUKlSpXw5s0bnD9/HpUrV0b+/Pm1ypkTJ05g3LhxqFevHi5cuICUlBRMnz5dY2K4KlWq6JytvnHjxvjrr7+gUqlQs2ZNjTI5NTUV58+fR7Zs2YyO8VapVDh58qTOc2LgwIFYsWJFlmYMN1ZPaty4MXbt2oVy5cqhZ8+eaN26NebNm4f4+Hj07dtXSbehCdNev36tM99zcnLC48eP0b9/f3To0AHjxo17rzlKPikmXCqMsihv3rwG10U0trajiBhd7zXjOtsZ97F//37x8PCQ7Nmza603KyKye/ducXNzk8qVK0tkZKTy/uvXr4uISI8ePSQ4OFhEROLj46VEiRJiaWkpfn5+4ufnJ5aWllKyZEm5ffu20fWt1Z49eyZjxoyRb775RurVqyc//vij3Lt3T0SMr4koIrJu3TqD67U2bNhQ5syZIyIi/fv3l/z588uYMWOkVKlSUrNmTb2/06hRo+TVq1fK/w099Dl58qTymzk4OMjJkyc1Xn/27Jne9+pKj6GHsfVoRUS8vLzk3LlzWtucPXtWcubMKXZ2dsoaqtmyZVPW/7xw4YLkyJEjS+vABgYGyi+//KJ852vXrklaWpp07txZRowYIe/evZNOnTqJhYWFqFQqsbS0FDMzM2ndurWkpKRkaS1jT09PZW33jHbs2CHu7u5G0yAiRteLNeZ91kXPbNOmTVK1atUsrd+bkpIiK1eulPDwcAkPD5dVq1ZJcnKywc+3sLCQhIQEERExMzOTBw8eGNz+2rVrcvr0aRERSUxMlK5du0pgYKA0adJEbt68aXStTH9/f+X9f5ex80LXWs8Z13z+4osvZOvWrVqfu3XrVvniiy9EJH3Nzty5c4tI+vq36mvh1KlTkidPnn+U/kuXLkmOHDnE399f59q7GWVl3Vq1tLQ0nesm+/j4KOesLoULF9a51uuUKVOkcOHCIpJ+3Xt4eEjdunXFyspKvv76a/H39xdPT0/lOt+5c6fUr19fyePr168v+fLlk5IlS4qZmZkEBgZKyZIllUexYsXE0dFR8ubNK40aNZKkpCSlDLl165aULFlSKdOykk57e3ul/Mro+vXr4uDgICLp5YSbm5t89913MnLkSK28sV69elK3bl158uSJ8v7Hjx9L3bp15csvvxRXV1fZvHmz3mMpkl52VKpUSe7fv6+s+3zgwAHx8/PLchmgS9++fSUxMVFERKKjow1e21999ZU0bNhQHj16JA4ODnLhwgXZv3+/lC1bVvbt2yci6fmaOl/SxVh5qc+1a9cMfo/9+/drrKNtzJIlS6RixYoaab106ZJUrlxZfv/9d+nYsaMMGzZM630jRoyQ9u3bS4kSJWTw4MFarw8ePFhZr97MzEyuXbsmjRs3Fi8vL6V8TEhI0KiDZHbmzJks5wfGzovs2bMbPbd+++038fb2luXLl4u9vb0sW7ZMxowZI/b29uLu7p6lvGLfvn1Sq1YtcXd3F1tbW6lYsaJs27YtS98hK3UHR0dHpVyuVauWTJs2TUT+r7y0t7fXua75lStXlPXCXVxclLqkn5+f7N69W0RErl69Kra2tpKWlqZ8b3Vd0cbGRjkPChQoICtWrJDExERxd3dX6gCnTp2S7Nmzi4horc196tQp+fPPP6Vq1apSsWJF8fLyUurGGZ07d068vLxk1KhR0rVrV7G1tTVY17t69ap06tRJvvjiC/H395dWrVop9aVq1apJjx49tPbxxRdfiI+Pj6hUKhkwYIDG544bN06WLl0q7969M/ZziUj6b6arnNm7d6/Y29vL8+fPjT6yUk/KKCYmRqZMmSIbNmxQnhs+fLjkzJlTfvrpJ7GxsZHRo0dLx44dJXv27DJ9+nQpUqSI/Pzzz1qfFRkZKf7+/mJnZ6czf/+cMND+AMaNGycBAQFy+PBhcXR0lP3798vvv/8u7u7uEhkZKT4+PrJt2zZJSUmR3Llzy6ZNm0QkPSNQF2QFCxaUgQMHan32wIEDpWDBgvLtt99K586dReT/AuWXL19KjRo1pF27dmJra6sz8z537pzY2dnJ/v37xcHBQb777juxsbGR3r17S+3atcXe3l5iY2OV7dPS0mT79u0SGRkpkZGRSgOCiMjNmzcNPpKSkqRGjRo6gyq1v/76S7p37y4hISHy559/Ks+PGDFCxowZo/xtqJAxFkTEx8crDRsi6YVL79695ddffzXwK2pav369xmPdunUya9YsKVKkiNStW1dERGcF3MzMTB4+fCgiItWrV5e//vory/vMTFcgLyJy4sQJjWBfXwOLg4ODeHt7K4VFYGCgLF26VEREDh06JE5OTjJo0CAJDw83mA5jwbrarVu3ZPPmzbJixQqNc8DYuS0i0qVLFwkMDNQI+uPi4qRYsWJKo4yxNJQtW1YGDx4s+/btExsbGzl16pScPn1aDh48KN7e3loFduaHm5ubku4CBQooQd7FixfFzs7O4DGKi4sTOzs7cXR0VNLp4+MjBw4cEJH0IMLW1lbOnTsnfn5+YmdnpwQy9vb24uvrK2fPnhWR9EB87ty50qJFC6lZs6ZUr15dbG1tJU+ePFKiRAlRqVSybt06iY6O1vkwhS1btkjdunXl5s2b//izdJ0XKSkpEh0dLU+fPtX7PhsbG7l48aLW8xcvXlQaTtzc3MTa2lpE0q/H9evXi0h6RU1dGVy0aJFUqFBBcubMqXyfiIgIWbduncF0b968Wdzc3GTbtm1Sp04d5XfVZfHixfL1118rDXm6zJ07V4oUKSJWVlZiZWUlRYoUkd9++0153dHR0WAFxcrKSmfFNy4uTjkGIoYbOmfMmCEWFhbSvHlzmT59ukyfPl1atGghZmZm8uWXXxqsMD58+FBq1aolLi4uYm5uLrlz5xZLS0upUqWKElhmJZ3ZsmWTQ4cOab1+8OBBpTx0cnJSrh1d7OzslDwgI/Xv7uvrq/PcychYQ1BWygBd3qdRLHv27EpZ5uTkpASpu3btkhIlSohIeiU/Yzmsy98JyiZOnCh58uRRzgP1Y9q0aTJ48GDx8vKSFi1aKNsfO3ZMBg4cKM2aNZPGjRtrPETSAy1dgUJsbKz4+vqKk5OTzrrBlStXxMnJSaytrfU2yKrPb5VKpRzPIUOGiK2trSxevNhooP0+jQbGzoucOXMqDaiG/P7775I/f34lwPT29pa5c+dmKa/IipSUFFm1apWMHj1aRo8eLWvWrJGUlBQRyVrdoXr16tK2bVtZtGiRWFpaKtfs3r17JU+ePNKiRQuZNGmS1mdMnjxZmjVrJiIilSpVkrVr14qISIsWLaRu3bpy4MABadu2rXLjRCT9mJ4/f16OHDkiL1++VJ5X50cuLi5SvHhxSU1NFZH0oK1atWoiIsqNnMw3d4KCguTixYtib2+vsw60Z88esbe3l7CwMDlw4IDBRiVjDhw4IDY2NlK5cmUlX6xcubLY2NjIvn37JCoqSt68efO3P18kvdHtm2++UX5DkfTfGIAAEDMzM70P9THKal3NED8/PyVGcXBwUOpk6rJi3rx5YmtrKyNGjJC9e/fK3r17Zfjw4WJnZydz5syRxo0by4oVK/7RsfjYsev4v0DX5ETA/022UK5cOaSkpKBmzZp4/fo1qlSpoqyL2LNnT2VtaXX3YvVEEkeOHFFmVoyIiEDTpk3x559/aqz3GhcXh9WrV6NYsWIIDg5GQEAA3r59i5YtWypLvyxbtgzx8fEYOXIkFi1apCxj8ObNG4SFhSEoKAiVKlXCqVOnMGHCBAQGBipjM2JiYpTJMNTfqXbt2hprc6qpu0HpGk+mUqmQJ08evRMYqbm4uOCXX37Rej7zTIeVK1fGjh07dH5Gxtlr7e3tlfFyGd+beaxu0aJFsWTJEiQkJGRpQpTMQwdUKpWyjrB6iZxp06ZhyJAh+PXXX5UxSA4ODnj8+DHc3d2xd+9eJCcnG9yPemiBej3VjMvlGOvKD6R3XWrfvj2mTJmisabnwIED0aRJEyQnJ2PHjh0IDAzEN998g969e2P37t3YsWMHatasibdv32LOnDnYuXOnznVgp06dCldXV2Wss7e3N86dO4fAwEA8e/ZMY3IlHx8fnetNGju3gfSxfHXr1kXhwoWVyczu3LmDypUr46effsLWrVuNpmHixIlo3LgxJk+ejNDQUBQvXhxmZmbo2bMnypYtq4z5FR3zRaq7fqmXsKlatSpGjBiBx48fY/HixcqEM5m7zooI7t+/j1GjRqFAgQKwtLTE5cuX4evri+LFiyvnxuzZs5EzZ0506tQJRYoUQWxsrEZX/3bt2qFLly44dOiQzq6J9vb22L17N27dugUzMzOlu7yu75Gamgo/Pz8cO3ZMYz4H9fmmnvzEkDJlyuDt27fw8/ODnZ2d1nnx9OlToxMPtW7dGn5+fjrPC3Nzc9SpUwcXL17UmnhQrXDhwpgwYQLmzJmjDJlJTk7GhAkTlHzT398fFy5cAACdXeQzz/6r7lrq4uKCadOmoWHDhlrj59S/6ebNmxEaGopmzZrh9evXynJduo7FlClTcO3aNXh6eupcsuqrr77C1KlT0bNnT415Cvr27Yv4+HiEh4fjm2++wfbt25Uu+Znlzp0bu3bt0hqPu3PnTmUZrfj4eOTOnVsZ95hRfHy8MpREX3dQ9WRo+pbC2bFjh8ElbLKSzjJlymDo0KFYv3690gXx2bNn+OGHH5Ryx9vbG46OjjrTAKSPdVXnBxklJibCysoKo0aNQlhYGObPn693rXQrKyv89ttvGDFiBM6ePau1NKK+MuDdu3dQqVTKmOXMzM3NlcnoRAQxMTF6z/HU1FTle7q5uSnr+ebJk0eZ1HTu3Ln47rvvcPfuXRQtWlTj3EpJScGmTZvQoUMHveVl5mFSIoKEhAQ8evRImUMjIzMzM7i7uytrdwPGu6QCwP3793V2r01NTcWDBw/g5OSEgwcPao1BPXjwIGxsbODk5IRTp05pvX7q1Cll2FvG7zF+/HgUKVIEnTt3VoYlZa6rqa/lxYsXo169elma5FF9XgwfPlznkpn9+/c3OL+FWqtWrdCqVSutJQFLlixpMK/IOGwtKSlJZzqTkpIMDp3ISt1h2rRpaNWqFdatW4cff/xRuV5XrVqFChUqICAgAGPHjsXevXs1uvofPHgQ/fv3R2RkJIoWLYqNGzeiUaNGCA8Px1dffYXKlSsje/bsWLFihZJeKysrnUPDvv/+e5QtWxa3b9/WWD3Bz88PY8aMAaA9lEp9fqrzqIYNG6JDhw6YMmWKMpHvsWPHMGDAAKU+4OrqqnfiTyB9gq7q1aujatWqOldHqFixImJiYjB58mT88ccfsLW1RbFixTBv3jwUKFBAmSMjM11zOOgzYcIEVK1aFYUKFVI+b//+/XB2dkZERESWVm1o1aqVwXrS+PHj4enpqTUB6Pz58/Ho0SMMHjxYGRoKpNdn1ROkffXVVxg+fDiWLl2Kd+/eYezYscoQJV9fX8yaNQtt27aFmZkZBg4ciAsXLugcYmpsNYdPAWcd/xfkzZsXjx49wuvXrzUqx3Z2dnBwcMDDhw+VtaLfvXunc11EQ2s7NmzYEEB6YDFr1iyNGR4zrveakpKCFStWaFRy1Eu/nDt3DsHBwXj37p0yi/Xp06dhY2ODbdu2ZWlNUAAGZ6AeNmyY0fFkffv2hbW1td4JjtQyzsqcUbFixdCpUye0bt1amRztfbm6uuLw4cMoVKgQIiMjsWLFChw8eFCpxF6/ft3oOBR9szeq17hWe/XqFVJSUpQKeGJiIlJSUmBubo7U1FRUqFBB75Ij06ZNM7hcztixY7XWo719+zaKFi2KDRs2IFeuXHj9+jUGDBiA+fPnK0G9hYUFOnbsiMmTJ+Pdu3d4+/YtvLy8lLGa6pmehw0bZrAgUKlU2L17N1q2bIkyZcoo44R//vlneHp64vnz5yhTpozRtRGnTp2KO3fuYObMmQbXMpb/v4b16dOnlYJMPfeBrjQ0bNgQO3bsQKlSpZR1zzOvF3vr1i2kpaXB3t5emQNAn0ePHhlc51UduGeuXImIMmb++vXrWmtlZly/t127doiNjdW6Hs+dO4cvvvgCb968gZubm96lt9QTjFy+fFnv+rfOzs4wMzNDQkKC1jYPHjyAj48P3r17h1WrVuk9/7Nly4b4+Hh07NhR57jQ0NBQrcl+Ms9WfOrUKeTKlQtVq1ZFtWrVULVqVY3gq0yZMpg4caJS8cvs0KFDCAkJgZmZmbI80NmzZ5GamopNmzahfPnymDJlCu7evYupU6fi1atX6N+/v/KbTZ06FfXq1dM7+2+1atXw+PFjrbHL6opcjRo10KFDB4MTqqmPhbElUX755RdERkZqzFMAAMuWLUPPnj3x+PFjjB8/HlOnTkX9+vV1VlAsLS3Rp08fdOjQQZnU5+DBg4iKisL06dPRtWtXmJubG5z9Vr3UTeYgOC4uDiVLltQ7WVpWl8kB0pc2MpTOr776ClWqVMGTJ0+Uc+jUqVPw9PTEjh07kDt3bvz555+IjIzE7NmzdS5x1bZtW5w4cQLz5s3TaGDs3LkzSpcujVmzZqFx48Y4ePCg3mAmPDwcAwYM0BpH+ObNG0yePFlvg6yx3/rixYvYtGkTXr16BTMzM50Ne0B6/lqhQgX0798fjRo1QsuWLfHXX39h2LBhmDNnDo4fP45z587h8OHDWrO6Z1xfWl3+68uHM6dXfX5Xq1ZNabAyplixYujatasy18fp06c15vpQj+e+e/cu5s6dqzRCHD9+HF26dIG3tzcqVKiAsLAwdO7cWeM3mz9/PoYPH46kpCRERERgyJAhGufNxIkT0a9fPwwfPlxnvhYTE4PGjRvj0aNHWg16Ga/loUOH4rvvvlMmedSVr40cORIHDhxApUqV9B6Lxo0bY8+ePciWLZvO+S3WrFmDGjVqYM2aNcpYdbUXL16gePHiaNeund7PHzlyJOLi4tChQwccOnRI4zX1bx4cHAwRwZIlS5Qxzk+ePEHr1q1hZmaG2bNnG6w75MyZEwcPHkRgYKBWI9Dbt29hbm5uMDDNSKVSaTTcfvXVV1leZk1dbuuTnJyMunXrGlwGMjExEX379sWiRYuUhh4LCwuEhoYiIiICLVu2ROnSpZXx/7p06tQJ+/btw9WrV+Ht7a1RZunbb7Zs2XDlyhW4ublp1QvV1CsXtGzZ0uD3BNLXyb537x5++eUXjTpQjx49dE6Wq4uxetKJEyewdOlSrUnhjhw5gubNm+PGjRsoVKgQFi1ahHLlyqFSpUr46quvMGTIEKxYsQI9e/bEw4cPlfc9evQItra2GrGOodnR1TcCPnUMtP8Fy5Ytw5w5czB37lxl9surV6+ia9eu6NKlCwIDA9G+fXvkzp1bWWMR+L819oytE5yVzGTZsmValTS1gQMHYvLkyXj9+jWWLFmiEcxkXoP16tWrOoPoKlWqGJ2BWh1Azp07F3nz5sWRI0fw9OlTZYbJypUrKzOfFyhQQOcER0OHDkW7du00ZirNKDU1FQ0bNsS2bdvg7u6O5s2bo1WrVsqszVmRlJSkVDxCQkJQsWJFDB48GPHx8ShUqBDevHmDESNGYO7cuejfvz+GDRuGH3/8ETdv3lRad3/55Rds2rRJmUhETb0OuaF9HzhwAA8fPsS2bdvQuXNnvRNCqJd4M7RcjmRaj9bf31/rDhKQnqGrZ4P97bffMGHChCxPnGXM06dPtYL1n376Cc2aNcOYMWOyFKz/U7rSkLHBQN8dI1OLjo7W+FtdkcufP7/OY5x5rczixYsjIiICNWrU0Nhu9+7d6N27N86ePQsvLy+DS29FR0ejYsWKOvenng20UaNGWLhwocakJampqdi1axd27NiB7t2748cff0S7du0wZ84ctG/fHteuXcOxY8fQvXt3REREICYmxujSc5llnK24Ro0a2Lt3L6KjoxEdHY24uDh4eXmhatWqqF69OnLlyoWhQ4di9OjROvMKJycnvHz5EkuWLNFYN7ply5YG73ZmpG+ZnLi4OBQrVsxo44upuLi4KL0lMrpy5QrKli2LZ8+eaU2+lJG6Mrt27VpMmTJFozF24MCBSmOtmZkZHjx4oDXD861btxAQEICGDRuiZMmSWhMN/fTTT4iNjcWSJUv0NkA+f/4cJ06cyNI5YSydr169wpIlSzQqlC1atFCClkePHuHbb7/Fvn37dPYiuH79OkJDQ7Fx40bltZSUFISEhGDBggXo0qUL9uzZYzCgMtYoMXLkyL8ViKtlpVHs8OHDePXqFZo0aYKrV6/iq6++wpUrV5Q7gjVq1EBAQAD8/f0xaNAgnd+lV69eaNKkSZYmSzLm9u3bAKDRAAqk9xw7f/48fH19kT17duzduxeBgYG4ePEiatSogfv37+PRo0cIDQ3F1q1bNX6T4OBgREVFwcPDA3/88QemT5+ucV707t0b3377LUQE06ZNw5QpU5TJvry8vDBw4ED06tXLYPn/4MEDXLp0CVWrVjX4/ZydnY1O8mhlZQVvb2+0aNECrVu31roTa2ypyAULFuht6Hz48CG8vb2N9nRT5+9DhgzRWRerUKECDh8+rNEbEYDGEqbG6g42Nja4ePGiwXznfajPHWPXxdGjR1GyZElYWloaLbenTp0Kd3d3g8tAqiUmJioBv5+fnxL8zZ49G2FhYWjVqpXOcibjHda7d+9i3759iI6Oxu7du3Ht2jXkzJlT6TWV0dKlS9G0aVNYW1vrnFAzo6xcm+reSJl/6zNnzsDJyQm+vr5Ge4zmypXLYD0pZ86cOn9z9XJrb9++xZAhQ+Dk5IQffvgBK1asQOvWreHr66tMmGbsJtr/Agba/4J8+fJh9erVWq1hJ0+eRNOmTVGoUCEUKVIES5Yswf3795XX1Wvsbdmyxej6vMYyExcXFyxbtkxrPeG+ffti+fLlGvvVRd0qfuvWLa0WdnUrU86cOTFp0iS9M/W6ublh9+7dKFasGJydnXH06FEUKlQIu3fvRv/+/XHy5EmDM9uqVCrkzJkTt27dwrRp0wzOyvzXX39h5cqVWLp0Kfbv348cOXIgKCgIQUFBcHNzM/hdZ86cierVq6N+/fqoU6cODh8+jOLFi+Pw4cP4+uuvcefOHeTLlw+RkZGoX7++xprDkZGROHz4MKKjo7Fz506tQPt9VK9eHWvXrtVq1VZzdnbGiRMnkC9fPo0g4NatWyhUqJDO5WWywtLSEnfu3IGnp6feyqQu6vVB1T0uTEm9pEnGpXg6dOigEQjqu0ZSU1ORPXt2BAcHw9PTU+NzS5UqhV27dsHV1VXnTPIZ14U0NAs8kF7gpqSkYO/evbh27ZoS0N27dw9OTk5wcHDIUterzDJ2Nz9w4AAGDRqEUaNGKUvNHT58GOHh4ZgwYQK+/PJLTJkyBdevXzfYNVFfo5m6F4iuLvKWlpbw9fXFlClTMGDAAIwcORItWrTQOPdGjBiBp0+f4tChQ5g5c2aWl8PL6OzZs2jQoIHWuspxcXEYO3YslixZgrS0NI30Ze7a+j4t34aGX9SuXRvjx49Hw4YNNb7nzz//jAULFuhcXUAXQ0tJZUXPnj1haWmptUTMgAED8ObNG8yYMSNLn6OPuvv79OnTtRr3jh8/rgw56NChA3766SdUrFhRZ3fQpKQkvQ2QtWvXxosXL/QuRwikB1bjxo1Dhw4d9OYj+hr/UlJScOjQIVSpUgW1atUy2qMCSG/wzhi0qe/U29vbY9u2bQbvTOprlNi9ezeaNWuGp0+fZnltXH3rFRtqFNPn6dOnGnfI7O3tlfXQddEVSGQc0qOrZ0xGdnZ2CAsLQ2RkpNKjwcHBAT179sTIkSNhaWmJXLly4c8//0RgYCCKFSuGoUOHokWLFoiJiUHdunU18tkrV67g4sWLUKlUKFy4cJbvjGak7v6auUEtPDwclSpV0mqofPXqFaZMmaIR5OlqNAgICMDy5cuVHjK6PH78GMuXL8eyZcsQExODYsWKoVWrVmjRooXRslEdCJUoUQK7d+/WuE5SU1OxdetW/Prrr7h586bBNa7t7e1x/PhxvT0OsmXLhk2bNmndmTx48CAaNGiQpTXejfUoyihz70W1lJQUo+dOZhnrRdWqVdNbxqkb6bPaS1Kf97nD+vr1a2VFjgkTJsDS0hJFihTB6dOndabzfcsqQ/ULZ2dnnfmNOv1paWlKjzp9w9+MpaNAgQIYOXKksgqS2uLFizFy5EidQ8piYmIQExODAgUKoEGDBjqXtc3oc7hjbQwD7X+BnZ0d9u3bp7W+57Fjx1C1alXY2Njgjz/+QKNGjTS63l26dAkVK1bEzp07Da7Pe/36daOZyebNm9GqVSts2rRJqTx89dVXOHHiBHbv3q3c8dFnxIgRKFiwIMLCwnS2kDo7OyN79uw4evSoctc+M1dXV5w4cQJ58+ZFvnz5MHfuXFSvXh3Xrl1DYGAgXr58qbc7klrOnDmxfv16lC1bFk5OToiNjUXBggWxYcMGTJo0CQcOHNB6z507d7Bs2TLMnz8fcXFxOseBZbR37140btwYL168QGhoqLKkxw8//IBLly5hzZo1sLe3x8WLF+Hj44OcOXNi8+bNytjVkiVLYvDgwbhy5Qrmzp2rUUnStbyNLsnJyShbtqzOu+JqHh4e2LZtG0qWLKkRBOzYsQMdOnTA7du3dXbl37VrFypXrozff/9d593k7du3w9vbG56enjhw4ADWrl2r9/eoVKmS0sihPncdHR3Rv39//PjjjxqZvKHeEIbExsYiODgYtra2SpfBY8eO4c2bN8pcASdPnjR4jSQkJODixYta3UjDwsIwcOBApbKYmXptaiC9IMpcSGW8Dq5fv466desiPj4e7969w5UrV+Dn54fevXvj3bt3mD17Nnx9fbW6XvXr1w8hISFo3769zrHTERERyn50DbmQDOuvq9+vrqTp6po4aNAgo41mefPmxbFjx/Q2StnZ2SnH08PDAzt27EDx4sURFxeH8uXLY9myZQgLC8PYsWN1dmM21EvnwIEDSjfSAwcOYO/evdi7dy9OnjyJwoULo1q1aqhWrZreBii1qlWrGl1X+syZMwaHX1SpUgWjRo3ClClT0LFjR8ydOxfXrl3D+PHjMXfuXDRv3txoxeHy5cta4yEvX76sjIfMly+fzqEoGYOdLl26ICoqCj4+PkrjxZEjRxAfH4+2bdvi559/1tinvootoHvcpnpcdXR0NIKCgjS6bqqXqXN2djYY8KmvD30NkOol1ZKTk3UuR6hutHBwcDDYldnYneTU1FTY2dkZ7FFhrNv30qVL8ccff+gMqNRB7PPnz7XWeU1NTUViYiK+++47zJo1y2Ag/ujRIzx8+NDgesVff/01OnbsiG+++UZrrHhWu+M3aNAA7dq1Q9OmTXW+riuQyHgO6ws01EFCly5dsGbNGoSHh2vMHzBq1Cg0atQIs2bNyvLQncz7z+r5C6TP8WGsodPMzAyWlpYYP368xtwKDx48gJeXF969e2cw8Nu5c6fBIQmZ3bhxA0uXLsWyZctw6dIlVKlSxWAPrYxDi3RVxW1tbTF27Fhs2LDB4BrXX3zxBSIiIvQ2FOkaOtG/f39s27YNX3zxhd75A9R69eqFrVu3Gu1RtGjRIkyePBlxcXEAgIIFC2LgwIHKzZhu3boZPXeA9Dv56jkHUlJSULVqVYMBcEa6eklu3LgRderUgbW1tVaDbmbGuqcD6XVDdRnl7++PqlWrws3NDV26dIG7uzsWLlyI3LlzazWspqWlIT4+HqGhoUhNTcXatWs11oBv2LChkufeunXLYP1izpw5OvObgwcPonbt2nj9+jVu3bpl8HvkyZPHYF3t8OHDmDRpEiZPnqw0Vu3atQuDBg1C//79s7RWd7169RAfH48ePXrojCUaNmxo9Mbip46Tof0Lqlevjq5du2Lu3LlKq+PJkyfRrVs31KhRA3v27NHZHSM5ORlv3rzJ0vq8KSkpBteUnTp1KmbOnImQkBDs2LED8+bNw+bNm3Ho0CEULlxY50QTaupJ21atWqW3VRxIH6eydOlSDB8+XOfrRYsWVcZmlStXDpMmTYKVlRXmzJkDPz+/LE1w9OrVK6WC5erqikePHqFgwYIIDAzUeXcpOTkZsbGxOHLkCG7evAlPT0/Ex8fr/Q4AlLGXGcfqAumVXXXFLFeuXLh//z58fHyQL18+JeA7duwYrK2tcezYMezatQvbt29HYGCg8nuouwhlpZBQr/GpT0hICMLDw/HHH38ASP+d4uPjMXjwYDRt2tRgV/5nz54BgM71DMuWLYuYmBhcuXLF6MRZgwYNwrx58zBhwgSN9ddHjRqFt2/fYuzYsTp7Q2SsROj7fLVHjx4hJCQEv/32m1LopKSkoFOnTujTpw/27dtn9BqJjIzUuS6pq6ur8lu0b98euXLl0vhtRo4cqfx/586dGDx4MMaNG6dRKRg2bBjGjRuH3r17o0yZMjh9+rTGJGKNGzdG586dAQAJCQnImTOnRhpOnjyJtm3b4v79+zrXy8zYE2batGk6j5G6gU39exo6pt999x3KlCmDzZs36yzoAN1rcWeUI0cOPH36FHny5IGPj4/S6+PGjRsQEdStWxcAUKNGDb13m41NPOTi4gJXV1e0atUKQ4YMQeXKld+ri7+xdaVHjBiBfv36oV27dsrwC7Uvv/wSLVu2xKJFi2Bra4thw4bh9evXaNmyJby8vDB9+nQ0b94cANCuXTvEx8dj+PDhOo9nr169kC9fPhw+fFhrPGSvXr2wefNmhIWFad0JnjlzJh49egRfX1+cPXsWpUuXBgBliIebmxvc3Nxw/vx5ZV+GKrbGxm2mpqaiffv2mD59utHhSvrY29vrnQhn4MCB+OGHH4x+Rs2aNREdHa030FanN7MnT54o+WzhwoUNdusPCwvDd999pxVov379GmFhYdiwYQMGDRqkNI5lNG3aNIgIOnTogLCwMI081MrKCl27dsWyZcugUqlQsGBBvYE4YHy94pIlSyqTon777bfo2LGj0tBiaWkJHx8fo3eAGjRogL59++Ls2bM6G70yV6YB7SEuhoSEhGD58uUaveWKFSuG3Llzo0WLFpg1axZ++eUXpYfVjz/+CEtLSxw6dAhNmzbFsGHDlPf9k/M3c0Nn7dq14ejoiIkTJyoNnep9dO/eHWfPnsWvv/6q0ajUs2dPrFmzBpMmTdIK/J48eYLw8HCjkzxmlDdvXgwZMgTz58+Hv78/oqOjdfacUgsICMCmTZvg5+eHo0ePagRNVlZW8PDwQMuWLY2ucT1x4kQMGjQI48aN0/mbR0ZGIjQ0FEFBQcpr6gnSpk+frncsMpCed/bq1Uvp6RASEqIzj588eTKGDx+OHj16aNQNvvvuOzx+/Bh9+/bF0qVLDZ47EydORPfu3bF8+XLlPBcRNG3aFPPmzUPJkiV1TtqZ0blz55SGA/UNpcTERJw5cwaWlpZZajBRe/v2rc6JHidMmAB3d3eMHDkSTZo00eqF0aFDB72Ng7Vq1UKZMmUQEhKChIQEpTF24sSJcHd3x8aNG1G0aFG99Yt79+5hx44dUKlUGD58uEaelpqaiiNHjihxh7HvaqznakpKCp48eYLvv/9eCYBtbGwwePBgjSDbUAP3gQMHsH//fr3nmLGbJp9DoM072v+ChIQEtGnTBrt27dIYe1SzZk0sXrwYzZs3h4uLC7p164Y6deoo7+vevTvOnDmDs2fP4siRIyhUqBBcXFwQExMDf39/HDlyBKGhobh06ZLRLtfqVtSZM2eiX79+cHd3x549ewwGzhnVqFEDgwYNUirQahlbhdPS0rBw4UIUK1ZM5wzUwcHBRseTGeuO9MUXX2DMmDEIDg5GSEgIXFxcMH78eERGRirdPwFgz549WLp0KVavXo20tDQ0adIErVq1Qo0aNWBubm5wvFZWuq4YG4fy4MEDne9LSEhQ3n/z5k0MGTIE7dq10yjUFy5ciPHjx+Pu3bs674qrPX/+HF9//TViY2Px8uVLeHl5ISEhAUFBQdiyZQvy589vsCu/MVkZI+jv769MnJLR+vXr8f333+Pu3bsoUaKEVm+I77//XtnW2MQny5cvV+5mZnThwgWUKVMGr1+/houLi8FrJDw8HEOHDkXfvn01GqJKlSqFHTt2oHr16ka7yRctWhSzZ8/Wukuwf/9+dOnSBQ8fPsShQ4dQqFAhjR4GN2/eREBAAF6/fv23ul79Hd9//z3Cw8N13pHW15U0MjISXbp0gY2Njd6VEtTOnDmD3LlzY+TIkZgxYwYGDhyIihUrIjY2Fk2aNEHbtm0Nvr9q1apaDYuZJx5q06YNDhw4ACsrK+UudrVq1TQqMfv378evv/6K69evY+XKlfD29sbixYuRN29etGrVCt9//73O7vhq7zP8IvPsv2qOjo4GKw729vZGx0MaG4qydOlSg8cTSG9M1VWxnTFjBsaMGYNVq1YZHLeZ+e6vuutsREQERo8eDXt7e60Z1jNSqVTYtGlTlifC0UffmEj17NZHjx5F3bp1YW1trbwnNTUVZ86cQaFChbB161Zs377dYI8KFxcXg3ebU1JS8Pr1a42JKjN6+vSp3m7dCxcuVALxadOmaQXivr6+Sn7v7OyMnTt3KrMdqx09ehR16tTBs2fPkJKSgg0bNmDhwoX4888/kT9/fnTo0AFt2rTBpk2bsGbNGoPd8f/tyYU8PDwQHR2t1fPq4sWLqFKlCh49eoQXL17obby5evUq8ufP/4/P35EjR8LR0RHz5s1D9uzZlWt579696Ny5M+Li4pSxzy9fvkSDBg3g4uKCdevWQUTg5eUFBwcHrcAPALZs2YIWLVrgiy++yNKQBCD9TuKSJUuwatUqPH/+XBkHf+TIEYPHM2Pjri5ZOWfUv7muiTcz/ub6hk5khbHGmHbt2iEsLEyrLFi4cCFGjRqFGzduGD13atSogZMnT+Lnn39WrplcuXLB1dUVQUFB+OOPP3Rex6aUmpqKcePGYfbs2Xjw4IFyJ3n48OHw9fVFx44dcfr0aURHR2Pv3r3Yv38/rKyslAnR1JMGGpr7olixYsqd78yriTx69AiHDh1C9uzZddYvgoKCcPToUYiIVm8k9f8bN26szB5vSFZ6rgLpdcOLFy/C1tYWBQoU0MiLjTVwv337FkuWLNGaDFVNXcarb5qcPn1a46bJ+8zE/tEyySJhpNPFixeVNTXV612KGF9j7++uz9u3b1+dj1y5cklISIjGc8asWbNGAgICZMGCBRIbG6usHVymTBkpU6aMVKtWzeCjevXqOj/3yZMnkpaWpvz9559/SokSJWTjxo1y7949ef78ucZj8eLFsmDBAhFJX1/Tzc1NVCqVWFtby/Lly0VExMvLS2xsbKRRo0aycuVKefv2rcY+T506pfE4duyYzJkzRwoXLiyrV6+WhIQEad26teTMmVPMzc211hzUJSYmRqZMmSIbNmwweizVatSooaxLndGSJUukatWq0qhRI3F0dJScOXNKnTp1dK49KpK+xueMGTNk4sSJGuulZsuWTWNdaV1ev36tsR7nzZs3JSIiQllDde/evZKcnKz3/dbW1nL58mUZP368xprfly5dUtYqtrOz07kublZ5eHjoXNN169at4uHhISJi9BrJvIamet1IAKJSqeTmzZuiUqnk+PHjcuvWLZ0PGxsbZa3qjE6fPi02Njbi4uIi58+fF5H09SPVaxrv379fSefEiRMle/bsMn/+fGX9+Hnz5kn27Nll3Lhx0r59e3nx4oXWPhITE6V9+/ZZPmaOjo5y5MgR2bdvn+zbt09jLd7q1atrrEGv5uvrK48fP1b+r++RN29eSU1N1Tgvli1bJj179pTIyEh59+6diKSvzduqVSspX7683LlzR0TS16Tev39/lr+HSPrxjYyMlKZNm4qHh4d4eXlJy5YtZdWqVWJrayudOnUSa2tr5Xj//PPPUq9ePaPrSouIuLu7K2v3ZvzNtm/fLrly5cpS+vz9/XWu/6vm6uoqBw8e1Hr+wIED4urqKiLp18itW7dERCRHjhxy/PhxERG5du2aODk5ab33+fPnsnbtWo21nn19fWXhwoVa20ZFRYmvr6/Y2dkZXRs6OTlZhg0bJk5OTkp+Z25uLv3795ekpCSjefzgwYNl7NixIiKyfPlysbCwkPz584uVlZU4Ozsr51dGf/31l+TNm1f5W9e1qlKplPVgVSqVNGvWTNq1a6c8unTpIuPGjZNHjx5pfEbmvFv9GWZmZuLi4iKurq7KQ/2dv//+e4mKijL4EBE5fvy4xlrc69atk4YNG8rQoUPl3bt3snfvXklKSjJ4vLOyXnFGDx48kNGjR4uNjY1YWlqKs7Oz2NrairW1tRQsWFBKliyp8ciqxMRE2bx5s8yaNUumT58ugwYNkoiICJk+fbpS1ut7hIWFSYsWLTTK2bdv30qrVq1k1KhRIpK+XnLmclgkvZzw9vYWkX9+/mbLlk2pV2W8lm/cuCG2trYiorku+fPnzyU4OFhy5colmzZtEjMzM3F3d5cLFy5offaFCxfEzc1NbG1t5dSpUwaP5ZAhQ8TX11esrKykfv36snTp0vde8zoqKkpZi1hEZODAgeLs7CxBQUFib29v9JxRr0+s75FZSkqKnDx5Up4+far1WlpamkYdLausra11lvtXrlxR1jU3du7Y2dlplRedO3cWKysr5fr28fGRvHnz6nxkhbE6UFhYmPj5+cnvv/8utra2ynm1fPlyKV++vM7PPHXqlISGhir5lpmZmXTt2lWjzt2rVy8pV66cVKhQQWxsbOTcuXNan3P27FmlHmWsftGuXTt5/vy51mfoy0911Yf+aV1NRMTHx0esrKz0lr3btm2TOnXqKOt1Z+bs7Kxcx87Ozsr1ePjwYSlUqNA/StvHgoH2v0xfpnXy5Elp2bKlBAQESOnSpaV9+/ZK4FC7dm1ZsmSJiIh06tRJypYtK7///rsEBwdL2bJl9e7LWPCbP39+yZ8/vxQoUECmT59u8KHvwlT/ayqZP1/90LWftLQ0efXqlRw/flypYImIzJkzRyPoy6pNmzZJ1apVpW7duhIQECAzZ86UtWvXyrp16zQeSUlJ0r59e7l+/fo/+q62trbKb5zR5cuXxdbWVqMiqethzKBBgyQ8PNzgNrVr15ZZs2aJSHqF18PDQ3LlyiU2NjYyc+ZMo/soW7as9OzZUyuo6dGjh5QrV05E9Ad2GSUnJ8uOHTtk9uzZSqB59+5defnypfTs2VNy5coly5cvl/j4eImPj5dly5ZJrly5pHfv3sr3MHSNqIPazI9x48aJlZWVVoVc17lXuXJlqV27tiQkJCjpTkhIkDp16kiVKlXk22+/lc6dO4tIekF4/fp1efnypdSoUUP5vdLS0mTQoEFiY2OjfL6dnZ2EhYWJiGZFMKNHjx6Jubm50d9DJL0CaWHx/9j78nCq1v79e5vnmUgyREJEaZ5pUKJ5oqikGamUToM0aDjRPCgaaFBp0JwGaRZRKglNVJpTBw2Gz+8P373eve2199qq877nnN+5r2tdF3uv/azpWc/zfKb7liM5OTnmXZKTkyNvb28qKSkR6zTjb78CXEZwXVBdXU23bt2ilStXkru7O8nJyZGsrCw5OjoyC3PBhUdmZibVq1ePxowZw/RtcfDz86N+/frR9+/fmWdmZ2dH9vb2FBQURI6OjiLGS21DhmvhMHLkSLKzs6MbN24w4//169epadOm5OvrS0REjRs3phs3bhARUfv27Wnp0qVEVLOY09fXp8GDB9O6deuIqGZhaGVlRfLy8iQnJ0eJiYlExL2wdXZ25nRyTJgwgQwMDGjz5s1Mf9i8eTMZGhrShAkTJP6WDYIOSB6Px9q3X716RfLy8lK3uWDBAiotLZW4jzgDIzQ0lGbNmkU8Ho/WrFkjZDzv2bOHrl27JvV5ODs7M/f+0aNHpKioSMOHDydLS0tmXKqsrKTExERatGgRLVq0iA4dOkSVlZVMG56entSpUyd68eIF89nz588ZR6sg0tLSaMKECaSlpUUNGzak+fPnk5OTE8nJyVHbtm0ZJ73gJg0yMzPJ0NCQNDQ0SFZWlvT19QkAqaiokLm5udBcz7YG4DuE9fT0yNXVlVxdXUlPT480NDQYp3C9evWoXr16Qs65nJwcMjQ0pMDAQCL6+f4rjaOzdh+sqqqigIAAkpOTIxkZGU7Dz8nJia5fvy7xfrZr1442bNggtCZhw7dv36ioqEjEoUtUMx6cP3+eiIiuXbtGysrKFB0dTR4eHmRoaCh1nxGHoKAgiomJIaKaPtq+fXvi8XikqqpKKSkpREQUExNDdnZ2pKCgQAoKCmRnZ0dbt24Vaufjx4+0cuVK8vPzIz8/P4qKiqKSkhIiIrKzs2OcboKwsbER6huS+o6JiYmQM4uPjRs3kqamJvF4PFq0aBGtXr2adSMixgkobuNaAzVq1IjOnTtHRML96sGDB6SlpUVE/5mjIiMjycPDg7S1tUlWVpbU1NSoQYMGxOPxqF27dkLr7x49etC4ceMoLy+PHBwcmOctiPPnz1PTpk2JiKRaX/wspFmrcUFdXZ1UVFTEGtpaWlrMmktNTU3I2amtrf3DgcW/E/5NHf+TwEUKIQn81GBJ+rz8/cTp2rIROtRO3eRrfQsSbPDrIlJSUiSeo6mpKT59+oSqqiqRFDZpZcoA7nSkzp07IzY2FqtWrWLupZWVFaZOnYqxY8dyti8JBQUFjNaxpFRQoCaF5vbt22KlLczNzSWmpz9+/BjW1tbo27cvVqxYIfTdzJkzkZSUxJB/SEJ6errYWpiqqirExcWJTeWPioqCnp4eUlNTYWdnh5iYGKxbtw5ZWVk4ePAg5s+fz6SViUNqairc3d1RXl6OQYMGQV1dHdevX8fTp0+xceNGNG/eHI8ePcLcuXMREhLCmsapqakpkeRj7dq1CAkJwebNmxkiO3l5eUycOBHLli2DoqIi5zty8uRJsWzfz58/x4ABA+Dg4IBz586JrfdSVVVF//79kZeXJ6QtamVlhSNHjkBJSYnRJ83Pz4ezszPy8/Ohp6eHS5cuCaUc1069+vbtG4gI2trayM/PF0oxq6qqwrFjxxAaGsrI1kjC0KFDceDAAWzbto1Js7p+/TqCgoLg6OjI1PQLgk+yVle27ps3b7L2vVWrViE4OBg+Pj5CaW4uLi64d+8e3rx5w5kC1qFDB1y8eBFXrlzB58+f4ejoiE6dOqFLly7o2LEjjI2NkZOTAzMzM6Fj8KVGwsLCJOpKBwYGspZfvHjxAm3btsWZM2ewcuVKiecYFhYGbW1tiWnGkqSkduzYAU1NTc5SlB07duDMmTNo1qwZ9uzZg7CwMNy5cwc7d+7Eli1bkJWVhaZNm8LLy0uoDvrz589YsWIFDh06hPXr1zN8AuII6jQ1NSWmzgqyQ7Ph3r17IsRc0krGSTPe8cFFesWF1NRUtGvXjpXVuDa+fv0qMp/y7xW/7GD58uW4cOECzpw5g6tXr2LYsGFISUnhJMErKiqSqFesoKCA+Ph4bN++Hfn5+fDw8MDYsWPRs2dPZn65cuUK3NzcxGqY86935cqVQkRLISEh6NixI2uq5uvXrzFr1ixMnTqV4QYQB0HCSHGorKxEcnIyunTpgoSEBNy/fx+urq7w9vZmmPTZ+i8ALF68GPv27cOaNWsk9l9/f39oampiy5YtUFdXR3Z2NvT19dG3b180bNgQ27dvx86dOzFs2DChVFegRlLr0qVLKCkpwfnz56GoqMisq+7cuYPv37/D1dWVkQGztbXFkSNH6kTyyEdeXh78/PwkciWoqKgwso6zZs1CcXEx4uLicP/+fXTs2BGmpqZi+wyf2VySUkeDBg1w5MgRODs748iRI5g0aRIuXryI+Ph4XLhwAa6uroiKikJAQIBQWdv69esRHByMhQsXcpKUPnnyBEOHDkW3bt2YUoCrV68y/UCa2ui2bdviwIEDiI+PZzhrXr16BV9fXwwYMAA3btzA2rVrJco1BgcHC/1fUVGB27dv4969e/D19cXu3bslroGePn3KKvGYk5ODVq1aobS0FNra2igtLUWzZs2YlPGOHTsya2k27ou6qokUFRXBzc1N4vqirjYAACHJL/5a7dOnT4iOjhaR6ZPEts+Hn58fdu/ejZycHFhYWIh8zyVxu3v3bowaNQpeXl7w9/dHdnY2AgMDER8fj48fP3KWXvwd8K+h/SeAq/ZIcCAQN6lzISEhAT4+PujZsyeSk5PRo0cP5OXl4fXr1+jfvz+2b98u8fd79uzBxo0bERsbK7Qo8Pf3x/jx4+Ht7c15Dr169YKHh4dQ/S0gLFP2s5g/fz7nBABIdjrs2LFD6H/6PxKmBQsWIDc3F9+/f5dYQwLU1GI5OjqKDOJ8rFmzRuj/iooKZGVl4fTp0wgJCUFoaChOnjyJgQMHwtLSEq1btwZQU2eVn5+PgwcPonfv3hIXlGvXrsXcuXNhbW0tUi8mTsJB8PsLFy4ITehDhgyBnZ0dwsLCUFRUBGtrayHmY3F4+fIlzMzM4OLiAlVVVdjY2CAiIkLiOQgadh4eHpy1dUBNjSy/Br9Ro0Y4d+4cevXqJdVimY3tG6hhbh42bBiePHkidhEmCCLC2bNnRbRF+fe+srISCQkJyM7ORmlpKZo3by6kRS8Ogmyz4u5XeHg45syZw3mtqqqqICLcu3dPaKK7fPky3NzcWDU9BWFqairikKiN/v37w9vbm6njZ6vDYjOCBw0ahGPHjuHbt2+cerL37t0TWrTUJu6zsLDAli1b0K1bN6FjxMXFYdmyZRLJsPjkSXxcuXKFeWbPnj1DZGQklJSUUFhYKEKQVxtcCwd+3WZ+fr5Qv5FUD1lbEkVZWZlx8Pj4+KB+/fpYtmwZCgsLYWtri9LSUhw8eFBkYcuXK+KfP7EQiQku8KWpt5UEY2NjXLlyRcgBKenYgpJxffr0YT6XxDrbt29fTnZ/PsrLy1nbcHBwkMjyW1ZWhlmzZmH//v14//69yHVWVVVBQ0MDt27dgpWVFbp3744+ffogKCgIhYWFsLa2RteuXUFE2L17twgJnoyMDE6cOMHcF3F6xQoKCmjUqBHGjBmDUaNGsdaifv78GS4uLpg6dSoAUamnXbt2YfTo0RgwYICQwXP48GHs2LEDkyZNkshvsWXLFk45NWlQUlKCLl26wMrKCpcuXYKPjw9+//135nu2/nv16lWcP38e+/fvZ1jTxfXfZ8+eSe3oFAeuMYm/duAz7PNRXV0NHo+HI0eOcB5j+fLlnFwJgooiTk5OmDZtGkaOHIlHjx6hWbNm+OOPPyRqXHMZwe3atUNBQQEaNGjAkLyuXr0aT548QbNmzaCoqIi1a9di+PDhQue2d+9eBAQE4N27d+jYsSMsLS1ZSUofP36MS5cu4datW1i1apVQHfj06dMlrq0E4eTkhIKCAnz9+hUNGzZkCF8VFRVhaWmJ27dvo0mTJlBWVpZaapGPBQsWoLS0FBs3bpS4BrKxsUFwcDBGjBghNM8sXLgQZ8+exeXLl3HixAl07NixTiSSted8/lqpNuu8oPO7srIS+/btw507d0TWFz9qA7BJfgmO1dI44QU5XcrKyvDbb79hwIABrM5MLjIzaQOLf2f8a2j/CTA3N5dICnH//n3MnDlT4qTO5cF3cHDA+PHjMXnyZGYwmDFjBjQ0NGBqaoq7d+9KPMc7d+4gMTFRZAC8desWBg0axLAQ5+TksC5cPD09oaOjg6tXr4os0vgyZWzXJg7iFkiurq6cEwDXgLNz507WydrExAQJCQn4448/EBkZiejoaLHMt3xJK1dXV1aWd3GDyYYNG5CRkcEMekVFRdi0aZPQhDlhwgSYmJhwyjkcPnwYy5cvx6hRo6S9rSJwcHDA2LFj0b9/fzRt2hSnT59G27ZtcevWLbi7uzPkbVwQnIAAcMpICKJ58+acJGJ8CGqbysrK4tWrV9DX1+ckMlNSUmJl9udHP79+/QpfX1/4+flJvXD8lUhNTQURwcXFBQcPHhTKClFQUICpqalUZCZAjczNu3fvRAzt7Oxs9O7dm9E7l4TarOUVFRW4d+8eSkpKmKh07969ERERIcLcDHAbwVzGPh+XL1/G5s2b8eTJExGys8uXL2PXrl3Ytm0bunfvjpMnT+LZs2cIDg7GvHnzEBAQINUxakNOTg4vX76EgYFBnXTk/0w0btwYixcvhru7O8zNzZGQkAAXFxfcuXMHrq6uePfuHQCILGx1dHQwdOhQWFlZcR6jc+fOWLhwIXJzc7F9+3bG4fTt2zf4+fkxRH6SEBYWhl27duHq1atMBGrfvn0YM2YMVFVVkZOTI1Yyjg8u1lkHBwdOx9zbt28xevRonDp1ivUY2dnZIiy/eXl5DMvvpk2bkJKSgkWLFmHkyJHYsGEDXrx4gejoaCxbtowh1jQxMUG3bt3g5+eHnJwcWFpaIjU1Fb6+vnj79i0nCR4XLl++jI4dO4r9nkseTF9fHzY2Nhg3bpyIUzgqKgpbt27Fu3fvmIVs48aNsW7dOvTs2RO5ublo0aIFvn79KpUe+Nu3b5msBGtra1aHZXFxMeOUEJQi5RsokgwzabLd2Bydnz59wuTJkzlJHnk8HueYIe4cJJHR1j6GkpKSRI1rAPD29kZubi6cnJywd+9eFBYWQldXF0ePHsVvv/2Ge/fuSTwOlxH87NkzbN26Fa6urjA3N8emTZvg7u6O+/fvo0OHDiAipKeni4wbeXl5aNWqFUpKSqCsrMxJUvqz4EtuRkREYOLEiSKqE6tXr8awYcNgaGjIOTbVRkFBAVq1aoUGDRpIXANFR0fD19cXs2fPxsKFCxEeHo6HDx8iLi4Ox48fR/fu3YXa5c+vXJrpdWH2b9euHZo0aSJR7pXNBjA3N8f48eNhZGTEKl8KsK/VbG1tcfr0aTRs2FDoc3FZCLXXVc+ePYOxsbGIc47v4H706BG2b9+OR48eYc2aNTAwMMCpU6fQsGFD2NnZib0P/xj8+dnp//+Bq/Zo0qRJZGNjw9Q1btu2jRYtWkQNGjSgXbt20dOnT6lJkyakoqJCsrKyTO1DYGAgjR8/nohqyHT4NYI6OjqUnZ1No0aNovT0dDI0NOSs91VWVqabN2+KnGNaWhpDAOHg4CBSryVIEKaiosJaT5Odnc2QkXDhzZs35O7uLrZeVlNTU2xds6amJhER2dvb0/r164noPzU11dXV5O/vT/Pnz6eUlBShur1Lly7RgwcPmPoxrhoSIm6yKHF49OgRK8kNG/r27UsjRoygb9++CdUGpaSkkKWlJRkaGrLei7rgwIEDJC8vTzIyMtS9e3fm84iICHJzc5OqjY8fP5KSkhJFRkbSzp07hTZ+W7GxsSK/i42NpWXLlnHW1rERNGloaJCqqiodOnSIiGpq7968eSP2HC0tLSk+Pl7k87i4OOZ59e3bl+Tl5cnS0pKWLFnCEHgJ4uLFi9SnTx9q1KgRNWrUiDw8POjSpUvM93l5eRQdHU2LFi2i8PBwoU0aPH36lKqqqqTaVxyio6NJVlaWqfklIiouLqYePXrQ5s2biYiooKCApkyZwtTFBQQEcBLnVVVV0bhx42j58uUSa7CIap65ra0t3bhxg9TV1eny5cu0a9cu0tfXp7Vr10p1HWx13nfu3KE1a9Ywdd6LFy8mVVVVZjxSUlKiuXPnStU+H+fOnaPZs2eTn58fjR49mlRVValt27Y0ePBgToI8PgoKCmjOnDk0bNgwpgb05MmTdO/ePaqsrKSYmBgaPnw4ubq6MrWB9vb21LlzZyIihiRT3LZhwwaSk5MjLS0tatasGdNH1q5dS126dKnT9UqCNPW2tckYa2PKlClkZ2dH79+/p927d5OysjJTyywNVFRUaPjw4VRVVcWMB4WFhdSpUyc6ePCgVKRXXl5e1L59e0pPTydVVVVKTk6m+Ph4sra2puPHj1ObNm3Iw8NDiPzpw4cP5OnpSW3btiUTExOmVlVdXZ2Zv+Pi4pi+d+fOHWratClpaGgI1UNPmTKFhg8fLpYELygoiJSVlTl5UdasWcN5r4YMGULOzs5CBF73798nZ2dnGjZsGBERKSgosK4/8vPzSVFRkZPfQtzY+vDhQ1JXV2eIGmVlZYU4IfB/JFBsnBd/FscLG+pC8jh//nx6+vTpn3o+0nAlfPz4kSZPnkyenp5CNbPz58+nxYsXi4xZghsRkZKSEitx3P3790lZWZnCwsJIU1OTmjRpQg0bNmRq0mNjY6lNmzY0ZcoUVpLc6dOn06RJk4hIOpJSLo4CaSH4ngsiJiaGevfuTe/fv69zm3FxcWRkZCTVGujSpUvUrVs30tfXJ2VlZWrfvr3QtVdVVVF4eLjQGkVTU5MWLlz40/M5H/Xr12cl6uODzQYg+g8XQl0g7n5LC0lEpBcvXiRlZWXq1q2bEGna0qVLaeDAgf+Vd/B/jX8j2n8CuGqPPn36hLi4OHTp0gUaGhrIzMyEpaUl4uPjsXfvXigoKHB68Bs0aIBTp07B3t4eDg4OmD17NoYPH47r16/Dzc2Ns7bOw8MDL168QExMDKM5eOvWLYwbNw7GxsYgIsjKyiImJgbm5ua4efMm3r9/j+nTp2PlypXo2LEjunbtiqZNm2LdunVCbfNlyi5fvsx5r7y9vfHs2TOsXr0aXbp0weHDh/H69Wsmgnz69GnIy8szdV18zJgxA1++fMGGDRugqqqK+/fvw8zMDLq6urh48SLs7e3x4MEDuLi4oLi4WOI5SJsKygfVSvmRhODgYOzbtw+nT5/m3Ldr164SI70LFizAy5cvxWorl5WVYdmyZTh//jxrHS0/dfbVq1coLi5m6tOBmhR2DQ0Nxlv9/Plz1K9fXySF9tixY/D29sYff/wBdXV1yMrKMt/xeDx8+PCBM227VatWEmvrlJSUcOjQISxcuFCoXGD69OkoKyuTSpN86dKlWLFiBX7//Xe4uLgAAM6fP4+ZM2di+vTpjAbk27dvER8fj507dyInJ4eJVvXt2xf79u0TScG8cuUKjhw5gh07dqCsrEyirEVdUtvYMjoqKytx/PhxjBkzRqK33MnJidFW5nuk+el2VlZW+Pz5Mx4/fgxlZWWMHz8eQE2K5p07d3Ds2DERD70gHj58iC5duqBt27YYNmwYhgwZwrofESEiIgJLly5lIhuKioqYMWMGFi1aBAB4/fo1ZsyYwfTP2lOPg4ODSJ23lZUVkpOT4e3tDRUVFaSnp0NdXR0FBQUoLS1FXFwcli9fzilFBdRE9Ni05p8+fYrs7GxUV1eL7VskkEqXmpqKXr16oX379rh06RIePHgACwsLLFu2DBkZGTA0NMSOHTvg7u4ulCq6evVqjBs3DtHR0VJJMN26dQuFhYXo3r07U4d84sQJaGlpMf2xuroaBQUFrO97p06dJMqhdejQgTN1VhCSypG8vb2Rnp6OFy9eYM+ePejbt6/U7fJ4PJw9exbdunVjTWV+/fo1rl69CltbW6Fx8cqVKxg4cCBev34NIyMjJCUloVWrVtDQ0EBGRgYaN26Mo0ePYsWKFbh16xYyMjJEoif37t1Dy5YtISsri5ycHDRs2BANGjTAoUOH0KpVKzx58gT29vYSo9Ffv36FrKws/Pz8kJmZidjYWCZ9Ny0tDZ06dYKioiIT2ZfEjfL48WMkJiaKLYN69OgRp9STpaUlQkJCmHedj82bNyMyMhJ79+5lTdWUkZFBixYtcPHiRYlyaqampjh37hzWr18vNC76+/vD2dmZ8z0EaqLRgjWrguDxeFBUVGSkisRluz19+lTs75WUlGBpaSkUeWObtx0dHZmSFT8/PwwcOJA1Mi+pJIENgteWkZHByZUgCWxjliAOHz6MevXqIT4+XkguFgDOnDkDHx8fvH79GomJiSgqKsLgwYOZ+WTnzp3Q0tLCuXPnEBcXBxMTE6ZmOC0tDYWFhfDx8YG8vDxSUlJQUFCAmJgYZm6/evUqQkJCMHDgQEyZMgXu7u54/vy5WI4CaVE7Y44Pfmp5RUUFTE1NRTILMzMzRbhA6P9KBTMyMjBv3jyEhYX91BoIAGbPno3Y2FiEh4cLvQMLFiyAv78/lixZwnmNkmrqgZqoviS515+1AYCadVK9evUQFBQkdL+3bduGt2/fSpTKFIS45wXU1N0PHjwY06ZNE9rv5s2bGDBgAPT09KR6B//OEH16/+KnER4ejqFDh+LSpUustUcjR45kOqSGhgY+fPgAoIYMaOLEiVBUVMS1a9dE9IbNzMzw4sULADWLqLNnz8Le3h6DBw9GUFAQLly4gLNnzwppUr9580YovYufDrZt2zb4+vrC2dlZiLCnZ8+eiImJga2tLS5cuAA9PT3IyMhARkYGHTp0wNKlSxEYGIisrCwsXrwY3bp1Y9IZgRpjJj09HcnJyVLdqwsXLiApKQnOzs6QkZGBqakpTp06BWtra4wbNw4DBw5ETEwMkpOTWScAANDW1sYff/wBoKZm8N69e7C3t0dJSQnKy8uZwYSNGKsug4kkUjYnJyeR+ptXr17h1atX4PF4cHR0lFifA9T0BbaamOfPn0NdXR0zZsyAu7s7GjVqBFtbW5EJW1FREampqRg5ciTrhMyHoaEhk+bJB39hyIetrS1u374tMnBOnz4dY8aMwYMHD7Bjxw4YGRmJtP/q1SvWz/X19VFcXIzIyEj07NmTSeH28vJiauv27t0LKysrEYImBwcHmJiYYMiQIUhISICnpye2b9/OLFZrw9PTE+/fv8ekSZOYxZGSkhJmzZrFGNn8c5o2bRqmTZuGzMxMbN++HSNHjoSamhqqqqoQEhKCiIgIZv/AwEBERUVh0aJFKC8vx5IlS6TuP2zgSnlVUVHh1Kju168f+vXrJ/b7zZs3o127dujevbtQul1oaChmzZol1tA+evQoMjIyUFZWBnd3d4SEhCAnJ4d1sejp6Yk5c+YgJCSEMYJtbW2FiKpGjRqFwsJCzJs3j7V/Dh8+XCSNX0tLC58+fUJJSQljTCooKMDW1hYAMGvWLFRUVACoSUEWB/6xNm/ejB07doiQUv7xxx949uwZJ0Ee/74tXryYWTjw4eLigvXr1+Pr16/Yv38/evfuLfS7VatWMX/XNopr4969e2jRooUIMZW7uztTF3rjxg14eXnh2bNnIk4LHo/HzDXe3t7IzMzEt2/fAACfPn1CREQETp48ycnlwQY+2ZkgBgwYgMuXL2P48OHg8XjMPp6enlK1yX8+BgYGKCwshI2NDTQ1NVFUVAR3d3esXr0aW7ZsYfYtLS1FWFgYc4/LysqYuU1bWxtv375F48aNYW9vj8zMTDRu3BivX78WMbTfvHkDS0tL8Hg8PHnyBA0bNkSTJk2wf/9+tGrVCseOHRMZYzIyMoRSnZ2dnQHU1C36+vqibdu2zPtRUVGBvn37MmMVFzfK2rVrMWfOHIwaNQpJSUkYPXo0Hj16hPT0dEyePBlr165l5aiQl5dn+tT06dMRGBiI27dvCxlEO3bswJo1a5jz5d9vvhOY73QhIqirqwvxTCgoKKBNmzbw9/dHkyZNkJiYiC5dujDf9+7dG7t27cKQIUOQlJQEQNSQsLW1hZ+fH2NIaGlpSXRWGxkZQUVFheHpqA2+84ut7/O/69ChAwYOHIgtW7awztu3b99GVlYWtm/fjqCgIEyePBnDhg3DmDFj0LJlS87xmT9f1yafe/TokZCBRkRC6zLB8+e3Ic7wEjdmCWLo0KHw8/PDypUrRYxgftndoEGDhH5TUlLCBBFWr17NBFz491tPT48xgoCa9YmWlhZ8fHxYSUr79+8PCwsLXL9+XYSjIDAwkOEokISqqiqsWrUK5eXlaN26tch6KCgoiLON2tweMjIysLa2xsKFCxlHhDRrIP5vDAwMYGtrKzQW79y5EzExMULjm4ODA4yNjTFp0iROQ5utpj4qKgpLlixBcnIymjdvjvT0dJw/fx7Jycmwt7cXcSpIawNIQnR0NPbs2SPyuZ2dHYYNGybV2obvZKmdZr5ixQqkp6fj7t27rMcwMDDAu3fv8Pz5c4nv4D8C/+0Q+v8vyMjIIG9vb2revDk1b96cvL29Ge1Ve3t7RtvQ1dWVpk+fTkREa9asIWNjY6lkK96/f8/IPVRVVdHSpUvJw8ODpk2bRh8+fKBPnz7RiBEjWCV/Pn78SM+ePaPy8nLKy8tj0hUfPnzInL+WlhYjZ2VhYUEXLlwgopqUScG0cEkyZdJAXV2dSX9p2LAhXblyhbp06UJt2rQhGRkZqbS6hw8fTpGRkUREtHDhQtLX16exY8eSqakp9e/fn0xNTVlT+m7cuEFmZmZExJ3yNG/ePFJVVaXQ0FDmfoWGhpKamhrNmzdPRGZl4cKFtGnTJjp37hwjLXX48GFq1KiRiJSOlZUVHT58mFPOYfLkyaSoqEhubm7k6+srUg6gqalJV65ckfres4GvX66qqkq3b99m/v/8+TN9+/aNM4WYSLq07YqKCoqPj6eQkBCaOHEibd26lcrLy4mIOLVNiWokf6TRKk1OTqYrV67Q3bt3WXVd+Xj58iUtW7aMrK2tSVVVlXx8fIjH45GsrCxFRUUJ7ctPwZRGt5kLXCmvnp6ejI7vj0JRUVFs+QVf31RQ7zM4OJimTp3KaBDj/7SMJelxSgNxGsJ8mJubM7rw/HHP39+f5OXlmVS/n9VQlaQ1X1lZSTt27KCXL19KbENVVZUZG2unMisqKpKRkZHQWPojqF+/PqucYGJiIiN50qxZMxo8eDDl5OTQx48fqaSkRGjjkkMTxJs3b+jy5ct0+fJliSUZRHXTaZUGsrKytGrVKiJiT2UuKioiW1tbsrGxITk5OWrTpg3p6upS48aNmbR9Z2dnRhbGw8ODRo4cSc+fP6eZM2eShYUFnThxguzs7OjAgQNUVFRERUVFdODAAbK3t6cTJ05QREQELV++nD59+kRnz54lJSUlUlRUJBkZGUY2qKioiDp06EA8Ho8pK+LxeNS+fXsqKipiric/P5+OHj1KR48eFUnhtrCwYNVgz8jIIDMzM7K2tqY9e/aIPLN58+YxqcXSSD0dOnSI2rdvTzo6OqSjo0Pt27enI0eOSPU8uOTUlJWVWcfne/fuMX0zPT2ddHV1ydjYmCk9aNCgAenq6jKa8Tt37qQGDRrQ3Llzmfs1d+5cMjExoejoaHJwcCBZWVmaOHEi69h47tw5at26NZ07d44+f/5Mnz9/pnPnzlHbtm3pxIkTdOXKFdLX1yc5OTmx87Ygvn//TgcPHqQ+ffqQvLw82dvbU4sWLahNmzZix2ciovj4eJKTk6MhQ4YwJQBdunQhWVlZRlouOjpaosZ1eno66ejosN4vTU1NzjKfb9++UWBgoJB0paKiIk2dOpW+fv1Ky5Yto4SEBGb/wYMHk4yMDBkbG9dZ4rGsrIyys7MpOztbaA4WV0p4+/ZtUlVVlartefPmkZGRESkoKJCioiItWrSI/Pz8SFdXV6rSil8B/nsOgDQ1NVnfc0VFRdYxPjc3l9HBloQOHTrQqFGjhOTvKioqyNfXlzp27EhExFn+yWUDSANFRUV6/Pix0FjTv39/6tGjB8nIyIiUD7GVE+np6YktITUwMCBjY2Nm/S14nEOHDpGFhYXQb9jewdWrVzMScn9X/Gto/xfx6NEjqqqqoqioKGbQYJvUf4V+3pAhQxhNOr6xdPr0abK2tqYhQ4aQvLy8RIO4Q4cOdPjwYSKqMWTd3NzoypUr5OPjQ3Z2dj93IwTAtUCSBlwDDn8wqQ2+Fmp+fj5ZWVmRiooKo5eroqJC1tbWzASnp6fHLIAEsWfPHtLV1ZXqPFu2bEknTpwQ+fzEiRPUvHlzsQtKa2trev36NampqTGTOxvMzMwk1vRIA/4CGWLq7ZSVlWngwIES65CWL19Ourq6tG3bNsbJEBsbS7q6uhQREcF5DlzapnWBJGP4+/fvlJiYSO7u7iQvL08tWrSgTZs20adPn4iIqFGjRjR+/HhGO5OPTZs2kaWlpVS6zVwwNDSktLQ05lz5k3dSUhK1b9+eNm3aRIaGhjR9+nTas2ePSC2vNGjQoAHt379f5PN9+/aRiYkJEZGIE8vFxYWGDh1K0dHRQouBn4GNjQ2rkcGHuDpvTU1NGjRokFQaqlzg0poXN1YIgmvhsHLlSpo0aRJVV1dLbOfcuXPk7u5OFhYWZGFhQe7u7oyjYf78+WRhYUHFxcXM/gkJCaSiosI8SxUVFdZaXD6UlZUZJ6bgefLHPSISW287ZswYqRxZvwLKysq0a9cuIiJ6/fo19ezZk9TV1al58+Z0+/Zt2rNnj1jH3IwZM4ioxtjZvn07EdUYrXp6esTj8UhRUZESEhJEHABsvCOCzoGnT5/SwYMHhYyQnj17UuvWrZl6cSIiX19fMjIyIlNTUxFnVe2Nf62SuFGUlZWZekV9fX26ffs2EdVwQejo6FBhYSE5OjqSvLw802/k5eXJyclJyNiXhFevXtGIESPIyMiIZGVlRcZ4Lri4uNDgwYPpy5cvzGfl5eU0ePBgcnV1JSLpDAkXFxfat2+fSPv79u0jFxcXMjQ0pLCwMLK2tmYdG+3s7Fgd6FeuXCFbW1siItLU1CQdHR2Rfdjm7W/fvlFCQgL16NGD5OTkqFOnTiQrK0sqKiqUkJDAeg5ERE2aNBFxxhIRRUZGUpMmTYR0gsVB0v1q0KCBxDFLEOKMYDMzM+ZeJScnk5aWFp05c4b8/PzI1dWVZGVl6e7du1IdQxzEcRRcuXKF4brhgoWFBR0/fpzU1dVJVVWVWX+tWbOGhg8f/lPnJy3477lgUCE3N5fatm1LPXv2JCKiVq1aUUBAgMhvp0yZQq1bt+Y8BldNvTQYOXIkbdu2TawT5vv37+Ti4iKx7/GDIrt372aca6NGjaKOHTuSmpoa+fr6koaGBpmYmDDGdcOGDUlDQ4OxRZSUlITGRD4ePHhASkpKNH36dOrQoQMVFxcz/BdXrlwhCwsLkfUc2ztoaWlJ6urqQo6ivxv+NbT/JLBFSGVkZBgPPFGNMfzq1SuRSZ3L4BI8xoEDB2jhwoW0cOFCSkxMZAZqFRUVVgKOS5cukYqKCtna2tL169fFnv/p06fp4MGDRFTjobe2tiYej0d6enp0/vx5Zr+qqip6+PAhXb58mVJTU4U2acC1QPoV4Iqw9urVi9zc3IQINt69e0dubm7Uu3dvIiKpSNn4+PLlC+Pc4G9ENQOSuEgt3wtaUVFBu3btYo30NmzYkHVw5iM+Pp4GDRr0UwtkfpRBXl6etmzZQkePHqUhQ4aQnp4eTZo0ifr160c8Ho9cXFwoMTGR1fCrrq6mmTNnkpKSErN4U1BQoOHDh4slf2rTpg2z9e/fXyJBU10gieRDV1eXtLW1adKkSayR1o0bN5KCggKpq6tTXFwcxcXFUbt27UhWVpaGDBlCERERpKenR76+vrRy5co6ExsRsWd0EBE9fvyYlJWVxUYL+ZHm2sR9bFt4eDhpaWnRsmXL6NKlS3Tp0iVaunQpaWlpSb2A+xU4c+YM9ejRg7ne2qiurpZIdjZq1Cj6/Pmz2Pa/fPlCK1asoF69elGLFi0Yp5m+vj7p6+tTcHAwBQUFkZaWFnXq1IkhABLcWrRoQefOnZN4HWwLh06dOpGKigo1adKE+vfvT5qammRubk59+vRhjQLwyc6GDRvG9Jfhw4eTvLw8Q+zIRTLWtWtXIeKk2mDLECCqecdtbGyIiGjcuHFkYWFBJ0+eZMaqEydOUKNGjWjChAkS74M4fPz4sU77cxHxaGpq0smTJ0U+Dw4OFkv6U1ZWRrdu3aK3b98SEYmNJoqLMLJBSUlJxFHUpUsXatGihdTZV3369CEnJycmqktUM+81b96cPDw8yNzcnDlGixYtGDLDM2fOMMZKdXU1JScn09q1a2nt2rXMM66Nb9++UVFRkQihn5ubG9na2tLGjRvp8OHDdOTIEaGNTxQmbsvOzqb69euTrq4uubi4kIuLCxO9vnfvHnOvuAwJJSUl1jk1Ly+PlJWVSV1dnS5dukTKysqsY6OSkhKrcZidnc3Mp+rq6oxTSRCC83ZGRgZNnjyZdHR0yMjIiGbNmsU4sNTV1SksLIwMDAxYz4GIm3xu6tSpNGvWLNZnxEft+8Ufj3x8fEhWVlbimFUb/IyN2u0XFhYSUQ2p7rhx45j7oKWlRebm5oxTRxxKS0tp7ty51LZtW2rUqJFIvxg5ciTZ2dnRjRs3qLq6mqqrq+n69evUtGlT8vX1perqanr27JmQg6Y2VFRU6NmzZ6Smpkb6+vrMe/Lo0SPS0NCgyspK+v3336lly5ZUr149VuJaafbhehaZmZki41JGRgbzzC9evEiqqqpkY2NDY8aMoTFjxpCNjQ2pqakJkaWKgzTEcny8fv2ambcF1/9+fn5kZWVFPB6PGjRoQN7e3rR161ahd4rLycMVFJk5cyaNHTtWKLuzsrKSxo0bxzg5W7ZsyUr+GhYWRs2bN6dv377R2LFjmexafnbaiBEjmHYlvYNENSSgte/L3wn/kqH9CSgoKGAlhSgvL0daWhpTkyGJQECSfh4A3L9/X6JcSe/evXHixAkRqRG+5M+mTZuwYsUKbNq0CU2bNpXquj58+ABtbW2mroqrRlCcBp84EBG+fPnCaBxyycIIQpJ8wIkTJyQSYy1evJhTliUgIEAiKduKFSs4dVibN2+Opk2bIiYmhqm///79O8aOHYt79+5h9erVEvVLHz16hNOnT2P79u2sEktOTk549OgRiAhmZmYitXzSkHO5urpi/Pjx8PPzY/ommy6u4N981H7mpaWlePDgAZSVlUVIY9h0HPngki+rS12ppHcsPj4egwcPhpKSktjfHz58GJGRkUzdXFlZGTQ1NVnvvyBq6zaLQ8uWLbF48WL07NkTnp6e0NLSwtKlS7F27VokJiaKrU/kIvAThI+PD1avXo3IyEi8fPkSAFC/fn2EhIQgMDBQKlI/oOadqS3FM3XqVCE9V0nQ1tZGeXk5KisroaKiItI/+VwV379/F1vnLQne3t5ITk7GoEGDhHTm+Vq44qT7+ODxeJg5cyZmz56NRYsWscr4aWho4Pv375g8eTJ27NiBqqoqyMnJoaKiAhYWFujYsSPn/dy+fTsaNGiA0NBQTJkyRei7DRs2ICIiguHiqE0yJkju9OjRI8ydOxchISGsdfMnTpzglEPT09MTqbcFgJSUFAwZMoRTR3v58uUwMzPD0KFDAQCDBw/GwYMHYWRkhJMnT0qlgSrpHeVfh7e3N44fP44OHToAqOFK2LZtGwYNGiQkjScOtcdtABKln2ojMDAQjRs3xq5du0RqOW/evAkvLy8UFBRwtvP27Vv4+voyJJ/Af7hRduzYgd9++w0mJiYICwvDhg0bEBISgvbt2yMjIwMDBgxAbGws5zHy8/MxZswYXLt2Tehz/pitoqKCy5cvw9HRkfX3a9asEfq/oqICWVlZOH36NEJCQhAaGory8nLs3r1bSKZScH0iDTlX48aNMWDAACHZL6CGA+Hw4cPQ0NCAj48Pli1bhhYtWoiMjUZGRlBXV0dcXByjN/727Vv4+PigrKwMly5dQr9+/ZCamoqPHz8KHYM/b1+6dAm5ubno0aMH/P394eHhIUTw2bJlS4SEhGDYsGHo06cP6/jMRT7n5uaGuLg4WFlZsY4pUVFRIveLLx/24cMHPHjwgOH6qQ0ej4cLFy6gurqaIZDlE/epq6tj+vTpmDNnDho0aIDExES0a9cO1tbWWLx4MQYPHoyHDx+iZcuWWLVqFQ4dOoT4+Hix79Pw4cMlcsD4+vrC19cXx44dE+rbnp6e2LFjB9TV1aGkpIT79++LlR+0trZGXFwcWrdujQ4dOqBPnz4IDQ3Fvn37EBAQgAkTJiAmJgbTp0/H3LlzMWfOHDx9+hRHjhzB/PnzERgYiPnz53PuIwn899zV1VVoXKr9nr98+RIbNmwQegcmTZoklSxnYGAgDh8+zFpTP3DgQKxevRqfP3/G5MmTkZCQwKyrZGVlMXToUGzYsIGpRX/x4gUuXbqE1NRUpKamIi8vD0ZGRnj+/DmCg4OhqKgo8o7xQUQIDQ3F2rVrRbhs5s+fD319fVy5coWxMfh4+PAh2rVrh/fv3+PYsWMYMGAAvLy8hNbXe/fuxf79+9GiRQvo6+vj3bt3uHv3LkpLS+Hk5MT0AXt7e4nvIAC8e/cOBgYGnNwmf1n8jwz8fzTERUgBMKlVRD9Hqd+mTRsyMDAQipAKypVER0dTt27dhFIPBSV/BCWtlJSU6uz1I+KuEZQWMTExZGdnRwoKCqSgoEB2dna0detWqX/PJR/AFmFVUVFhvHDiUp6GDh1KioqKFBwcTAEBAaSurk52dnbk5+dHfn5+jNTLlClTOCXbiGrSAw0MDEhfX5+J1Orr65OBgQGlpaWJZDzw8e7dO5KRkSFHR0dSV1cnNTU1atq0KROx42+168Rrb9KAH2WonXLNjzIQCXvzfwRnz56l5s2bi5Q1ODs7U3JystTtXLlyRWLdNdHPy1b82WDL6OC/k39GqhS/lrE2HB0dRfqTk5MTNW/enNq1a0etWrUiWVlZiRFYLuzYsUPi9rPQ0ND4aY4CthRjwbRiflSmvLycCgsL6cSJE7Rv3746y+6pqqqKRMGSkpJo06ZNpKioyGR6JCYmkomJCfn5+VFSUpKI3CJbXbTguXLJoUlTbysJklJSBaVzJMHU1JSaNWsmtu/5+PjQnDlzSFtbmzIyMmjixIlUv359atWqlcQocu1oMlFNpPvBgwd0584dql+/PrNJI9945MgRatWqFaWnpzPtpaenU5s2bZgyK2nx8OFDVm6UqqoqofThhIQECggIoLVr19L379+JSFSerrbUU7t27ahTp0508uRJysrKotu3bwttXCUc4rB+/XoaNWoUpaamspaTVFRUMJlsAQEB1KBBA0pISKDCwkIqLCykvXv3UoMGDSgoKIiIavq7goICOTg4MHNqs2bNSFFRkY4dO0bx8fE0cuRICg4OZs12y83NJWtra1JQUGDkFxUUFKhJkybMPe3duzcpKyuLnbfbtm1L/v7+rJFhIunGZ37m04QJE5jMp/Hjx5OioiJt3rxZqr4pzf2ShNDQUNLX16eNGzcy/C8bNmwgfX19+u2332jy5MlkampK3bp1I11dXfrjjz+IiGjv3r3k5OREjo6OpKamRoqKitS4cWORd5GIpOaAycvLE8tRwJVJOWvWLFqyZAkR1fR9OTk5srS0JAUFBZo1axaTWk5UM7ezpZZLs48k8N9zwdTxH33PxYGrpp5Icvnn0KFDmbbKysrozJkzFBoaSm3atCEFBQVydHQkopqsKA0NDWrRogWNGzdObDbEH3/8QTdv3hThstHS0mLldjhy5IhQSd3x48epXbt2pKKiQrq6utS1a1e6ePEiVVVVcZapLly4kFVa9Z+EfyPafwJUVVVZI6SysrJQUlJCWVkZADDSRrXF36WBsrIyeDwe7t27x3jcnJyc8O3bN+Tm5sLR0RH5+fn49u0bq+QPG3ujoExXbeH62jh06BBUVVVx584dWFpa1vn8+Zg/fz6ioqIQEBAgJOW0fv16BAcHY+HChZxtcMkHPH/+HIBwhNXKyoqREPDx8WGVZenWrRvU1dUZuQdx4PF4KCgokCjZdvLkSQA1EdHakQAvLy+oqqpCRkYGr1+/ZrzzfOTl5cHZ2RnTp0+XeB6CjNI/Cn6UYcOGDULeXH6U4eHDh8jIyEDfvn2ZqFtd0bRpU2zevJmJTvFx+fJljBs3jomYckFDQ4OVGV0QXNGyjIwMsVI6s2fPRnV1NVq3bi30eVpaGmRlZYXYe3/2PPkoLy8XyegoKytDamoq6znyvfNVVVU4cuSIEGOtp6eniGdYHGbPno1NmzbB3t6eeQfS09ORnZ2NUaNGYfPmzfj+/TsOHz4sJN1UOwL7v4StrS0SEhLESu5Ig9TUVABAr169EBsbKxKd6NixI2dUhg9xig8A4OXlBScnJ4SEhDCfsWWO1Ab/uydPnnBeC58FVlKGgKurK3R1dREXF8dkdnz58gW+vr748OEDzp07J/EYysrKyMvLg4mJCYKCgvD161dER0cjLy8PrVu3xvPnzzn7Llffy8nJwfnz5zF27Fjs3LkT+vr6SElJqdO8Iy17dG0IZnABNe9iZWUlk3XE/1tVVZXJyPhZfP36FdnZ2SKSbXzJQC6pJ1VVVdy6dUvsvJWcnIzIyEhER0dzZnkI4vHjx3B0dERZWRmKi4uF+jNQwzBtYGCAqqoqfP/+HSEhIdi8eTMrQzV/7n369Cmio6OF3pPx48eLnBeJyXarrq5GcnIy8vLymN93796deZf4kWFxePr0KUxMTCAnJ4cLFy4wn3/58gW///475s+fL7Q/2/gMiGY+2djYICQkRGqZO2nvV1FREQDAxMRE6Pf169fH5s2bRVj+k5KSMGnSJDx9+hRr1qxBUVERRo0aBScnJwA1Sgjq6uqc43dYWBjMzc1x8uRJ2NjYSHVNbDh27FidMilv3LiBa9euwcrKCh4eHlBVVcWDBw/QsGFDGBkZ4cSJE2jevDkeP34MJycnfPr0Sap9aoPtPf/+/TuTeVhZWQkZGRkoKyvjypUrnOct7RxUXl7OZK01atRIKFNOVVUVZ86cYV0nubm5ISgoCBcvXkRWVhZsbGzQuXNndOnSBZ06dYK2tjYAyf2fnw0hCdOmTUNcXBx+++03obXxsmXLMHLkSNZModqws7NDbGwsnjx5Ak9PT5GMDmlRl3XUXw3/Gtp/AnR0dHD8+HERHWEZGRnIysrCw8MDQM2g4+LiItLxDh06xHmMZs2aIS8vD/fv32c6Xnh4OJ48eYJTp05h0qRJEn/PZpTVVVPVxcUFM2fOhJubm9S/qw19fX2sXbuWkaDgY+/evQgICMC7d+8421BTU8Pdu3dhbm4uoj/dpEkTfP36ldmXb3QL6hLzZS4EU574siw7duwQkYsQdw4/qsPK131MSkqSqF8qjRa3JFRUVEBZWRm3b98WO8kdPXoUgwcPRqNGjdC2bVvweDxkZGQgNzcXiYmJ6NOnD4KCgnDs2DFmYqprCrGysjLS09NFziE7OxutW7fGly9fpGqHy4gGJA/OCQkJ8PHxQc+ePZGcnIwePXogLy8Pr1+/Rv/+/XH//n3MnDlTRBbl0KFDWL58OdLS0n7ZeYpDVlYWevfujfLycpSVlUFHRwfv3r0T0t0tKChA79698eLFC1b90kGDBrEaboJ6s+/evUObNm0wb948oX0WL16MZ8+eYe/evRg9ejSuX7+OjIwM5vv8/Hw4OTlJ7N+C+FmHgCScOnUKa9euxebNm0WkRuoKSc+Mv3Dgyw3WhjQpf4sXL8bKlSvRvn17xsF448YNXL16FdOnTxfS1uVKdfwZ3Lt3Dz179sS3b9+YNO87d+5ASUkJZ86cEZHDqo369euLTUlt3rw5NDQ0JPZdAPD390fDhg2F+t60adOQlpaGz58/o3v37rh27RqePHkCBQUFNG/eXEiTV5rFnre3N549e4bVq1ejS5cuOHz4MKKiohida3Hv5v3798Hj8UTmJzbwpZIkoaqqCjt27GC05GunQs6cORMjR45kLT/i8XgwMDDAihUrJEo98VOBay/Q+ZC2hKM2VqxYgY0bN6KwsFCiQ1hQQ1qSISENJElq/grIysqKdRro6+vD3Nwcx48f/ynjsi5gu1+VlZUIDw/H2rVrmXFWTU0NAQEBCAsLg7y8PJSUlJCdnY3GjRsLtffw4UM4OjpKPadKwq5du5CUlISdO3eyPkeuvn3hwgWhvqegoCAkIQcAR44ckVg+5+/vLzG1/M2bN5zp52/evGHa5Tsu2AzO9+/fQ1tbm3HaSLs+/pGySTbwyx7FlX++fPkS+vr6CA4OxoABA0SevTjU1hmXhMTERKxcuRJr1qxBcXExgBrZvaCgIEyfPl2qeZvvXMnMzMTdu3d/2FD+mXXU/xr/6mj/CejTpw/GjRsnEiHV1NSErq4uY7iNGDHih4+xdOlSeHh44NSpU4zX1M7ODgcPHmQE7u3s7KClpSW0aBNE7UWvp6dnnRa9AQEBmD59Ol69esVaIyiNV6+iooI1MtiiRQvGs8sFLS0tFBcXi2QGZGVlwdjYmLN+SUtLC0lJSSgoKEBOTg6AmuhYXSImFhYWnDqscXFxrL/l10ESh34pF/galOIitB8+fEDDhg0lTgKenp7Izc1lolJATXTvyJEjMDMzw8aNG7Fx40YMGjRIyEDo3bs3Vq1ahcmTJ3OeZ8uWLTFt2jTEx8ejXr16AIDXr18jJCREpP6RDUSEoqIiEV4AcfuKQ0REBHPO6urqWLNmDczNzTF+/HgYGRnhwIEDjLaoIJycnJh+8rMgIiQmJiIlJYV1cfLhwwd4eHhg8+bN0NTUxI0bNyAvL48RI0YwWSmBgYFo1KgRbty4wapf6ubmxhkxPHv2LOMAFMSwYcPQokULeHp6QklJiYk88ZGUlIQ+ffpIda1sDoGlS5cyDgFB4+lH4OzsjK9fv8LCwqJOBgQbiAiPHj0ScSA4ODhg2bJlCAkJERuV8ff3R1ZWFo4fPy6UpRMUFITx48cjISEBsbGx0NbWRk5OjlBf0tLSQmxsLKqrqyEjIwMejyfR0H748CHWrVsnFEkLCAiAtbU1+vfvz+lg4WvYC2bZDB8+XKjeVhL4tXlWVlZ4//49evXqBaBm7JWRkeHsuwCwf/9+3Lp1S6jdrKwsVFVV4cGDB9DT0wMR4f379+jYsSM+f/7MaKZLyy9w4cIFJCUlwdnZGTIyMjA1NcXXr19hbGyMlJQU1iiX4P2SxoiWBkFBQdixYwfc3d3RtGlTkfMPCAjAkCFDMH/+fGZsFISurq6IA782li9fjpkzZyIiIoJ1Xl69erXE3zs5OQmdFxHh1atXePXqFRwdHZmoKJtDuPa5qaioiBgKtVFeXs46XyUmJopku61btw5Tp05FYWEhJ4eLNA4qcdkjd+7cga6urpCTXhzGjh2LESNGiPAc/AhUVFSYaCTfmA0ICMChQ4ewYsUKofFkwYIFeP/+PTZt2oRmzZph/fr1QrwDR48exenTp9GsWTNG114c+JHwW7duieh4C96fgoIC1KtXj5UDpl27dhL7NsDd97p27crq+Pj06RO6du2KkJAQnD9/Hq1bt0ZAQABGjBiB2NhYFBYWIjg4GADQv39/iftI47hgw88+37oYuIcOHcLcuXOZdRJf8/vVq1cICQnBvHnz0KZNG6SmpuLixYuIjIyEgoICE9Xu0qWLkOFdUFCAR48eoVOnTlIFjviQkZHBzJkzMXPmTMaBJs6eEAcfHx+Ul5fj+/fvsLW1FXHS/KpMoL8y/o1o/wlgi5AKkkLUpaOLg4yMDDNJCBJUATULA/6CXUZGhtWw4oqC8Re9ktIf+Z4+Nkjr1eMiGduwYQNnGzNmzEBaWhoOHDiAxo0bIzMzE69fv4aPjw98fHzw9etXxMbGIjw8nCEVuXLlChYsWAB/f38sWbLkpz3nq1atgqysLAIDA3Hu3Dl4eHiAiFBRUYGoqCgEBQUxEygfFRUVKC8vh4KCAlRUVBAUFISQkBCxXn8uQ3rKlCmcJCCxsbGcpCeSIC2JkyQUFBSgf//+TMopUONZtrKywpEjRzgdHNXV1VBSUoK8vPxPeUhVVVVx//59mJmZQVdXFxcvXoS9vT0ePHgAFxcXfP/+XchY4uPatWtwd3cXIdgRB0me2KCgIERHR6Nr165CBF58HD58GGlpabC2toaWlhauX78OGxsbpKWlwdfXF7m5uWJLVfhkfsOHDxeJGAL/iVZv3boVampq0NfXF0lJjouLQ0hICAICArBixQp8/fqVSaesawS2d+/eICLs3r1bxCEgIyODEydOSHU/xaFbt24oLCyEn58f672UxljipxifOHGCdXyrqqrijMp8+/ZNYsofv3RIEpSUlNCqVStm4dSuXTuR4xw8eBDDhg2Ds7OzkNMrPT0dCQkJOHbsGI4cOQItLS20aNECQA0hYklJCXr06IE7d+7g6dOnOH/+vFiyJS5UVFSITUmdM2cOsrKyJPZdoIY46/fff4ePj49Q2/y+9/r1a+Tk5KBz586c5GzioKGhgezsbJiZmcHU1BR79uxB+/bt8eTJE9jZ2aG8vFyqdn42I0NPTw9xcXHo3bu32PPMysoS63SaNWsW1NTURN5lQfD7be3+z18vcM3L4eHhIu3p6+sjOTkZmpqa2LlzJ4YMGSLiEDYzM4O/v7/UJKZc6fw6Ojoi2W7m5uaYPXs2fvvtN6irq4ttm4uMkp8q/OnTJ2hoaAjdq6qqKpSWlmLChAkwNjZGXl4eE7xgQ9++fXHmzBno6+tj2LBh8Pb2Fks0Jw6SggErV65EQkIC48Ti4+TJkxg+fDg+ffqE1NRUuLu7o2HDhsxYsG3bNqioqOD06dPo3Lmz2GPzeDwUFxdj2LBhuHjxIhMYKCkpgZmZGQYNGiRVuu+6desk9m1pwFU+J5gtAYimlrPh+vXruH79OrPPxIkTcejQISxcuFDEcdGvXz+UlZWha9eu6NSpk9B72Lx5c5w/fx7a2tpYuHAhZsyYUacMDWki4nyHCD8jS1L5Z21y2zt37mDVqlXYvXs3qqurUVVVhffv32PIkCFISUkBj8dDfn4+LCwsMGbMGGhrayMyMlLq8/9R8Ilbx48fj0WLFok4UaR1Yv4b0f4XQuBHSPPz84VqcX+mlrk2UlJSxNYRAsC4ceMwfvx4ZoFVG1xRsL1793KmP0pTJ8iGadOmMX/zeDzExMQgOTmZScVMS0tDYWGhyMJLHCIiIjB58mSYmJigqqoKtra2qKyshLe3N+bOnQsTExPExMQI1S85ODjA2NgYkyZNgqysLGudeHBwMAoLC6WqE+d7U4GaBX9ubi5u3boFS0tLJrLPZpjl5+dj4sSJCAkJwfLlyxEUFCQyeH/+/Bn9+vVDhw4dJBrSa9aswdatW+Hu7o4FCxZg+PDhaNSoERwcHHDjxg0EBgZi/fr1KCgoQP369WFqaioygfIX4jdv3mSNsJaUlLCWCvTo0QOzZs3ivE8AYGlpiezsbJw9e1bo/ejWrZtUESoZGRlYWVn9cP/jQ1tbG3/88QcAwNjYGPfu3YO9vT1KSkpQXl6O3r17Y/bs2UhKSmKcYyUlJfjtt9/QvXv3nzo2H/Hx8Th06JDYxcnx48eZhbOBgQEKCwthY2MDTU1NJu1NUVGRuQ5BlJaWQkFBgTViCPwnWr1161b4+flh7dq1CAoKQsuWLQHURL1jYmLw22+/ISYmBvLy8qioqBBiPuZHYPmQFIFNTU0VGm+AmgjdsmXLftjQE8S1a9dw/fp1qZiuxWHq1KkoKSlhODCOHDmC169fM4tggDsqM2/ePFZnqqampoizTRzOnTuHS5cuMRwWVVVVcHZ2Zgzv7t27MwzptcensLAwzJw5E4MHD4aXlxfWr1/P9KHq6moEBQVBXV0dCQkJaN26NUaNGsU4GPnYtm0b3r59y/lOy8vLY8aMGSKfBwcHIyIigjluRUUFbt++LdJ3ATBswrdu3WLte0ANW3VdjRdBWFtb4+HDhzAzM0OzZs2Y+uTNmzfDyMhIqjZ+RUaGgoKCxHXAoEGDcPHiRbFtff36FVu2bMG5c+fg4OAgEn2LiopCSkqKyO/KysqYsb62sVIb4rg+JkyYAKCGvV+SQ1ha8N+1tLQ0Jp1f8F3z9vYWyXZ78uQJ8vLyUFlZKTT+CwYZpMHq1atBRBgzZgzCw8OF3le+06Bt27ZMdDQ5ORn29vasZX5JSUn4+PEjDhw4gD179iAqKgpNmjSBt7c3vLy8pKqDnzNnDmJjY4XGQn4woLKykrUNc3Nzpoa4c+fOyMvLE2LBnjNnDsOCzcXWPHToUPzxxx+4f/8+kyafk5MDX19fFBUVYe/evZzXEB0dDUtLS+zdu1diLS6bQkz79u2ZMVdStsSlS5eEUsvbtGmDNm3aoLKyEpcuXUKnTp1Ejte2bVshZ/mePXtEHBcODg4wMTHB8OHDMXjwYCxduhR+fn4wNjZmxtycnByUlZVBW1sb4eHhmDBhQp3eAWnUUmo7uSSBiJCVlYWLFy/i4sWLuHLlCj5//gwHBwfGsRIcHAx5eXlm3cDH0KFDMW3aNERGRqKyshIXL17Eo0eP4OXlhU6dOqGyshKysrISg2mAdEo258+fR5cuXSArK4uBAwf+LQ3ln8W/Ee2/MSR5eE6fPi1RpsbIyEhiFMzd3R1ZWVlYt26dSPqjo6MjEhISmN/k5OSIRFh5PJ5YDyMXQYlgG1xkDYIoKirC3bt3UVZWBicnJ2ZBw1W/pKam9tN14mwoKSlhvMOSkJGRgREjRiA/P581berNmzcwNjZGw4YNsXbtWri7u0NdXR23b99Go0aNsHbtWty4cQNJSUmcJCBcAzlfRq60tFTE08/j8eDm5iZC4gQAK1euREZGhlC/+DNx7Ngx9O/fH8eOHRPx9EsLLy8vODs7Y9q0aVi0aBHWrVuHvn374uzZs2jevDnWrVuHTp064f3790yk7vbt26hXrx7Onj0rQkgjDpLqxM3NzXHq1CmxxEU9evTAqFGj4OXlBX9/f2RnZyMwMBDx8fH4+PEj0tLSxJL5+fv7o0WLFjh16pRUEcPWrVujadOmQhksAQEB8PLyAlBDEMRPO/4RiOOuuHr1Kjw8PH46hax58+bYuHGj2NppaWBkZISkpCR069YNRIRbt26hcePGOHr0KFasWCEVEc6WLVtw4MABkZQ/X19fDBgwQEQGSBLU1dVx69YtvH//HtHR0ULRChUVFWRnZ4sYbvn5+WjWrBlUVVVx9epVkXEvLy8P7dq1w7t372BsbIxPnz6JpMinpaVh2LBhP+XMEuy78vLysLOzQ0hIiFDf5WP37t1Yv379n9b3du3ahcrKSowaNQq3bt2Cm5sb3r9/DwUFBezcuZORJ5OEX5GRERkZicePH2P9+vWsRmF5eTkGDx4MfX191rTvw4cPi21b0nwpWIvML0moDbaI99evX0Uyp96/f4/KykoRMsD8/HzIy8tLTbDGf9datWoFDQ0NZGRkCL1rTk5OnNluP5uJlpqainbt2olNF+aKQrIZT8+fP8fevXuxbds25OfnS1UCJ4nMbOTIkejTpw+2b9/OGKDfvn2Dn58frKysfgkJqqamJs6dO8c4uvi4efMmevTogZKSEs42+H07Li5O7Lo0NTUVvXr1Qvv27XHp0iU8ePAAFhYWcHZ2xrt371BYWCgxW6JevXqcRHw7d+6Enp4e3N3dAdTwHmzZsgW2trbYu3cvWrZsidTUVJG6+wcPHqBTp05M1kxt2azc3FwoKipi9uzZCA8Px4wZM8RKT9Ym0fszoK2tjdLSUjRr1oxxBnTs2FFozWloaIgzZ86gWbNmQvbC48eP4eDggPv378PNzQ2FhYX49u0b8vLyEB8fj1OnTqGyslLsGp4Pafqev78/UlNTkZ+fD0NDQ7i6uqJLly7o3LkzJ6GoIP7OZGj/RrR/IUpKSrB3715MnDgRQA0BiyAJhaysLLZu3SqV8SUNgoODsWfPHjx9+hTAf2pqNDU1mQiZp6enSL0Vj8eDpqamxCjY8ePHRdIfe/bsia1btzIRzcePH6N///64e/eukC4y/3jiUtTYPO4/C0kTLlv9EgCsX78ezZo1Q25u7k/XidfWkx0yZAgOHjwIQ0NDTj3Zx48fMzXHOTk5ePXqFfNdVVUVTp8+DWNjY6YWHqipKeLXFfbp0wfz5s1DgwYNUFxcjIYNG6JRo0ZITk5G8+bNkZ6ezkzQXANj48aNMWbMGERERLB6axcvXowlS5bg4sWLrCROgvdYUo3c+fPnWUlTiAjh4eEwMDCQuKj28fFBVVUV+vTpw5q+K43Rtn79eqYGb86cOZCXl8e1a9cwcOBAzJ07F9ra2sjOzsbu3btx584dKCsrY/To0Rg+fLjYhRkbJPkyFyxYgPDwcGzbto21JjYiIoJ5T5csWQIfHx9MnDgRVlZW2LZtG4AaPWBfX1+0bdtWhMxv9erVsLS0lCpi2KZNG5w9e1bsuSorKyM+Ph5NmzaFvb292FRKcRDHXTFhwgSRBeaPYNmyZZg+fTqWLFnCaqRIU1tWVlYGAwMDEBE0NDTw9u1bNG7cGPb29kLee7aozKlTp9CwYUNs2rQJBQUFaNiwoUjK39u3bxEdHc20IykikJeXh4qKCsyYMQMZGRn49u0b+vTpw9QKdunSBZcvXxYxtK9cuYKOHTvi5s2byM3NFTG0c3NzmbH53bt3QtEjPvT19Rnymx+FYN9VUFCAhoaGSN/lw9vbG97e3mLbkqZeXBIE+VBatGiBZ8+esbJHS8KvyMi4cuUKUlJScOrUKdjZ2Yn0UXd3dyQnJ0NJSQkXL14UcXRKSocWRElJCWJjY5kU9wkTJjDp7Vzzb1lZGWbNmoX9+/ezkrJ16NABY8aMEVkkp6WlISYmBhcvXpTqHPnvGlBjNNR+15ycnBAbGys2261NmzbIzMyEo6MjDhw4AKDumWiCKdVsTgVpopCCqKioQEZGBtLS0vD06VPWOns2fPjwgdXZ2qRJE5SVleH48eNo0KCBEGHh9+/f4erqytT+fv/+HYGBgayZaBkZGbC0tBSZk/kZbtXV1axzmry8vNTaxfy+XVZWBn9/f5GsnkOHDiE0NBSLFy9mFGL42LhxIwYMGIAZM2ZgwYIFzNqDn7FnY2PDcDWwOYnev3/PBJMiIiKwadMmAP9RsFm9ejWOHz+O4OBgTJkyBYsWLRJxXCxZskSoJE5bWxu6urrQ1taGlpYW5OXloaysjOPHj4PH4+HUqVOscyCPx2M1tAVTz2vzINRG7XmhtLRU5Dns2rULHTt2lDivlZWVsa7jPnz4AEVFRQQFBcHZ2ZnhJABq1oidO3eGv7//L3HibN26FUBNqd6sWbOQm5uLyMhIhguHT1DMhb9zTPhfQ/sXYuvWrbh9+zZjaB89ehQ9e/ZkBpTr169j9erVWLBggcR2uDxypqamyMjIwIYNG6CsrMwsWqOiorBkyRIkJydzTqaxsbESF70XLlzgTH8MCgqCubk5zp8/D3Nzc6SlpeHDhw9MXdF/C+IkwvgT7ooVK+Du7o5z584JfV9UVISTJ09i//792LRpk4jnfMuWLRIXf4LYvHkzdu/eDQA4e/Yszp49i1OnTmH//v0ICQlBcnKyCCEJEaG4uJjpLzIyMnBxcRFpW1lZGevWrcPy5cslGtJcJCDS4MWLFwgMDBSbEsVF4sSHpBTi8PBwsRI1RARLS0tO+SSu9F0uVFZW4vjx4+jZsyeAmnsfGhoqsp+qqirGjRsnsa3v37/jyZMnaNSoEevEe+rUKRgbG7P+dsiQIdi7dy8MDAxYCWYEJ1wDAwNW5nlBMj9BUiy+ATZ37lyYm5tj/fr1iI+PB1ATMdy6dSsTMZwwYQLTD9lQXl6OgIAAbNu2DbKyssjLy4OFhQUCAgJgbGzMeu9qg80hwOeuWLNmDefvucB3ALq6ugp9Lq42lU2BgJ9inJOTg0mTJrGmGNeOyixZsgQGBga4c+cOYmNj0a9fv5++FmNjY3z58gWVlZVwdHTEokWL4ODgIPSueHp6YtasWbh16xZjiNy4cQMHDhxAeHg45OXlMXLkSAwaNIiJzKWnpyMiIoLJbtDS0mId569evcpaklQXCDovZWRksGPHDonRiO/fv7MaCVxSk+IgWKLEBWmYy7lKNKSBlpYW+vfvL/b7OXPmIDw8HKGhoRLTNgXJjZSVlYUMkIyMDPTs2VNobZCeno59+/YhOTkZnTt3FishBtSsN1JSUrBp0yaMHDkSGzZswIsXLxAdHY1ly5Zh4sSJrI6FNm3aiHB3SAJXOv+9e/cYMko+E7eenh709PRw7949RkJTTU2NcdR5enrCwcEBAQEBUhna5eXlmDlzplinwty5czFmzBhOFYOUlBTs2bMHBw8eRHV1NQYMGIDjx4+zzudskBQM0NHRESGcrJ1NVVRUhEuXLuHkyZOsmWjKysqshGjt2rXDsmXL4OLigqCgIOzdu5d571+8eIHg4GCR8VQc+H17165d0NbWZq2hv3v3Lvbs2SPyuYGBAd69e4esrCzExcVhwoQJKCkpQZs2bSAvL4+XL1/C3t6eM7Wcfy/4c9+RI0cwaNAgjBs3Du3bt0eXLl1ARDh//rxYx4W1tTXevXuHkpISJlocGhoqJJslIyOD8+fPi0TWJaFv377MeUszRzx58gRTpkzBxYsXhUj5pOVaAGrkKOPi4rBo0SIA/+FvWrFiBbp27YqUlBRcu3ZNZPwyMzOTim+nqqoKV69ehYODA2cA0dTUFPr6+nj9+jW0tLQgJycnUosvCZLWUX95/Cnq3P+folWrVnT27FnmfzU1NUbwnojo0KFDjJC8JDRu3JjOnz9PRETXrl0jFRUVio6OJg8PD+rfvz8REXXo0IFGjRpFFRUVzO8qKirI19eXOnbsyHmMjx8/kqenJ/F4PFJQUCAFBQWSkZGhfv36UUlJCUVHR1O3bt2ouLiY+U1xcTH16NGDNm/eTEREurq6dOfOHSIi0tDQoNzcXCIiOn/+vFTX+augp6dHe/bsEfl8z549pKurS0REL168oN9++40GDBhAAwYMoFatWpG/vz8FBwdTQEAAqaurk52dHfn5+ZGfnx81bdqUNDQ0aMqUKVKdg5KSEhUWFhIRUWBgII0bN46IiB4+fEhaWlpERMTj8YQ2GRkZqlevHnl6elJaWhrxeDxKT0+np0+fMtvLly+psrKSiIhmzZpFS5YsISKihIQEkpOTI0tLS1JQUKBZs2aJnNP169cpMjKSjh49ynxWWVlJv//+O7Vs2ZLq1atH2traQlv//v1p37590t76H4KhoSHFxcWJ/d7W1pauX7/+p54DEZGysjI9ffpU4j4FBQU0ZcoUcnV1JVdXVwoICKCCggIiIiorK6MxY8aQrKwsycrKMu/6lClTaOnSpVKdw+DBg0lPT48mTJhAYWFhtGDBAqFNHIKDg6XefgUCAwOpRYsWpKysTCoqKsy1HjlypM7vel5eHh09epSOHj1K+fn5v+T8iIguXrwocSMiqqqqovDwcNLQ0CAZGRmSkZEhTU1NWrhwIVVVVVF8fDxt376diIgyMjJIT0+PeDweKSoqUkJCAhERtWnThiIjI4lIeIxPS0sjY2PjX3ItzZo1I0VFRZKRkaGJEyfSmTNnqKysTGif2uNJ7Q0As/E/MzQ0pCVLljBjyuzZs0lbW5u2bdvGjDmxsbGkq6tLERERv+RaiETnQkHk5eVRhw4dmOfB3/hj5I+iS5cuUm1du3aVqr2RI0eSnZ0d3bhxg6qrq6m6upquX79OTZs2JV9f3x8+T0Foa2sz4wsb3r17Ry4uLsy94d/T0aNH07Rp04iIe21w6tQp0tfXZ+0zMjIyZGJiQikpKUREpK6uzryjcXFx1KtXL9LQ0KDMzEyRc8vIyCA1NTWpr1Wad00SNDU1KS8vT+Tzhw8fkqamplTnMGnSJLKxsaHExERSVlambdu20aJFi6hBgwa0a9cuatasGcnKypKLiwvt3r2bvn79KtJG/fr1SUlJifr160cHDhxg3YcLqamppKqqSjY2NjRmzBgaM2YM2djYkJqaGl26dInz91ZWVhQUFCQyRvChqKjIOtbm5+eToqIiFRYWkqOjI8nLy5OFhQVZWFiQvLw8OTk5UVFRkVTXUF5eTqWlpcy7/uTJE1q1ahWdPn2a2cfY2JiuXr1KRMJjwqFDh8jCwoJ0dXXp3r17RES0detWcnBwoKqqKurSpQtpamoSj8ejoUOH0qhRo5ht3LhxFBERQW/fviUiIn19faZ/Ojo6MmuNgoICUlVVFfot2waAlJSUqHnz5vTw4UOprr0uqKyspNTUVPr48aPE/dq1a0dt27alhIQESklJYZ3PuHD37l0yMDAgNzc3UlBQoEGDBpGNjQ3Vq1ePCgoKSEtLi+7fv09E/3ke2tradOzYMTIwMCAtLS2RNaLgRlTTtx4/fiz2HGbPnk1t27YlJSUlcnJyoqlTp9KRI0fow4cPzP2IiYmh4cOHk6urK3Xt2lVo+yfgX0P7F0JPT48xtoiIWrRoITRIPXr0iFRVVTnbUVZWpmfPnhER0cyZM2nkyJFERHTv3j3S09MjohrD7sGDByK/vX//PikrKxNRjTG9cuVKxniMioqikpISof3z8/NZF72Ojo6kpqZG8vLy1KhRI2rUqBHJy8uTmpoaOTk5kZOTE8nKypKtrS0REVlYWNCFCxeIqGZA45/DfwM/MuH+6kWYkZERM4E0btyY9u/fT0REubm5pK6uLrJ/VVUVVVVVSXmF7KhtSEdERFBsbKzIfrGxsbRs2TIiIpo3bx4ZGRnRypUrSUlJiRYtWkR+fn6kq6tLa9asoZiYGGrYsCGFhYVRYmIiJSUlCW2/Ajo6OhIXk0ePHqUOHTrQ3bt3pWrvy5cv9OnTJ6FNGnTu3JmOHDki9vvTp0+TgoICtWrVijFaW7VqRYqKipScnMwYn5cvXyZVVdUfMj5VVFTo8uXLQp85Ojoy75i4TU1NjdTU1Jh+qqGhQSoqKsz3qqqqpKGh8csmqoYNG9L169dJTU1N6Frz8/NZ+/dfFaGhoaSvr08bN26kO3fu0J07d2jDhg2kr69Pv/32m8j+ZWVldOvWLWYRR0SkqqrKLCwEF4tPnjwhRUVFZr+MjAyKj4+n+Ph4VsOECx8/fiRlZWXy8/NjnBxt27ZlPU8uiHsvqquraebMmaSkpMQYuCoqKhQeHl7nY9SGmZkZmZubk7m5OfF4PDIxMWH+Nzc3Z/Zr164dderUiU6ePElZWVl0+/Ztoe2vAjbnNI/Ho379+nEumqXF1KlTGWcqG0aOHEk9e/akoqIiob53+vRpZi7mWhtYWlrSpEmT6NWrV6zHUFVVZdYfxsbGlJaWRkREjx8/JlVVVerTpw8NHjyYcdYQ1SyWBw4cSG5ubj903dXV1azvmiRMmTKF1ZE4ffp0mjRpklRtcDkViIgyMzMpICCA9PT0SEtLiyZMmEA3b95k2tiyZctPPf/v37+Ti4sLpaam0pw5c5hgwJw5c+jFixfMfm/evKHLly/T5cuX6c2bN0JtCDo/2WBnZ0fr1q0T+Xzt2rVkY2NDRDXPIDk5mdauXUtr164VChpJg+7du9OmTZtITU2NsrKyqF69etSgQQNSUlKijRs3ElHNs+nQoQMVFxcz9/vKlStkYWFBCxYsEFr7Dh48mHE2FxYWkrKyMoWEhAg5E9iMeS8vL2revDn5+fmRiooKvXv3joiIkpKSyM7OjvM6bt++TWvWrKH+/fuTnp4e1a9fn4YPH07R0dFChndeXh5FR0fTokWLKDw8XGjjApdxSlTzHvKDVz+DkpISWrx4MQ0ePJh69epFc+bMoZcvXxIR0ZAhQ8jf35+Iauayx48f0+bNm6lLly40atQo2rFjh8SNqMbOOXfunNjj83g8MjAwoKVLl7I6LiZPnkyqqqo0ZMgQCgoKoqlTpwpt/wT8mzr+C1FWVoZPnz4xaT0ZGRki30tT76Kmpob379+jYcOGSE5OZlLglJSUmJpvDQ0NFBYWitT1FBUVQV1dnTV9TDC1nJ+SZWlpycqCKk1qy/v375ma4datW2PFihVQUFDAli1b/quEBSNHjuRM/a7NpC1IciItu7kkSNKTFby/kmrJxels8/HixQvUq1cPY8aMAfAfxs1t27Zh+fLliI6OZk3LsrOzw7BhwzBr1izs3r1bIjM5n8xMMO2OBGpjuFLQpUnBHDt2LPbs2SNWooavu9isWTOx9ddcdYTSpFVNmjQJ06ZNQ1FRESthYGhoKIKDg7Fs2TKRz2fNmoX3799j3759aNOmjVCqnp2dHZPqyAUTExORGqu6pB6HhYUhKioK6urq2LlzJ5Pa9vHjR4wePRodO3aUSl+dC2/fvmVNkysrK5Oa6beqqgo7duxgrc0HUCfiQ0kQp8vr4OCAnTt3SlQgEOTUEISg9rSGhgaKi4thbm4utE9WVhaMjY3x5s0bVqmcrl27IiEhQep0OS0tLcjKymLixIl4+vQpkpKSsHfvXqSlpWHJkiV1uCPi69N5PB6WL1+OefPm4cGDB1BWVoaVlRVr3XZdMXXqVObvmTNnwtvbG4WFhTh9+rQQmeLt27dx69YtsYSAfxVwlWhIi8TERLHvYocOHbBixQqcOXOGlVU8OTkZZ86cESp3AGrmkWfPngHgXhu8fv0a06ZNE1s/bGFhgSdPnqBhw4Zo0qQJ9u/fj1atWuHYsWPQ0tLC8uXL0alTJ1hbW6Njx44AaqTrPn/+XOd3+GfJzCTVcAuWDoiblz58+MCsVTQ0NJixsEOHDkwpjZOTE5ycnBAZGYljx45h+/btaN++PZo0aQI/Pz+MGjUKmpqaEtP5JUFeXh7Z2dkwMjLC4sWLRb4vKytDQEAA4uLimDFTVlYWPj4+WLduHVRUVNCzZ09kZGSIXXdNmzYNU6ZMwdu3b5l09vPnz2PlypVM2Q6Px0P37t1/WFEjMzMTq1atAlCT5luvXj1kZWXh4MGDmD9/PiZOnMiqEFNVVQUvLy/MnTsXBw8exJEjR9C/f3+cOXOGWW+8efOGkb5jSy1/9+4doqKiMHHiRGzYsAFz585FUVERDh48yNQe37p1S4jw9u3bt0Lki/xxuVmzZmjWrBlT+saXzZo8eTJDRLl161ZMnDgRenp6MDQ0FEnV5yJDa9q0KR4/fiwyhwiiZcuWKCoqYhQOfgSFhYUwMTHBnDlzWL+LjIxEz549YWtri69fv8LLywv5+fnQ09PDvn37MGPGDIa4TJwSwuLFizFjxgyxxMtZWVkS9b4TEhKwf//+n5KF+6vjX0P7F8LCwgKZmZlo2rQp6/cZGRkSXyw+unfvjrFjx8LJyQl5eXlMB+Rr/gI19Px+fn5YuXIlU5ty9epVhISEYPjw4QgODoanpye2bt3K1I1WVlZi7NixmDp1KvT19dGqVSsR+ZYVK1bg5s2bCAwM5Ky7aNOmDaMJu3DhQvTp0wcdO3aErq4u9u3bx3mdP4O6SIQdO3ZMIpP2rzC0V61aBTMzMxQVFWHFihUMG2VxcTEmTZoEgLuWfN26dUJt1tbZ1tDQkGhIv3r1ilWqRpDYiItQjc0RJMgSn5WVJfYeSGtwcUnUSFN/zVVHKA2GDRsGQJi0jU/qx+PxIC8vj/3794v8bsyYMVi9ejVkZGR+2viMjIzEzJkzsXnzZubdrisBSWRkJJKTk4Wko7S1tbF48WL06NEDnz59kigLJw2cnZ2FWJX51xcTEyOiMy4OQUFB2LFjB9zd3dG0aVOp75G04NLlraqqkkg69OHDB2RlZSEzMxNVVVXMAicvLw+ysrJo0qQJNm7ciC9fviAgIAAnTpxgat6uXr2KGTNmwMfHBwEBAWKlcvjSiVw4dOgQLl68CAMDA7Ru3Ro6Ojro0KEDPD09YWlpKVLLyceKFSuY+8pl0AvW/6upqYkwDv8sgoKCmL/Ly8sxceJEaGlpYcOGDUJOaFtb2x9WdvizwVXnLciFIo2Tce3atZgzZw5GjRqFpKQkjB49Go8ePUJ6ejomT56Ma9euMQoH9+7dE/otj8fjJDcCuNcGnz9/lighNnr0aNy5c4epTfXw8MD69etRUVGBqKgo2NraIjs7Gxs2bMDt27ehrKwMHx8fTJkyRYgojgtc8yFXjTVXDbfgfRMHLqeCIIgIFRUV+P79O4gI2traWL9+PebOnQtTU1Pk5OQIaRX7+flJrVXM51Nhm7umTZuG1NRUHDt2TEj6y8/PDy9fvsTEiRPh7u6OkJAQ5OTksBJBjhkzhiH84tfrmpubY/PmzfDx8UFgYKBEsjRp5uTy8nKoq6vD1NQUV69exYABAyAjI4M2bdowTiAFBQVs3boV8+fPx927d1FaWgonJyeGj2X+/Pnw8vJiasP5/SI5ORlOTk5IT09nziUxMZHVmNfS0sL69etFzo+vuMLluFBWVuaUzeKTwkoraVobXMaphoYGYmJiMGHCBLx48QJNmzYVeaZ82VhJMDc3F8vSbm5ujqqqKty5cwcJCQnIzs5GaWkp/Pz84O3tDWVlZSgqKmLZsmXw9/dH/fr1GQNZkDGci3i5qqpKouOiXr16v1T6+C+J/2U4/Z+GuXPnkomJCWtKVnFxMZmYmNCcOXM42/n48SNNnjyZPD096dSpU8zn8+fPp8WLFxMR0bdv3ygwMJCprZaRkSFFRUWaOnUqff36lTN9TE9Pj7Kzs0W+z87OJgMDA6lSW9jw/v17qq6urvPv6oq6pH5z1S/9tyBNLXlt5OXlkaurK50+fVrsM3n06BEpKiqSpaUlxcfHi3wfFxfHpGo2btyYbty4QURE7du3Z2qJExISSF9f/4evrS74Fan60qT8cUGwFp5ta9CgAVMCIIh9+/aRiYkJdezYkdauXUtE/0m7IqpJaezZs6dU56ClpcW8w2pqaqw1UFxQU1Nj7oUgLly4QGpqamRhYUHHjx9n9uWn7a9Zs4aGDx8u1TEuX75MampqJCcnR4qKihQUFETdu3cnVVVVysjIkKoNXV1dOnHihFT7/gi8vLyoffv2lJ6eTqqqqpScnEzx8fFkbW3NXH+rVq0oICBA5LdTpkyh1q1b06pVq2jAgAFCadYlJSU0aNAgWr16NZWVlZGHhwcZGxuTnJwc8Xg8kpeXJx6PRyNGjKDKykrS0NAQSi3lY/jw4aSgoCBVXb2+vj4NHDiQ1q1bJzROm5mZCW2qqqrE4/GY/gKA5OXlSUtLi0JDQ0lDQ4PatGnDtNu2bVuSk5NjUm779+8vcZMGcXFx1K5dOzIyMmI4D1atWiW2LOPRo0dC5Qbnz5+ntm3bUkpKCr179+6HykD+LPzqEiNra2tmDhBM/Z43bx5NnjyZ8/e9evWiuXPnMr9//PgxVVVV0eDBg2ngwIFExL02KCsro969e5Ovry+tXLmS1qxZI7TVxtOnT+ngwYMMHwuRaFlaZGSkSFkaF35kPvzViIqKYq757NmzpKSkxHAjrF69mohqSkAmT55MOjo6ZGRkRLNmzRIqs2vZsiUpKChITOfnwpQpU0hDQ4NatGhB48aNExoLlJSUWMd3Pg8DF1eDjIwMlZeXM+ufN2/e0N27dykqKopJua5fvz7rOH7r1i2peSfs7e1pzZo1VFhYSBoaGnTt2jXm/tWrV0+qNohq1sqZmZlCpXVpaWn04MEDztRyPiSVTY4bN44sLCzo5MmTzBhz4sQJatSoEU2YMIG0tLRITk6OWrRoQdOmTaOjR4+KlAaoq6tLTNXnQu3nw8ZLcf36dabsRnDfunBX8Hg8kTIDopp3WkVFRerzff78Oe3Zs4fGjx9PTZo0IRkZGaZfcHGjVFdX061btygyMpI8PDxIW1ubZGVlmXrtlStX0qRJk/4rdsP/Cv9GtH8hZs6ciYMHD8LKygojR45kZFUePnyIXbt2wdjYWCoPGJdHDqjxDK5ZswZLly5lvLmNGjVivN1c6WOfP39mZUqVl5fH58+fpUptYUNdPNo/g7pIhKmqqkpk0v5VePToEVavXs2kFdra2mLq1KlMOldFRUWdZcSsrKywbNkyjBgxAiYmJrh69arIM+EzBPv7+2Pq1KmoqKgQSg+bOXMmpk+fDgCszOSRkZH49OkTpk2bJjZSxockyS5pUZdnxya5wk/x40r548KePXuEUvH52LZtG96+fQt/f3+MGzcOjx8/FooMLV++HNOmTUPXrl3Rq1cv5OTkoLKyEmvWrEFOTg6uXbuG1NRUqc6BLVIwadIkxjPM9T59+PAB/fv3x+jRoxEZGSmkIBASEoIBAwYgMTFRYhaDNOjQoQNu374NZ2dnmJqaMuUn169fZ9rmgoKCwp/qub5w4QKSkpLg7OwMGRkZmJqaonv37tDQ0MDSpUvh7u7OqUAwbNgwnD17VijVWlNTEwsWLECPHj0QFBTE/P348WPWqIw4qZz8/HxUVVUxWSGZmZmorKwUiZy3aNECb968Yb1GQV3rPXv2YOPGjYiNjWXaePjwIfz9/TF+/HikpKQgMDCQiWDx4ejoyGjFsjGO1wWbNm3C/PnzMXXqVCxZsoQp2dDS0sLq1avRt29fkd8kJiYK9etu3boBkJ4t/r+JXy1FWVhYyIwlysrKDIv5yJEj0aZNG9Z5XxArVqyAq6srMjIy8P37d8ycORP379/Hhw8fcPXqVVRVVeHGjRtYsGCB2LUBP91anIRYYGCgWPlFoGZ8ql2WtmrVKkRERAiVpXHhR+bDXw3BUqhu3bohNzcXt27dgqWlJRwcHGBvb4/c3Fz06NEDsbGx8PDwYGTS+Hj69Cm+f/8uMZ2fC4LR+by8PKHvvn//zprmf/fuXbRq1YrJKpSEHj16YMCAAZgwYQLk5eXRrVs3oZTr9+/fs44FGhoaTLYJVwnSli1bJEajAWDgwIFiMynT09Nx4MABGBoawtDQUOh7fj+ztLSUmFoOsLPuC5ZNHjx4EImJiYxMIlATlVVWVsaQIUOkks0aPHgwkpOTMWHCBMk3Xgy2b98OExMTkb5UXV2NwsJCADVZCE5OTti7dy/q1atXpwwwfiYOj8fDvHnzhNa+VVVVSEtLg6OjIwAgPj4e0dHRePz4Ma5fvw5TU1OsWrUKFhYWzPhdW+pMkDFcUCKPDbX1vv39/YX0vvv37y9R8vDQoUNSX/dfFv9rS/+fhg8fPtD48eNJW1ub8UJpa2vT+PHj6f3791K3c+nSJfL29qa2bdvS8+fPiagmclCbOCk/P59Onz5N5eXlRESMVyggIIAaNGhACQkJVFhYSIWFhbR3715q0KABBQUFUcuWLVlJG8LCwqh58+Z06tQpcnR0pGPHjtHLly//UlGGuuK/waTNRZxF9OPkLVlZWaSurk7Lly8nXV1dsQzBP0JsdP36ddLR0aFdu3YRkWjEzMzMjFRUVJjtV0TAuFBaWkqTJ08mfX19ESZivifX3t6eYd50dXWl6dOnE1FNlFZaD7ypqSlDYCeIGzdukJmZGVVXV1NUVBQZGxsz77KxsTGtXr2aec8KCgpo7Nix1LJlS7KxsSFvb2/WTJG6gIuApDYZSVlZGU2cOJGJxMjIyJCCggJNnDiRSktLpcpiuHnzJrNP7XuRnp7+U9fDx5/tuVZXV6cnT54QUQ1525UrV4iohsRJMNpRW4FAkHRIVVWVNXqUkpLCMCqPGTOG5OXlRaLQ06ZNo99++40cHR2pXbt2QkRGz58/p86dO1O/fv2IiBjvPp95lahm7ujbty+tXLlS6NjiyP4sLCzEsj+bmZmRhoYGK0lkXl4eaWhocN5PaWBjY0OHDx8mIuEI7d27d5moBX9zdHQkQ0NDkpWVpejoaKYNadji/ykwNzdnnlmLFi0YBY8zZ85IncHy8eNHseRGRNxES/Xq1aMlS5aIJeNcsGABycjIUKtWrahv377Ur18/oe1nFU/4+BVkZn82Fi5cyKy/+EzztaGmpsa8Z4LvQHp6Ouno6Pz0Obi4uNDgwYPpy5cvzGfl5eU0ePBgcnV1laoNcWze+/fvpyZNmkhFlsZFpEokORpNRJyZlFw4cOAAycvLk4yMDHXv3p35PCIigiHi4+qfysrKlJOTI9L2vXv3pI7yRkREkJ6entRZIbUhIyNDr1+/Fvn83bt3zBpHRUXlh1U5+Jk2PB6P2rVrJ5R906NHDxo3bhzl5eXRxo0bSU9PjxYvXkxKSkpM392+fTt16dKFkzGcD0kZBMePH5doM3CxwP8TwCP6G6uA/4VBREzUQF9fn9UbdfXqVTg7O4sQzxw8eBAjR46Et7c34uPjkZOTAwsLC6xfvx4nT57EyZMn8f79ewwZMgQpKSlCdUFjxoyBtrY2li5dipCQEGzevJnxDsvLy2PixIlYtmwZkpOTGQIvwejn3r17ceDAAQwYMIA5H3F1F38XxMbGYuHChRg9ejRr/ZIgMdKPwsnJCT179hSpsWrZsiWePXuGESNGoLKyEjt27EDDhg1Za8lrk5DQ/+lsr1+/HiYmJjh58iRCQ0Oxdu1axpuspKSEWbNmCdXalpaWiiU2Wrp0qcQoLlvGhSBxHBe2b98u1X4ZGRliPeNGRkZISUnBokWLWOuvvb29sWrVKsjKyiIwMBDnzp2Dh4cHU0MXFRUlVB8qDkpKSnjw4IFIhsDjx49hY2ODrVu3omfPnqhXrx4TeWLTBq0rPn/+LPW+krzqtVFWViYUweLXfYWGhkJDQwO//fYb9u3bhxEjRsDMzIzRV1+2bBlatWqFmTNnYtCgQULnefToUaxevZqT5EjceQqOI0BN1FlHR+dP8Vy3bNkSixcvRs+ePeHp6QktLS0sXboUa9euRWJiolQEdd7e3rh+/ToiIyOZmuX09HTMmDED7dq1Q3x8POzs7JCbmwtlZWXWOu4HDx4wdfp8DeiioiI0bdoUR48eRYMGDWBsbIzk5GTY2dkJHf/evXvo0aMH8vPzOcn+VFRUkJqaKlJbffPmTXTp0gUaGhpYtmwZRo0aJfT9jh07MGvWLLx+/VrqeysOysrKyM3NhampKdTV1XHnzh1YWFggPz8ftra2mDt3LrOvjIwM9PX10aVLl7888dmfhbFjx8LExARhYWHYsGEDQkJC0L59e2RkZGDAgAGIjY3lbEOSBranpyecnZ2xfPlysfrHOjo6SE9PF1ujbWRkhBUrVmDkyJGs3/NrWGs/w5ycHDg7O6O8vJzzGgAwdbImJias86Hg+CBN/bu04MraEkRgYCAnYVvv3r3RokULLFq0COrq6sjOzoapqSmGDRuG6upqJCYm/tT53r17F25ubvj27ZuQ7rOSkhLOnDkDOzs7zhrrLVu2IDc3Fw0bNsSQIUNgZ2eHsLAwhmxr/fr1mDJlCkJCQljJ0vz9/dGoUSOsXbsW7u7uUFdXx+3bt5nPbty4wcofUxvKysq4ffu2CMFXbm4unJycxJJRCuLVq1coLi5Gs2bNGK35mzdvQkNDA02aNOHsn23btoWuri7i4uKgpKQEAPjy5Qt8fX3x4cMHnDt3TuL65NChQxIzPXk8Hh4/fizxGmRkZPD69WsRHo1nz57B1tYWZWVl8PDwwKhRozBw4EDOeyIOo0ePxpo1a8TOz7a2toiIiEC/fv2Exu979+6hS5cu+PDhA/T19REcHIwBAwYwWbqCYMsgSE9Px5cvX+qU4fJPxr+G9v8QGhoauH37tghTpJOTE4KDg+Hj4yPU+bOystCrVy+8evUKPj4+ePPmDWJiYmBjY8Psc+bMGUybNg33798HUENQwZY+BgAnTpxAREQEQ2ji4OCAsLAwdO7cmTP1lStd5K8E/mDMhl/lNFBSUsLdu3eZ9FE+WrdujfT0dM77xePxcPHiRZHP9PX14eLigsjISIboTJIhzQUzMzPs2bOHSV/kIy0tDcOGDRNKTf2zkJCQAB8fH/Ts2RPJycno0aMH8vLy8Pr1aya1PS4ujjEWMjMzYWlpifj4eOzduxcnT54UafPZs2dCKX/SwMrKCmFhYRgxYoTQ5/Hx8QgLC8OrV6/w4MEDmJqaMt/9CiNZRkaGMw1M0KFVVVWFI0eOMCUJdnZ28PT0FEk7kxY3btzAtWvXYGVlBQ8PDwA1KeXZ2dlCYxH/PKurq8W+Q1yOtz/DSSMOu3btQmVlJUaNGoVbt27Bzc0NHz58gIKCAnbs2IGhQ4ciOzub9bd8VnEdHR2EhoYiLi6OcVDKycnB19cXq1atgqqqKkJCQpCZmYnDhw8zz/jTp08YO3YsOnToAH9/fwwftLjzqQAAVWdJREFUPhwvXryAr68vgBp2an6KNFDjsDl27JhQ+iJQk6rs6ekJHx8fTmeTh4cHXrx4gZiYGGYxc+vWLYwbNw7GxsZo164dwsPD4e/vL1RSsGHDBhgZGYllnRaEIGEaG2xtbbF06VL07dtXaK5at24dtm/fzvl7QUhii/+noLq6GtXV1QxB6b59+3D16lVYWVkxab2ScPr0aYwcORIfPnxA7aUb/z08ffo0Zs+eLZZoKSwsDPr6+vjtt99Yj6Grq4ubN2+KNcTr1auH+Ph49OjRQ+jzM2fOwMfHR2oHjiDJpiTweLxfpkgAQOpyOB6PhxEjRrAStq1fvx7BwcFYuHAh7t+/DxcXFzRv3hwXLlyAp6enUDq/uPtYF5SXl2P37t3Izc0FUDOe8AmrAMDY2BhHjx5FixYthH6XmZkJT09P6OjoYOzYsejfvz+aNm2K06dPo23btrh16xbc3d3x6tUrbNq0CUuWLMHLly+Z+xQWFsaQxaqqquLBgwdo2LAhjIyMcOLECTRv3hyPHz+Gk5MTU5IkCa1atUKfPn1ESDgXLFiAY8eO4datWz99r7j657lz5yQ6Lu7evStxffIz8xQ/pZvvvGBL6ZaVlcXVq1exZcsWLF68GGPGjPnTAkSSHKV8JRo+Y/jly5dFGMMbN26Mjh07wtLSkpV4+fHjx7h06dJPn+ffHf8a2v9DCHZsQaioqCAnJwdmZmZC+zx+/Jih4Tc0NMSZM2fQrFkzkX0cHBwwZMgQrFmzRiQCx2dc3LZt23/zUv/xMDExQVRUFAYPHiz0+f79+zFjxgym7kZa8CMVkpwEPwK2KO60adPw6dMn7Ny5k7MGOyoqCpWVlbh48SIePXoELy8vqKur4+XLl9DQ0GDY1iXBwcEB48ePx+TJk5m+a25ujvHjx8PIyAiRkZHIyclBw4YN0aBBAxw6dAitWrXCkydPYG9vj9LS0p++D0BNXdiKFSvw+++/s9a0nzlzBlOnThWS26qrkcwGaeu3gZoFVO/evfHixQuhOlwTExOcOHGCcxFXUVGB8ePHY968eRIXmLq6ujh+/LgQg3hqairu3buH0NBQHD9+XOJx/oqOt/LyciaKo6enB0D4+fGnPsHnKS8vj6FDhyIyMpJZcFpYWAj1a2NjY5w9exa2trZCx7t//z569OiBFy9eIDMzEz169BDLpu3j44PLly+z1tV37NgRKSkpnM6mt2/fwtfXF6dPn2YWYZWVlejZsyd27NgBAwMD7N+/H2vWrBGSo6pfv75YZYza4GLAj4mJwYIFCxAZGQk/Pz/ExMTg0aNHWLp0KWJiYjBkyBAUFBSwRl87deoEQDq2+H8SxEWkeTwe4/gSBysrK/To0QPz588X6ygRnDPYstEmT56MuLg4NGvWjFX1QV5eHmpqamL5GwIDA3H48GFWVvOBAwdKxVD9VwTbeKCvr4+1a9cKyUIBwN69exEQEIDi4mK4ublh6dKlOHv2LO7cuYPS0lI0b94ckydPZlUBqSvevn0rVkHg7t27sLe3h5KSEu7duyfCgVFQUICmTZti165d8PLyQlVVFVxdXZGcnAygJsPt0qVLOHToEIgIKioqePv2LV6/fs2McT179gRQI4EVFxeH1q1bo0OHDujTpw9CQ0Oxb98+BAQEiOWVEMSxY8ckZlLWRdpSHKTpn5IcF1zrE0GupLqC71xKTU1F27ZthXiSFBQUYGZmhhkzZsDKyuqXBYgkRedzc3Pr5CjlM4bv3r2bkTr7kQyX5s2b4/z589DW1oaTk5PENVVdnLV/VfxLhvYXhKGhIQoKChi5Hz6uXLnCGOVcMh87d+7EsmXLRAztL1++IC4uDhMnTkR1dTVat27NfJednY2ysjLIy8uzEqUJ4p8UZfgV4CLOkhY/qyvKBTZCtaysLLx+/Rry8vKc8l3Pnj2Dm5sbCgsL8e3bN3Tv3h3q6upYvnw5vn37hs2bN3Oew6NHj+Du7g6gZnLhp9kGBwfDxcVFKskVcel/gnrHnTp1khj1DQkJwfv37zFp0iSRVPzZs2ejUaNGmD59Op4/f85EhmJiYpjf/2ikoi5Gae/evdGoUSPcuHGDIZB6//49RowYgcDAQCHJLTbIy8vj4MGDnKRnPXr0wOzZs5GUlMSQ4jRr1gxhYWHo1asXOnfujMuXLyM6OhqPHj1CYmIijI2NER8fX2fCxDdv3gjpl7JJpP0KqKioiKStHT58GLNmzUJISAhj4N68eRORkZEICwtDZWUlQkNDsWzZMqxcuZK13U+fPuHNmzcihvbbt2+ZjIfCwkL88ccfmDFjhoiBGRUVhc2bN2PGjBnw8vJCRUUFgJrIuZ+fH37//XfUq1ePk+xPX18fJ0+eRF5eHrNgbNKkiVB635AhQzBkyJC63zwpMXbsWCgrK2Pu3LkoLy+Hl5cX6tevjzVr1sDMzAyWlpZ49uyZ2OgrUKO3XVJSgrS0NHTp0gWHDx/G69evsXjxYqmkkf5O4Eek2coBpFk8c2lgA9wEbgsWLJAoIWZvby9RfnHlypWMLCZbWdrfDZLmXC7CNr4Gtra2NqtW8a+Avb09YmNjmTmTj5UrV2LevHn48uULLC0tcfr0aUyZMkVon1OnTsHCwgKDBg1Chw4dmJRrPlxdXdG/f3/07dtXIlnaxIkTWYlUY2NjmRIkaeDh4YEjR44gIiICiYmJjGF77ty5X+as5eqfly5dQrt27eDv7y/0u8rKSly6dIlzfRIeHi5Selcb4gJZ/HeTK6UbAKvUal3BlT04bdo0TJ48GV+/fgUR4ebNm9i7dy/jKCUiTqkzLuJlNvTt25fJxPwVzpW/PP6rFeH/QgiCxBmCiIiIIFtbW7px4wapq6vT5cuXadeuXaSvr8/ICYmT+ejXrx95eHgQj8ejgoICIQKdDx8+0M6dO8nIyIhatmxJBw4cEDouj8ejbdu2UatWrYSkBNjkIv7qWLNmDUMeUpuooq7EFdJAGuIsLsybN49UVVUpNDSUkpKSKCkpiUJDQ0lNTY3mzZv3S86Ti1CNC3379qURI0bQt2/fhPpvSkoKWVpaSnUOxsbGDCGKvb09I/Fy7do10tDQkEpyRVDaSEdHh3R0dIjH45GqqirVq1ePeDweNWrUiAoLCznP548//qCbN2/S3bt36evXr8zn4vr+n/EOlJWV0YMHD+jOnTtCm4qKCit5zO3bt0lVVVWqtn18fCgqKkriPs+fPycLCwvS1NRkSFO0tLTI2tqaCgsLKTExkZSVlWns2LGkqKjIPPd169ZJLaf26dMnGjFiBCOLxePxSE5Ojry9vessDcSHNFJZ/I2oRoqHL2kjiNOnT1PLli2JiOjw4cNkYWEh9pheXl5kbm5Ohw4doqKiIioqKqJDhw6RhYUFjRgxgpYsWUI8Ho+UlJSoc+fOEmWgSktLmWddWlrKfP4ryP6Iakhqtm7dSrNnz2bIOG/dusUQPAnuExoaKnYfaVBWViZE8NOsWTMaPHgw5eTk0MePH6mkpERo48PQ0JDS0tKIqIbQ7uHDh0RElJSURO3bt6/TOfzVYWlpSZMmTWKVAJUGo0ePppiYGIn7PHv2jHXOqa6uZqSRJEFaGbOysjLKzs6m7Ozs/7l05o+Ca86VhrBt6tSpNGvWrD/tHJcvX06Kioo0YcIEKi8vp+fPn5OLiwvp6+vToUOHiIgoNjaWlJWVaf78+QyJ4Lx580hZWZm2bNnCeQwusjQ2XLt2jSIjI+no0aO/7mJ/IcT1Ty4iMq71CRGJEAS6u7uTqakpaWpq/jJS2PDwcLHbwoULpWrD3t6e1q9fT0T/sTeqq6vJ39+f5s+fT0REu3btIktLS6F1K3+MkUbqjIt4mQs+Pj7/ONLL2vjX0P4fQpyhXV1dTYsXL2YMCf6ijW9YE9WwuhoYGJCbmxspKCjQoEGDyMbGhgCwMjXzN1lZWabt2sd++vQpPXr0iNTU1Dg1hv/qMDMzo3fv3jF/i9v4+tK/Ep8/f6bPnz/X+Xf/DV3RH2EmF4SOjg7l5uYSkXD/ffLkiRCzsyQMHz6cIiMjiaiG1VVfX5/Gjh1LpqamrJMUm47rnj17qEuXLowmNFENA7+LiwslJCRQUVERtW/fntGW/RGw9ftTp07R48eP6enTpyJGce1NGrx584bc3d3Fvq/a2tqszOhXrlyRmqV40aJFpKWlRQMHDqSIiAixjqbS0lKKjo6mSZMm0fTp02nnzp30/ft3IiJydHSknTt3EpHwc8/MzJRaI3XIkCFkZWVFp0+fZpx/p0+fJmtraxo6dKhUbdSGlpYWdejQgTECuAwEJSUlhgFXEA8ePCAlJSUi4u7Lf/zxB40dO1ZIp1hBQYH8/f2ptLSUDAwMKDw8nLKysn7omoik0/clIioqKqINGzbQrFmzRBwLd+7cIX19fbK0tCQ5OTnmmc2ZM4dGjhxJRCTVPj8KaVlzpWWL/ydAXV1daMyqK6TRwJaG0fhf1IBrzuXrW9vZ2TGMyk2bNiUNDQ3GCHd0dCQFBQVWDWw2I/1HkJmZSXZ2dmRpaUk6OjrUq1cvKi4uFtpn48aNQo5+CwsLZszmgrT61D+L/4a6BRfEaUs/fPiQ1NXV67w+4aOqqorGjRtHy5cv/yXn6ejoKLTZ2dmRiooKaWhokJOTk1RtqKioMGOrjo4O40DIyckhQ0NDoX1rO0qJuBnDiYi+fftGgYGBQvOhoqIiTZ06VShwIQ59+/YleXl5srS0pCVLlgipdfxT8K+h/T8El+j9t2/f6P79+5SWlkZ//PGHyPclJSUiMh/79u0jd3d34vF4dOjQISGZlGvXrjGdWEdHh65duybS5tWrV0lLS4v5//79+3Tq1CnG25uUlPSX9WBKA3ESHT+Lrl27inj6iGqieLWjWOKgqanJKsfz8OFD0tTU/MkzFIZgFLdv377MYMol36WlpUX3798nImGD6/Lly1LJcxARvX//numHVVVVtHTpUvLw8KBp06aJyEaIg4WFBashk5mZyThPrl69KjKZ1AUREREUGxsr9BmPx6NVq1bRsmXLfknWh5eXF7Vv357S09NJVVWVkpOTKT4+nqytren48eM0cuRIsrOzoxs3bjB99/r169S0aVPy9fWV6hi/wtGkrKzMTNiCz/3Ro0ekqKgoVRsqKioi8oRENVKG0sqq1AaPx2MWB+bm5oxzTRwcHR3J19eXvn37xnz2/ft38vX1JUdHRyKqcWKYmZlxHvuPP/5gnCqC47OhoSHreyyI0tJSmjt3LrVt25YaNWpE5ubmQlttsDmbzp07RyoqKtS0aVOSk5MjR0dH0tLSIk1NTeratSu5urpSSEgIEQk/s6tXr5KpqSkRkVT7SMKrV69oxIgRZGRkRLKyskJOIgB06tQpzjacnZ2ZLAMPDw8aOXIkPX/+nGbOnPmnOEL/l5AmIi0JMTExJCcnR2pqamRqasr6LoszJJ4+ffrD79k/FVxzrqTovmDmj5aWFmcGwM/g8+fPNHToUJKTkyM5OTlG2pGP8vJyJmr75s0bunv3LkVFRbFm77DB3t6e1qxZQ4WFhaShocGsDTMyMoQcqXFxcdSuXTsyMjJiAi6rVq2iI0eOSHUctkxKIqKDBw9Sq1atpGqDC/369WNdu9SvX58aNGhAPB6POnfuLPSdp6cnmZmZUc+ePX9qfZKbm/tTaw4ufPr0ifr3709xcXFS7S9NdP5X4WcyXN68eUORkZHk4OBAcnJy5ObmRvv372cc/X93/Fuj/T8EieGh+/TpE6qqqqCjoyNUB/jhwwfIyckxdR2ampoidUF37tzBqVOn8OTJE5iYmIglVGCrySwpKcFvv/2G7t274/Hjx+jfvz/u3r0LHo8nQhTydyOo+bNrny9evChCNAHUEN9cvnxZqjZGjhyJTZs2iUiZbNmyBd7e3r/kPPlQU1NjZIG0tbWZ58rvC+LQo0cPrF69Glu2bAFQ0x9KS0sRFhaG3r17S3Vsfq0xUEPcExoaKvS9NPXXxcXFTP2VICorK/Hq1SsAQP369RlZrh9BdHS0iGTJkydPUFxcjOHDh/8ShvYLFy4gKSkJzs7OkJGRgampKbp37w4NDQ0sXboUx48fh6+vL9q2bcvUSlZUVKBv375Skw6JO8+jR4+iV69ezN+SIA1vBBd0dXVZ+5empia0tbWlaqM2tLW18eTJExgYGODp06ecdW0bNmyAp6cnGjRowPBM3L17F1VVVQzh2+PHjzFp0iTOY6upqbFyVQQHB2PDhg0Sn8/YsWORmpqKkSNHwsjIiJUM5vz58zh//jwrkdi2bdswe/ZszJgxA+Hh4VBXV8fBgwdhYGAAb29vuLm5ITQ0FNHR0SLtGhsbM+9Ieno65z6SMGrUKBQWFmLevHkwMjLCs2fPmO9evXqF6dOn49WrV6ysufx7FxQUhOLiYgA15Gtubm7YvXs35OXlsXPnTs5z+Dth/fr1GDx4MC5fvsx6T7jIKOfMmYPw8HCEhoaKzO3Tpk3DtGnTwOPxMG/ePFZGY0dHx192Lf8EcM25GzZs+B+d2X9w9epVjBgxAjo6OsjOzsbVq1cREBCAkydPYvPmzdDW1paqxloS5s+fDy8vLwQHB8PV1ZUhxUxOTmbq+Tdt2oT58+dj6tSpWLJkCbMG1NLSwurVq9G3b1/Oa8nJyWGVe3JyckJOTk5dbw0rNDU1ceTIEWhpaTEs7JmZmXj37h3q168PIsLly5fh5ubG8IMoKCigTZs2GD16NI4fP84QwLGtTyTh0aNHrOuSXwUNDQ2Eh4fDw8NDrPyeIDp16oSzZ8/C3t4egwcPRlBQEC5cuICzZ8/C1dUVr1+/xowZM5h5prZNUpd1voqKCuzt7fH582ckJyfD2toaNjY2Uv1WX1+fGb8yMzOxfft2+Pj4QE1NDSNGjMCkSZNEFH3+TvjX0P6T8O7dOzx9+hQ8Hg9mZmbQ1dUV2UecETBs2DB4eHiILPb279+Po0ePssob1YapqSlKSkoQGxsrJAs0ZswYaGpqYuXKlejUqRNMTU2ZgfT27duMNMKkSZNgbm6O8+fPw9zcHGlpafjw4QOmT58uliTor4r58+ezSnQEBwejsLAQCxcu/OG2BeWCcnJyhBanfKkVY2Njsb8XJErj8XiIiYlBcnIyq67onwVBuQou6Yrnz5+jZ8+eDPu9l5cX8vPzoauri71790p9zOrqarFsxKtWrcLbt29RXl7OGGAfP36EiooK1NTU8ObNGygrK2P06NGIi4tj+m9WVhYmTpzIsJnevXu3zkRdgnj16pUIa6ypqSmqqqpQXFwsJPv1oygrK2Mme21tbbx9+xaNGzeGvb09MjMzoaWlhaSkJBQUFAgxR9dml60NaQj4Vq1ahQkTJmDTpk0SCUl4PB4WL16MoKAgbNu2DTweDy9fvsT169cxY8YMTqI1PubOnYtp06YhPj4ehoaGAGrucUhIiNRt1MbAgQPRqVMn1K9fHzweD87OzmIJ8PhEhU+ePMHu3buRl5cHABg8eDDDng9AqgWMJMyYMQPu7u5o1KgRbG1tWfXCT506hRMnTqB9+/asbYSHh2PhwoVwdnYWa4g/ePCAeefk5OTw5csXqKmpYeHChQzZDJscXV5eHsNiLM0+knDlyhVcvnyZMeD4rO6CCzZB4iD+d4LEX7KysozEXosWLfDs2TOGLX758uUYOnQo53n8XbB3714kJydDSUkJFy9eFHquPB6P09D+/v07hg4dyupA5xNZEhHu3r0rwmjcrFkzzJgx4xddyT8HsbGxYudcwXH0V2p51wUuLi4IDg7GokWLIC8vDxsbG3Tt2hUjRoyAvb09nj9/jszMTKxatQoAkJiYiHr16iErKwsHDx7E/PnzOQ1tLrI0AFi3bh22bt2Kfv36CZHeOTs7S92vFBUV8fr1axHnbHFxMSMN9bMwNDSEl5cX1q9fz7wn1dXVCAoKgrq6OioqKvDhwwc8fPgQJ06cwNOnT3HkyBHY2NjA0NAQEyZMYOZacag9vxIRiouLceLECUbS8c/Cp0+fpJJSA2oce1+/fgVQ46STl5fHtWvXMHDgQMydOxdeXl5CjlIuRRU2DBkyBJ06dcKUKVPw5csXODs74+nTpyAiJCQk1EkHvLi4GGfPnsXZs2chKyuL3r174+7du7C1tcWKFSukJt37q+Ffea9fjPv372PixIm4evWq0OedO3fGpk2bGIkeSdDR0cHVq1dFvEG5ublo3749K2MpH3fu3EHz5s2RlpbGKSJfVlaG3bt3486dOwz74/DhwyEvLw89PT1cuHABDg4O0NTUxM2bN2FtbY0LFy5g+vTpEtmp/2rgkugQJ8EjDdjkggShrKyMdevWiWWp/F9piUoCl3xXZWUl9u3bJyRlIqjpyYUbN27Ay8tLLBvxrl27sGXLFsTExDDM3gUFBRg/fjzGjRuH9u3bY8CAAXj27Bnevn0rJG3k6uqK+Ph41KtXDykpKaioqBDR05QWbDrbR48eRUpKCvbs2YOtW7dK/L00OpctW7bE4sWL0bNnT3h6ekJLSwtKSkrIyspCfn4+J7upuMVf7X6VmZmJyspKZvzJy8uDrKwsWrRoIVW/IiJERERg6dKljFyHoqIiZsyYgUWLFnH+HqiJWhQUFODbt29o2LAhgBp2bkVFRRFvdV0kPU6fPo2CggIEBgZi4cKFYplOg4KCpG7zZzBlyhTExMSga9euqFevnsjiZfv27TA3N8fJkyfFevyNjIywYsUKiUa/oaEhUlJSYGNjA1tbWyxbtgyenp64c+cO2rdvj2HDhuH9+/fYv38/Ew2TlZVFv3790KlTJ6xevRpjx47l3EcSbG1tsXv3bsbZJRjR5gLfUaWlpYW9e/cy2RV8TJs2DXv37mWi3f8EGBoaIjAwkDUiLQ2Cg4MlamAD0jEa/4sa/BXn39pITU1lZeSurq7GkiVLmOwFvnNqyJAhsLOzQ1hYGIqKimBtbc0qsVRXcGkuf/nyhbON4cOHo7i4WCSTsl+/fowc4c9CX18fV69eFVJfAGrmvHbt2sHJyQlt2rTBunXr8PTpUzRp0kQo+r9v3z4EBwdLjNDX7jcyMjLQ19eHi4sLxowZ80ucBrUz+/jGfHx8PDp37iySbccGHx8fdO3aFZ06dWJVSVFXVxdylP4IBKWG9+zZg7CwMNy5cwc7d+7Eli1bOG2FiooKHD16FNu3b0dycjIcHBwwduxYeHl5MWPY4cOHMWbMGHz8+PGHz/N/iX8j2r8Qr169QufOnaGvr4+oqCg0adIERIScnBxs3boVHTt2xL179zjlbL59+8aaflJRUSHVYAbUTMienp6sIvJTp07FpUuXoKqqig4dOqBhw4ZM2jNfy7SqqopZsOrp6eHly5ewtraGqakpI83zdwGXRMfP4MmTJyAiWFhY4ObNm0JRIAUFBRgYGEiUmOKSYvlvg0u+y9TUFPXq1cOYMWOE0tm3bduGt2/fYtasWZzHmDBhApydnXHixAlWL2rz5s1x8OBBoYnB0tISK1euxMCBA/H48WOsXr0aAwcORE5ODhOZtLa2FnJkSbuIEgd/f39MnToVFRUVTJS8X79+TESuX79+ItE7wWuRJu2KLW323bt34PF4sLGxQVZWlkQjWRwE+1VUVBTU1dWxc+dOoQyB0aNHo2PHjqioqICbmxs2b94sNj2Lx+Nhzpw5CAkJQUFBAUpLS2FrayuVbjoff5aMh5ubGwDg1q1bTNRCEvLz85GSksKaTTF//vyfPp+dO3fi4MGDInI8gli0aBHmz5+PnTt3sso0fv/+nZEJFIc2bdrgypUrsLGxQe/evTF9+nTcvXsXhw4dQps2bRAZGYlBgwbBwMAAX758QefOnfHq1Su0adMGS5YsAQCp9pGE1atXMynqZmZmP5TlsXv3bgwfPhzHjx9Hhw4dAAABAQE4ePDgX258/FlIikhLg6qqKqxYsQJnzpxhld6KiopispIKCgrw6P+1d+dhUZXv/8DfLLLJKiAoKoggCkLili0qKm6Za+ZW4kpJ5YIKUn4yLRU3cCu3cAF3Q9NKU0wFEY1UBFdEAUPNrVQUcAOe3x/8OF/HYRn0zIzg+3Vdc11wZuacmwFmzn2e57nv9HS0bdsWxsbG0vsW/Z/K8PdVWtsrXV1daSaQi4sLduzYgT59+mDv3r3SyN+tW7dku+BSv359JCcnK/2P79mzR+UpwuXNpJRDfn4+UlNTlRLt1NRUFBQU4OTJkwgMDMT3339f4uj/9OnTMWHCBFy5ckVq6/ksLy8v7Nq1C0II6b7iUXFHR0fZRuaLZygUK07mhw4dii+//FKlfRgYGCA0NBQjR46Eg4MD2rVrBx8fH7Rr1w6urq6oW7duqUtYVZWdnS0tB9yzZw8++OADmJiYoHv37ggKCir3+bVq1UJhYSEGDRqEv/76q8Skv3379lJr10pJkwvCq7rg4GDRrFkzqa3Us/Ly8kSzZs1ESEhIufvx8fERX3zxhdL2zz77TFhbW5dZrKp9+/ZCV1e31Oq6Z8+eFcbGxiI9PV14eXkptSwqvr377rvi559/FkIUVYnu2rWrOHz4sPDz8xMeHh4Vf3G0SJUWHVSkvPZdjo6OJVbB/vPPP1UqICVE+dWIjY2NS6xA+tdff0kVUDMzM1Vub/WiyqvQvm/fPtGsWTOlKtotWrQQMTExL3TM3NxcceLECXH79m0hhBBhYWGiR48eCkVY7ty5I3r16iXmz5+v0j5r164ttW551unTp0WtWrWEEEXVd8sr4FUVrFy5Uujp6Qk7OzvxxhtvKFR1VbWSa3nq1atX4nvvs5o2bSrMzMyEqampaNKkifD29la4BQcHl9vCJT09XSqOlpOTIz799FPh6ekp+vbtq9AZ4vDhw+KHH34Qc+bMEfv27StxX6o8piSWlpZStVlTU1NhZWUl3UxMTMRvv/0mPTYoKEhYWFiIt956S6lzxYYNG4SVlZU4fvy4CAgIELVr15bafFUl48ePFzNnznzh56vSeuu///4THTp0kD7Ti9/Dhw8fLiZMmCDLz0GalZOTI3bt2iWWLVtWYqX5n376SVSrVk3o6uqKTp06Sc+bNWuW6Nq1qywx/Pjjj8LBwUFs3rxZVK9eXWzatEnqYLNp06YK/SyldbeQw5gxY4SNjY0IDw8X8fHxIj4+XoSHhwsbGxsxduxYYWxsLGbPni3eeeedEiusq9LWs1OnTmLZsmVCiKL2iHZ2dqJOnTrCyMhILF26VLafRS5Xr14VGzduFJ9++qlo1KiR1MZs7969onPnzlKh0xfh6uoqtmzZInJycoStra3Yv3+/EKKoBakq3XKioqJKzJmqEk4dl1GzZs0QEhKC/v37l3j/5s2bMXfu3HKnRSYkJMDX1xctW7ZEx44dARQVxjl27Jg0HbE8u3fvxrp165Smze7duxd+fn5o1aoV9PT0EBERUeIa7Ly8POTm5qJv3764dOkS3n//faSlpcHa2hpbtmyRRvheVc+uocnPz8fatWtRr169EtdhLVmyRJZjqnukTBOsra1x5MgRuLm5KUwPu3z5Mtzd3VFYWIjz588rrX3OyMiQ1m2Xp0OHDggODpZGIp/XvXt33LhxAxEREQrrr/39/WFvb4/ffvsNvr6+OHHihLR+7HmrV6+u4E9eupycHJw/fx7GxsZwdXWFoaEhAKBJkyZYvny5NApXLD4+Hp988kmp67xUWT9dbMuWLYiJiYGHh4fC9jNnzqBz5874559/yt2HmZkZfv31V/j4+ChsP3jwIHr27IkHDx4gMDAQhoaGCmvvqiJHR0d89tlnKs28eFFr1qzBnj17sGbNmhJHq4GiNdjP27Nnj/T1m2++icjISHh5eZU6cqmK8gqqqfqY0pRVrCwkJAQbNmxAhw4dcPToUXTs2BELFy7Eb7/9Bn19fWzfvl3h8UuXLsWECRNga2uLgwcPlluHoDIaO3YsoqKi8MYbb7zU77Usfn5+uHXrFiIiItC4cWPpPXzv3r2YMGECzp49+9LHIM05efIk3nvvPemcrEaNGvj3339hYmKCmjVrIiMjA0DRjMriNdbFMyb++usvmJubo1GjRrLEsmHDBkybNg3p6ekAioomTps2DSNHjpRl/3IoKCjA7Nmz8f333+PmzZsAADs7O4wZMwaTJ0+Gt7c3PvjgA3Tr1g2dOnXCnj178NZbb+HEiRPo3r07EhMTy9y/o6MjbGxsEBcXBw8PD0RERGDJkiUKo+LlrfHWtLy8PBw+fBgHDx7EvHnzUFBQAD09PZibmyM3Nxf5+fkwMTFRej+6c+dOufteunQpxo0bB1NTUzg6OiIpKQm6urpYsmQJtm/fXilmjagbp47LKCMjo8SKisVatGghvSmW5Z133sHRo0cxb948bN26VVo/vWrVKpUr740dOxYjR47E/PnzpSmICQkJCAoKwqBBg7B+/XocOHAANjY20NXVhZ6eHt59912EhoZi7NixCusqXFxckJqaijt37ihUqH6VPb8upHiabfEHhI2NDWxsbGQ76fjxxx8REBAAGxsb2NvbKxW5qSyJdmFhYYlTnq9evQozMzOYm5sjISFBKdFOSEhA7dq1S93vs0XjxowZU2Y14lWrVmHIkCFo3ry50vrrVatWASiq5u7u7i6t2Xn69CnOnDmDe/fuyX4R6NkK7c9KT08vcTqThYUFLl++XOr+nv/bLGtq+P3793H79m2lfdy+fVvliup9+vTB8OHDERYWJtVrSExMRFBQEPr27Qug6PVdvXo1/vjjjxKny8mRADxbz6AkmuhkcPfuXXz44YdqPcbixYuRnp4OOzs7ODk5Kf19JyUl4ZtvvlF6XmxsrPR1SkqKNIXuzJkzCo979jW8d+8eoqOjkZ6ejqCgINSoUQNJSUmws7NDREREuQXVVCm6VpayCv8EBARIyfKOHTvQr18/qcZCixYtSrzgZGtri2bNmmHp0qXSNm0VoVKH06dPSxcPy/q9voyYmBjs3bsXderUUdju6upaoTX09GoIDAxEjx49sHz5clhYWODPP/9EtWrV8PHHHyvUnbC3t5eKTBYrfr+Xw8OHD9GnTx989NFHyMvLw5kzZ5CQkKD0d1ae9PR0LFy4UEpG3d3dMW7cuBLXEL8IPT09TJkyBVOmTJEKPT47fb64wvq3335bYoX1v//+G2+//bbSFPD8/HwcOXIEjo6OyMvLk5YoxcTEoG/fvtDV1UXr1q1fqf+xr776CrGxsTh58iQaN26Mdu3a4fPPP4ebm5vSZ/yL+uyzz9CqVStcuXIFnTp1ki7yODs7Y8aMGbIco7LjiLaM9PT0cP369VLXYN+8eRMODg5qLf9f7MmTJwgKCsLy5cuRn58PIQQMDAwQEBCA2bNnw97eHklJSahfvz4aNGggFe9JT0+Hp6enLMUzXieaGCnThAEDBsDCwgIrV66EmZkZTp06BVtbW/Tq1Qv16tVD48aNMXfuXMybN09KaPfv34/g4GBMnDix1LVDJVUjflZJ1YhTU1NLXX9dksLCQgQEBKBBgwYIDg5+0ZdAZW3btoWRkZFUfA0o+h/38/PDo0ePEBcXV+4+wsPDERsbW+r66ZSUFMTHx5eYJLdp00al9kd5eXmYNGkSVq9ejadPnwIoqlI9cuRIzJs3D9WrVy93PbscV6V37typ8P3Tp09x8uRJREZGYvr06RoZFRk5ciRatmyJ0aNHq+0YJY1WP6ukJPtFnDp1Cr6+vtKFnQsXLsDZ2Rn/+9//kJWVhX379pVbUE2VomvPu3//vnTiWlLF8mINGjSQTl69vb0xYcIEDBkyBOnp6WjYsCHatm1b7rG0WYSqsjIzM0NSUhJcXV0VZiUdP34cXbp0KbOYKr16LC0tkZiYCDc3N1haWuLo0aNo3LgxEhMTMXToUKSmpmokjs6dO0stxO7du6dURKy8yuZA0YzKnj17omnTplLHhYSEBKSkpODXX39Fp06dZIm1vIKuZY3+e3h4lHge/99//6FmzZooKCiQCnb16dMHTZo0URoVV6U1oiYUr+sODAxE3759ldat+/n5SWu25brQQcqYaMtIT0+vzLYoN2/eRKNGjSo0cvPo0SOl/swVKW6Rl5cnjeI2aNBAmsrYpk0bTJw4Eb1798bgwYNx9+5d/O9//8PKlStx4sQJpavtVDZzc3MkJyer3FP4VVXcvksIgYsXL6JFixZS+674+HjY2toiJCQEixcvlv4ujYyMMHny5DJH7V+kGvGLuHDhAnx8fDRSqfjSpUvo06cP0tLSULduXQDAlStX4Orqih07dqg09dXBwaHMqeGXLl0qN0lWVW5ursJ7gVxXtF/Wxo0bsWXLFqVEXB1CQ0MRHh6O7t27v1AfY7kUFBRgwYIF2Lp1K7KyspTe41WZsufr64tmzZph7ty5CgnVkSNHMHjwYDx48AB//fVXmSdQ1tbW5T7mec9eUC5tloIQAkIINGvWDN7e3ti0aROysrJgbW2NX375BV999RU/Y9TkvffeQ/PmzfHdd99JF0sdHR0xcOBAFBYWIjo6WtshUgXY2triyJEjcHV1RcOGDbFkyRJ06dIFqampaN68OXJzczUShxzTpb29vdGlSxelJUohISGIiYmpULeJ0jxf0DUtLQ3Ozs4YN24cHj9+jOXLl5f5fF1dXdy8eVPpPD4tLQ0tWrTA/fv3ER0djcGDB6OgoAAdO3ZETEwMgKLPl0OHDklFhbUtJSUFcXFxiI2NRXx8PAwMDKSCaD4+Ppg3bx7i4uKQnp6O2rVrKxVLK01Flr9VpRlJL4qJtozKmxr5/IhdafLy8hAcHIytW7eWePW5rOeX1w6o2IABAyr1GuxXjSZGyjRFlfZdpa1bVkVoaKhUufxZxZXLy6tqX9a60d27d2Po0KElTrdWByEE9u3bJ40qNG7cGL6+vipPA1Vl/TSg/iR5xIgRWLRokVLF7tzcXIwZM0bWNe/Py8jIgJeXF3JyctR2jGJl9VXX0dFRaWmPHKZOnYqIiAhMnDgR//vf/zBlyhSpcu3UqVNVSvgtLCyQlJSEBg0aKCTaf//9N9zc3KR1c2X1KJ88eXK5j3leXFwc3nnnHejr65c5a+PBgwfYs2cPrly5goCAAKkmwzfffAMDAwNMmTJF5WOS6s6cOYOOHTuiWbNmOHDgAHr27ImzZ8/izp07SEhI4MhVJdO5c2cMGzYMgwcPhr+/P06dOoWxY8di3bp1uHv3brlriuUiRwsxIyMjnD59WimJS0tLg5eXl0o1XsrTu3dvmJmZYdWqVbC2tpbeF2NjY+Hv74+LFy+W+LziZVQ7d+5E165dFc5pCgoKcOrUKbi5uUm1NDSxJl5uKSkpWLBgATZs2KCwTPDatWs4dOgQ4uLiEBcXh7S0NNSqVQtXr14tcT9ytw+t6rhGW0ZyLfoPCgrCwYMHsWzZMgwZMgQ//PADrl27hhUrVpRbrGjt2rVS64SyrqF06dJF+royrsF+1bi4uODrr7/Gn3/+qdWRspf1bBJcVvuu0tYtq2LFihUl9oD08PDAwIEDldo7lLT++vkrquL/95jctWtXmetG5aajo4POnTu/cK9uVdZPA0D16tXh5eUlS8wliYyMxOzZs5US7YcPHyIqKkptifbDhw+xePFiODg4qGX/z8vMzFT7MVQZrd6wYQN+/PFHdO/eHdOmTcOgQYPQoEEDeHl54c8//1Tp/cLQ0FBp6vaECRPw999/o1q1anj8+DFWrlyJP/74Q6HwVnFy3K5dOxQWFpb4mGIljUY8226otNZDxd5//32lbeVNraeX06RJE1y4cAE//PADzMzMkJOTg759++Lzzz9HrVq1tB0eVdCsWbOkC64zZ86En58fAgIC0LBhQ0RERGgsDjlaiNna2iI5OVkp0U5OTi637a2q4uPjceTIERgYGChsd3JywrVr10p9XnFfbyEEzMzMFAYWDAwM0Lp1a/j7+0vb1L0mXg5CCJw8eRKxsbGIjY3F4cOHcf/+fXh5eSm8d1tZWcHa2hpWVlawtLSEvr5+qTNzgYq1DyWOaL+S6tWrh6ioKPj4+MDc3BxJSUlwcXHBunXrsGnTJuzevbvU537++efYtGkTHB0dMXz4cHz88cdSjztSn1dlpOxlOTk5YePGjUo9fBMTEzFw4EBZEhUjI6MKVy5/fv3181dUi9cidejQASNGjJCtl2V54uLiMH/+fIXCLsXrp1Whyvppdbp//z6EELCyssLFixcVPlwLCgrw66+/IiQkRKXq5uV5/iKeEAIPHjyAsbExNmzYgJ49e770MV4FqoxWV69eHefPn0e9evVQq1Yt7Nq1C82aNUNGRga8vb2RnZ1d7nFGjRqF//77D1u3bkWNGjVw6tQpDB48GGfOnIGlpWWpSxeSk5MBoMR+pc8qbX30s4UNy1OvXj2sWrVK+v/w8PDAiBEjpJNaUo9Hjx7h1KlTJVaSryr/Z6+Lhw8fQgghLfu7fPkyfv75Z7i7uysMmKibHNOlv/32WyxYsAAhISEKRXpnz56NiRMnVmhmTWmsrKyQkJAAd3d3hZk+hw8fxgcffCBVIi9NcHAwpk2bpvB679ixA40bN9bo6y0HKysr5OTk4I033pCmhbdp00Yq4lpSsTQfHx+0bdtWSprLU97yNznOHSo9jTUSI5VVr15d/P3330IIIRwcHERiYqIQQoiMjAyVegc/evRIbNy4Ufj6+goTExPx4Ycfij179ojCwkK1xk2Vn6GhocjIyFDanp6eLgwNDWU5houLi1i3bp3S9qioKFG/fv1Sn5eamirs7e1liUEO69atE/r6+qJ///5ST9MPP/xQVKtWTWzYsKFC+8rJyREpKSkiJSVF5OTkqCliZcW9QUu76enpiRkzZshyrDVr1oi1a9dKt6ioKPH7778r9AhXh8DAQOk1DQwMLPMmB2dnZ6l/tKmpqbh06ZIQQohFixaJQYMGCSGEaNiwofjzzz+FEEK88847IjQ0VAghxObNm4Wtra1Kx7l3757w9fUVlpaWQk9PT9StW1dUq1ZNtGnTRq1/Q8/2lS3vVqNGDeHg4CD69Okj+vTpI+rUqSOsra3FiRMn1Bbf6+73338XNjY2Jf6OivsAU+XxKvVsvn79ukhKShIFBQXStsTERHH+/HmVnl9YWCjCw8OFg4OD9DdZp04dsXDhQtnOT/v37y/8/f2FEEXvvxkZGeLBgweiQ4cOYtiwYeU+39fX95V5vV/Wb7/9JrKzs0u9X0dHR9SsWVOEhoaKCxcuvNAxTE1NxcGDB5W2HzhwQJiamr7QPqsaJtoyKu+ktfjEtTyenp4iNjZWCCFEx44dxcSJE4UQRSdqDg4OFYrp8uXLYtq0acLZ2VnUq1dPPHjwoOI/GFVIYWFhpb2o8aJJcEXMmTNHWFtbi9WrV4vLly+Ly5cvi1WrVglra2sxa9asUp+3a9cuYWNjo7Dt1q1bIj4+XsTHx4tbt27JEp+qGjVqJMLDw5W2h4WFiUaNGmk0lhcVGxsrDh48KHR0dMT27dtFbGysdDty5Ii4du2arMc7dOiQGDx4sGjdurW4evWqEKLobys+Pl7W4zzLx8dH3L17V/q6tFv79u1lOZ6JiYl0odTe3l5KKtPT04W5ubkQQojJkyeLmTNnCiGKkmt9fX3h4uIiDAwMxOTJkyt0vMOHD4sffvhBzJkzR+zbt0+Wn6Esxf+z5d1atmwphg0bJp4+fSo99+nTp2Lo0KGiTZs2ao/zdeXi4iI+++wzcePGDW2HQjKwtrYWZ86cEUII8eOPPwovLy9RUFAgtm7dWmk+Z4rl5eWJ3NxcIYQQ9+/fFykpKSI8PFzs2bNHtmNcuXJFuLu7i8aNGwt9fX3RunVrYW1tLdzc3MTNmzfLfX5Ver3Lk5ycLBYtWiT69OkjbGxsRO3atcWgQYPEihUrVE68hwwZIpycnMS2bdvElStXxJUrV0R0dLSoX7++8PPzU/NPUDkw0ZbRjh07Sr1NnjxZGBsblzkqmJ6eLgoKCkR4eLhYtGiREEKIffv2CSMjI2FoaCh0dXXFwoULKxRTVlaWmD59uqhfv75wcHBgoq1GkZGRokmTJsLQ0FAYGhoKT09PERUVpe2wKuRFk+CKKCwsFMHBwcLIyEi6AGViYiKmT58uhFAedRw/frwYMGCAMDU1FZ9//rkQomgEePjw4UJPT0+6Mq6vry9GjBghfZCrm4GBgbh48aLS9osXL8o2+q8ply9fVvvFoejoaGFsbCxGjRolDA0NRXp6uhBCiCVLlohu3bqp9dia9CKj1UePHhVhYWHil19+qdCx/vjjD/Hll1+KkSNHiuHDhyvctM3IyKjEka6zZ88KY2NjLUT0ejAzM5NmUVDlZ2xsLF24+/DDD8W0adOEEEXndpXt/0hTo/NPnz4V69evF0FBQSIgIED8+OOPIi8vT6XnVqXXu6KSk5PF0KFDhb6+vsqzX3Jzc0VAQICUo+jq6goDAwMREBCg0dl5rzIm2mqWmpoqevfuLfT09ISfn5+4fPlyqY/V1dVVuOLWv39/cePGDXH58mWxbds2kZKSotIxn506bmRkJPr16yd27dqlMN2H5BUWFiZMTExEcHCw2Llzp9i5c6cICgoSJiYmJY56vqrKS4Ll9ODBA/HXX3+J06dPi0ePHknbnx9p7NChgxgwYIBYsWKFNDr2ySefCGdnZ7F7926RnZ0tsrOzxa5du0SDBg3E6NGjZY+1JA0aNBDLly9X2r5s2TLh4uKikRjksnr1arF161al7Vu3bhVr166V5RhNmzYVkZGRQoii6WbFiXZSUpKws7OT5RivgvJGq588eSKGDx9e4hKNipg2bZrQ1dUVrVq1Er169RK9e/dWuGlKWlqaWLFihfjuu+/E9OnTpVv16tXF3r17lR6/Z88eUbNmTY3F97oZPny4iIiI0HYYJBNPT0+xaNEikZWVJczNzcWRI0eEEEIcP3680r1vamK0OC4uTmEWTbGnT5+KuLi4cp9flV7v8hQWFooTJ06IsLAw0aNHD2FlZSX09PSEt7e3GD9+fIX2pa3lb5UBE201uXbtmhg1apSoVq2aeP/998Xp06fLfY6Ojo5Cov3syaiqAgIChJWVlfDy8hILFy4Ut2/frnDsVHFOTk5SEvGstWvXCicnJy1E9HJKS4JfFdbW1qWuC3p+erm6LF26VBgYGIjRo0eLqKgoERUVJT799FNhaGhYYgL+KnN1dRUHDhxQ2h4bGysaNmwoyzGMjY1FZmamEELxvU3O9f/lefjwoZg7d67o1q2baN68ufD29la4qUNJo9Xm5uYvnWjb29trfcbMypUrhZ6enrCzsxNvvPGGaNq0qXSztbUVderUEZs3bxZZWVkiKytLbNq0SdSpU0eMGzdOq3FXZbm5ueK9994TQ4cOFfPnz5fqRxTfqHL56aefRLVq1YSurq7o1KmTtH3WrFmia9euWoys4jQxWvz8gFWxf//9V6VR2qr0epfH0tJS6Ovri+bNm4sJEyaIX375RVpmRfJhey+ZZWdnY9asWViyZAmaNm2K/fv3a7TE/fLly1GvXj04OztLPfFKsn37do3F9Dq4fv26UqVuAHj77bdx/fp1LUT0cl6mfZdcnu2p7ebmplAROy8vD3Z2dkrPqVmzpkr9POUQEBAAe3t7hIWFYevWrQCK+mhv2bIFvXr10kgMcsnKyiqxcr6joyOysrJkOYa9vT0uXboEJycnhe2HDx+Gs7OzLMcoz8iRIxETE4N+/fqhVatWsrcyfPr0KT799FN8/fXX0uvZunVrtG7dWuFxvXv3xo4dO6QWOS/iyZMnJb7naNKMGTMwc+ZMqe3fs548eYKgoCD4+fkhPz8fQggYGBggICCg3DaV9OI2bdqEmJgYGBkZITY2VuFvXEdHp9K0mqQi/fr1w7vvviv1bC7WsWNH9OnTR4uRVZwcLcLKI4Qo8X39v//+U6mLR1V6vcuzfv16tGnT5qVe+9zcXMyePRv79+8vsctBZem4o05MtGU0d+5czJkzB/b29ti0aVOFT7Z1dHSU3iAqeiLo5+fHPtha4OLigq1bt+Krr75S2L5lyxalnpFUttzcXIwZMwZRUVHSm7aenh78/PywZMkSmJiY4K233sI333yDqKgoGBkZAShqgzJ9+nS89dZbGou1T58+VeLDt2bNmjh16pRSEpySkgJra2tZjuHv749x48Zh9erV0NHRwT///IOjR49i0qRJsrR1UcVvv/2G3bt345133lHL/qtVq4Zt27aV+/O4urri22+/RUJCApo3b650AqhKMjRq1Chs3LhRY69dSe7evYsPP/ywxPsMDAywaNEihIaGIj09HQDQoEEDqW0OqceUKVMwffp0hISEQFdXV9vhkAwqQ89mVUydOhWDBw9GYGAgOnbsKH1Wx8TEwNvb+6X23bdvXwBF58zDhg2DoaGhdF9BQQFOnTql8oXJqvJ6l6d79+4vvY9Ro0YhLi4OQ4YMQa1atZh/lIB9tGWkq6sLY2Nj+Pr6Qk9Pr9THlTaarKuri27duklvEL/++is6dOigdBLG0ehXz7Zt2zBgwAD4+vpKJ/EJCQnYv38/tm7dWiWSMU359NNP8ccff+D777+XXsvDhw9j7Nix6NSpE5YtW4bTp0+ja9euePz4sXTVOSUlBYaGhiX2dFSHY8eOobCwEG+++abC9sTEROjp6aFFixZqj0EukydPxpYtW7BmzRq0bdsWQFGP8BEjRqBfv36YP3/+Sx9DCIFZs2YhNDRUmnVgaGiISZMm4bvvvnvp/avC3d0dmzdvhpeXl9qOMXToUDRt2rTM0eqSZg8U09HRKXUUYMKECdLXhYWFiIyMhJeXF7y8vFCtWjWFx4aHh1cw8oobOXIkWrZsidGjRwMoOtldu3YtzM3NpRPf0piamsLDwwOjR49mX20Z1ahRA8eOHUODBg20HQqRkhs3bkijxcUXgv766y+Ym5ujUaNGL7zf4cOHAwAiIyPRv39/GBsbS/cZGBjAyckJ/v7+sLGxebkfgBRYWlpi165dart4XRUw0ZbRsGHDVLqas2bNmhK3F79RvOjzSbuSkpIQHh6O8+fPAyiaRjxx4sSXvlL7urGxsUF0dDR8fHwUth88eBD9+/fH7du3ARRNH9+wYQNSU1MBFL3eH330kcIHrDq1atUKwcHB6Nevn8L27du3Y86cOUhMTNRIHHJ48uQJhgwZgp9++gn6+kUTnQoLC+Hn54fly5fDwMBA1mNdunQJOTk5cHd3h6mpqWz7Ls/vv/+OxYsXY/ny5XB0dFTLMWbMmIGwsDB07NjxhUerS9O+fXuVHqejo4MDBw688HFUFRoaivDwcHTv3h2enp7YunUrPvjgAxgZGWHDhg1wd3cv9bmPHz/G0aNH4enpiV9++UXtsb4uAgMDYWtrqzS7iuh1EBwcjGnTpkkzZy5fvowdO3agcePG6NKli5ajq3rq16+P3bt3o3HjxtoO5ZXFRJvoJZW0LpNenImJCU6cOKH0xn327Fm0atUKubm5CA0NhZ2dHUaMGKHwmNWrV+P27dslrhmVm6mpKU6dOqW0vjgzMxNeXl548OCB2mOQW1paGlJSUmBsbAxPT0+1JaPacvv2bfTv3x+HDh2CiYmJ0ijwnTt3XvoYqoxWPzsy/fz9RkZGcHFxQa9evVCjRo2XjkedXnRkvti5c+fQsmVL5Obmyh3aa2vs2LGIiorCG2+8obWZDkTa0qlTJ3zwwQcYPXo07t27h0aNGqFatWr4999/ER4ejoCAAG2HWKWsX78eO3fuRGRkJJcFlYKJNpEMLCwskJyczERbBh07doS1tbXS+uuhQ4fizp07+OOPP+Dk5ISNGzcqrblKTEzEwIEDkZmZqfY4ra2t8dtvvymtCT9y5Ai6d++Ou3fvqj0GuT158gSZmZlo0KCBNLJdlfj6+iIrKwsjR46EnZ2d0gykoUOHaiSO9u3bIykpCQUFBXBzcwNQdJFDT08PjRo1woULF6Cjo4PDhw+XOSpc2RUUFODMmTMKRYfo5ZQ160FTMx2ItMXGxgZxcXHw8PBAREQElixZgpMnT2Lbtm2YOnWqNOOQ5OHt7Y309HQIIeDk5KR0YS8pKUlLkb06mGjLyNvbu8Sp4xYWFmjYsCHGjRtXpU+aXmeqrMsk1aiy/trIyAjnz59XurCRkZEBd3d3PHr0SO1xDho0CNevX8fOnTulNab37t1D7969UbNmTakSeWWQl5eHMWPGIDIyEkBR0ufs7IwxY8bAwcEBISEhWo5QHiYmJjh69KhaEztVRquvX7+OEydOYM2aNVLF1+zsbIwaNQrvvvsu/P39MXjwYDx8+BB79+5VW6wvYsKECfjuu+9QvXr1Un9WoOjnDQsL02BkRPS6MzExQWpqKurVq4f+/fvDw8MD33zzDa5cuQI3NzeNdSV5XUyfPr3M+7/55hsNRfLqqnpDFlrUu3fvErffu3cPSUlJ8Pb2xoEDB1g0oAqSo4owFfH09MTFixcV1l8PGjRIYf113bp1kZCQoJRoJyQkoHbt2hqJc/78+Wjbti0cHR2ldfjJycmws7PDunXrNBKDXL788kukpKQgNjYWXbt2lbb7+vpi2rRpVSbRbtSoER4+fKjWY5w8ebLM0eqlS5ciOzsbO3bsUGirYmFhgWnTpqFz584YN24cpk6dis6dO6s11hdx8uRJPH36VPq6NKw+S0SapokWYvR/mEiXjyPaGjRlyhT8+eef2L9/v7ZDIZm97FpF+j+qrL+eO3cu5s6di3nz5qFDhw4AgP379yM4OBgTJ07El19+qZFYc3NzsWHDBmlds5eXFwYNGqQ0fepV5+joiC1btqB169YwMzNDSkoKnJ2dcenSJTRr1gz379/XdoiyiImJwfTp0zFz5kx4enoq/Z7kOBFbuHAh4uPjyxytNjc3h7e3N44dO6bw3NjYWPTo0QMPHjxARkYGmjZtWmVeeyIidYuOjsbgwYNRUFCAjh07IiYmBkDRecWhQ4fw+++/azlCet0w0dags2fPon379rh165a2QyE1Kv6X4ojOi1Fl/bUQAiEhIVi8eDGePHkCADAyMsLkyZMxdepUbYRdqZmYmODMmTNwdnZWSLRTUlLQtm1bZGdnaztEWRS3k3n+f1MIAR0dHRQUFLz0MRwcHLBv3z6lZUJnz55F586dce3aNXTr1g0xMTGIjo5Gy5YtARS1i5s0aRLefvttrFu3Dps3b8b8+fNx/Pjxl46JiOh1oa4WYlSkRo0aSEtLg42NDaysrMo815WjwGhlx6njGqSnp4fCwkJth0FqsmrVKixYsAAXL14EUDSdfPz48Rg1apSWI6tcbty4gVq1ailtt7W1xfXr1wEUJUpz5szB119/jfPnz8PY2Biurq5SD3pNSU9Px8KFC6UCK+7u7hg3blyl62HbokUL7Nq1C2PGjAHwf4loRESEUrG3yuzgwYNqP0Z2djZu3bqllGjfvn1bGp2eO3cu9u/fj4EDByI/Px8AoK+vj6FDh2LBggUAiqa5R0REqD1eIqKqxN7eHvb29grbWrVqpaVoqp4FCxbAzMwMQNEMLiobE20N2r59O4uhVVFTp05FeHg4xowZIyUmR48eRWBgILKysvDtt99qOcLKoyLrr01NTaURQU3bu3cvevbsiaZNm0p1FxISEuDh4YFff/0VnTp10kpcL2LWrFno1q0bzp07h/z8fCxatAjnzp3DkSNHEBcXp+3wZNOuXTu1H6NXr14YMWIEwsLClEari+t4nD17Fl5eXoiNjZWWlTg7Oyv0FG/atKnaYyUiIqqIZ7tz7N+/Hz4+PmjXrl2lG2DQFE4dl9HixYtL3J6dnY0TJ05g165d+P333+Hr66vhyEjdbG1tsXjxYgwaNEhh+6ZNmzBmzBj8+++/Woqs8nlV1l+Xx9vbG126dMHs2bMVtoeEhCAmJqbStbVIT0/H7NmzkZKSgpycHDRr1gyTJ0+Gp6entkOTXV5eHrKysqRlB8W8vLxeet85OTkIDAxEVFRUiaPV1atXR3JyMgAm00REVHn5+/sjLi4O6enpqF27Ntq1aycl3q6urtoO75XARFtGpRXEMjc3h5ubGwIDA6vUNEz6P5aWljh27JjSG0taWhpatWqFe/fuaSewSqiyrL82MjLC6dOnS/yde3l5aaTFGFXM7du3MXz48FIL4sixRrtYTk5OqaPVREREVcW1a9dw6NAhxMXFIS4uDmlpaahVqxauXr2q7dC0jlPHZZSZmantEEhLhgwZgmXLliE8PFxh+8qVK/HRRx9pKarK6VVZf10eW1tbJCcnKyXaycnJqFmzppaiUl1FqllXlbYo48ePx71795CYmAgfHx/8/PPPuHnzJmbMmCF7z2dTU1NZRsiJiIheZVZWVrC2toaVlRUsLS2hr68PW1tbbYf1SmCirUbF04VtbGy0HAmpw4QJE6SvdXR0EBERgZiYGLRu3RpAUZXsrKws+Pn5aSvESk2b669V4e/vj08++QQZGRlShfSEhATMmTNH4W/jVWVpaVluZXw5q3G/Cg4cOICdO3eiRYsW0NXVhaOjIzp16gRzc3OEhoaie/fu2g6RiIioUvjqq68QGxuLkydPonHjxmjXrh1CQkLQtm1bWFlZaTu8VwKnjsvs3r17mDJlCrZs2YK7d+8CKLrSM3DgQMyYMQOWlpbaDZBk0759e5Uep6OjgwMHDqg5GtI0IQQWLlyIsLAw/PPPPwCA2rVrIygoCGPHjn3l27tVpMiZJoqIaYK5uTlOnToFJycnODo6YuPGjXjnnXeQmZkJDw8P5OXlaTtEIiKiSkFXVxe2trYIDAxE37590bBhQ22H9Mphoi2jO3fu4K233sK1a9fw0UcfoXHjxgCAc+fOYePGjahbty6OHDnCqzxElVx+fj42btyILl26wM7ODg8ePAAAqeVFZRQfH48VK1YgPT0d0dHRcHBwwLp161C/fn28++672g5PFi1btsSMGTPQpUsX9OzZE5aWlggNDcXixYsRHR2N9PR0bYdIRERUKaSkpCAuLg6xsbGIj4+HgYGBVBDNx8eHiTeYaMtq/Pjx2L9/P/744w/Y2dkp3Hfjxg107twZHTt2lPqkElHlZWJigvPnz8PR0VHboby0bdu2YciQIfjoo4+wbt06nDt3Ds7Ozvj++++xe/du7N69W9shymL9+vXIz8/HsGHDcOLECXTt2hV37tyBgYEB1q5diwEDBmg7RCIiokopJSUFCxYswIYNG1BYWFhllp29DCbaMnJycsKKFSvQpUuXEu/fs2cPRo8ejcuXL2s2MCKSnY+PD8aPHy/1Rq7MvL29ERgYCD8/P5iZmSElJQXOzs44efIkunXrhhs3bmg7RLXIy8tDamoq6tWrx1oaREREFSCEwMmTJxEbG4vY2FgcPnwY9+/fh5eXF9q1a8eBRbAYmqyuX78ODw+PUu9v0qRJlT1hJXrdfPbZZ5g4cSKuXr2K5s2bo3r16gr3V6aK0xcuXEDbtm2VtltYWFTp1nQmJiZo1qyZtsMgIiKqdGrUqIGcnBy88cYbaNeuHfz9/dGmTRvWo3oGE20Z2djY4PLly6hTp06J92dmZqJGjRoajoqI1GHgwIEAgLFjx0rbdHR0KmWlbnt7e1y6dAlOTk4K2w8fPgxnZ2ftBCWTilSAf749HxEREZVs/fr1aNOmTZVpAaoOTLRl1KVLF0yZMgX79u2DgYGBwn2PHz/G119/ja5du2opOiKSU2ZmprZDkI2/vz/GjRuH1atXQ0dHB//88w+OHj2KSZMm4euvv9Z2eC9lzZo1aNKkCfT19aULISV51avEExERvUrYErN8XKMto6tXr6JFixYwNDTE559/jkaNGkEIgfPnz2Pp0qV4/Pgxjh8/jrp162o7VCJ6SaGhobCzs8OIESMUtq9evRq3b9/G5MmTtRRZxQkhMGvWLISGhkotrgwNDTFp0iR89913Wo7u5ejq6uLGjRuoWbMmnJ2dcezYMVhbW2s7LCIiIqrimGjLLDMzE5999hliYmKkkRMdHR106tQJ33//PVxcXLQcIRHJwcnJCRs3bsTbb7+tsD0xMREDBw6slCPeT548waVLl5CTkwN3d3eYmppqO6SXZm1tjd27d+PNN9+Erq4ubt68CVtbW22HRURERFUcE201uXv3Li5evAgAcHFx4dpsoirGyMgI58+fR/369RW2Z2RkwN3dHY8ePdJSZPSsTz75BJGRkahduzaysrJQp04d6OnplfjYjIwMDUdHREREVRXXaKuJlZUVWrVqpbQ9Ojoa/fr100JERCSnunXrIiEhQSnRTkhIQO3atbUUFT1v5cqV6Nu3Ly5duoSxY8fC398fZmZm2g6LiIiIqjgm2jLLz89HamoqDAwM0LBhQ2n7zp07MXXqVKSmpjLRJqoC/P39MX78eDx9+hQdOnQAAOzfvx/BwcGYOHGilqOjZxUXoTxx4gTGjRvHRJuIiIjUjlPHZXTmzBm8//77uHLlCgCgV69eWLZsGfr3748zZ87A398fX3zxRantv4io8hBCICQkBIsXL8aTJ08AFE0nnzx5MqZOnarl6IiIiIhIm5hoy6h79+54/Pgxxo8fj02bNmHTpk1wc3PDyJEj8fnnn8PY2FjbIRKRzHJycnD+/HkYGxvD1dUVhoaG2g6JiIiIiLSMibaMatasiZiYGDRt2hTZ2dmwsrJCZGQkhgwZou3QiIiIiIiISEN0tR1AVfLvv/9KRZAsLCxQvXp1tG7dWstRERERERERkSaxGJqMdHR08ODBAxgZGUEIAR0dHTx8+BD3799XeJy5ubmWIiQiIiIiIiJ149RxGenq6kJHR0f6vjjZfv77goICbYRHREREREREGsARbRkdPHhQ2yEQERERERGRlnFEm4iIiIiIiEhGHNFWo7NnzypME9fT04OHh4cWIyIiIiIiIiJ144i2jOLj4zFhwgQcO3YMAGBmZoa8vDwUv8Q6OjrYu3cvfH19tRkmERERERERqRHbe8lo6dKlSj2zDx48iMzMTGRkZGDcuHFYtmyZlqIjIiIiIiIiTWCiLaPjx4+jQ4cOCtvq1KkDR0dHODk5YciQITh69KiWoiMiIiIiIiJNYKIto6tXr8LCwkL6PjIyEvb29tL3NWrUwH///aeN0IiIiIiIiEhDmGjLyMzMDOnp6dL3ffv2hYmJifR9ZmYmzM3NtREaERERERERaQgTbRm9+eabiIqKKvX+tWvX4s0339RgRERERERERKRpbO8lowkTJsDX1xfW1tYICgpCzZo1AQC3bt3CnDlzsH79esTExGg5SiIiIiIiIlIntveS2dKlSxEYGIj8/HyYm5tDR0cH2dnZ0NfXR1hYGL744gtth0hERERERERqxERbDa5cuYLo6GhcvHgRAODq6op+/fqhbt26Wo6MiIiIiIiI1I2JtozOnTsHd3f3Mh8zb948BAUFaSgiIiIiIiIi0jQWQ5NRly5dkJWVVer98+fPx5QpUzQYEREREREREWkaE20Zvfvuu/D19cXt27eV7gsLC8NXX31VZlVyIiIiIiIiqvw4dVxG+fn56NGjB27evInY2FipZ/aCBQsQHByMyMhIDB48WMtREhERERERkTox0ZbZw4cP4evrCz09PcTExGD58uWYNGkS1q5di48//ljb4REREREREZGaMdFWg+zsbLRr1w5Pnz5FWloaVq9ejSFDhmg7LCIiIiIiItIAJtoy+uWXX6Svr1+/jnHjxqFHjx5KSXbPnj01HRoRERERERFpCBNtGenqll9bTkdHBwUFBRqIhoiIiIiIiLSBiTYRERERERGRjNjei4iIiIiIiEhGTLRldOLECbRv3x73799Xui87Oxvt27dHSkqKFiIjIiIiIiIiTWGiLaOwsDB06NBB6p/9LAsLC3Tq1Anz5s3TQmRERERERESkKUy0ZZSYmIhevXqVen+PHj1w5MgRDUZEREREREREmsZEW0bXrl2DmZlZqfebmpri+vXrGoyIiIiIiIiINI2JtoxsbW1x4cKFUu9PTU2FjY2NBiMiIiIiIiIiTWOiLSNfX1/MnDmzxPuEEJg5cyZ8fX01HBURERERERFpEvtoyyg9PR3NmzeHm5sbJk6cCDc3NwBFI9lhYWFIS0vD8ePH4eLiouVIiYiIiIiISF2YaMvs+PHjGDZsGM6dOwcdHR0ARaPZ7u7uWLNmDVq2bKnlCImIiIiIiEidmGirSXJyMi5evAghBBo2bIimTZtqOyQiIiIiIiLSACbaGnL//n1s2LABq1atwvHjx7UdDhEREREREamJvrYDqOoOHjyI1atXY/v27bCwsECfPn20HRIRERERERGpERNtNbh27RrWrl2LNWvW4N69e7h79y42btyI/v37S+u2iYiIiIiIqGpiey8Zbdu2De+99x7c3NyQnJyMsLAw/PPPP9DV1YWnpyeTbCIiIiIiotcAR7RlNGDAAEyePBlbtmyBmZmZtsMhIiIiIiIiLeCItoxGjhyJH374AV27dsXy5ctx9+5dbYdEREREREREGsZEW0YrVqzA9evX8cknn2DTpk2oVasWevXqBSEECgsLtR0eERERERERaQDbe6nRxYsXsXr1akRFRSEnJwfdu3dHv3790LdvX22HRkRERERERGrCRFsDCgsLsXv3bkREROD333/H48ePtR0SERERERERqQkTbTX477//YG1tDQC4cuUKfvzxRzx8+BA9evRAo0aNULNmTS1HSEREREREROrCRFtGp0+fRo8ePXDlyhW4urpi8+bN6Nq1K3Jzc6Grq4vc3FxER0ejd+/e2g6ViIiIiIiI1ITF0GQUHBwMT09PHDp0CD4+Pnj//ffRvXt3ZGdn4+7du/j0008xe/ZsbYdJREREREREasQRbRnZ2NjgwIED8PLyQk5ODszNzXHs2DE0b94cAJCamorWrVvj3r172g2UiIiIiIiI1IYj2jK6c+cO7O3tAQCmpqaoXr06rKyspPutrKzw4MEDbYVHREREREREGsBEW2Y6Ojplfk9ERERERERVm762A6hqhg0bBkNDQwDAo0ePMHr0aFSvXh0A2NaLiIiIiIjoNcA12jIaPny4So9bs2aNmiMhIiIiIiIibWGiTURERERERCQjrtEmIiIiIiIikhETbSIiIiIiIiIZMdEmIiIiIiIikhETbSIiIiIiIiIZMdEmIiIiIiIikhETbSIiIiIiIiIZMdEmIiIiIiIiktH/A8DPQL2NfNEWAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndr_reports = validator.validate(ndr_result)\n",
    "\n",
    "ndr_stats = ndr_reports[\"statistics\"]\n",
    "\n",
    "label_stats = ndr_stats[\"label_distribution\"][\"defined_labels\"]\n",
    "label_name, label_counts = zip(*[(k, v) for k, v in label_stats.items()])\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.hist(label_name, weights=label_counts, bins=len(label_name))\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c9b2d6ad",
   "metadata": {},
   "source": [
    "We export those pruned subset to train the model with OpenVINO™ Training Extensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "98035fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_result.export(\"random_result\", format=\"datumaro\", save_media=True)\n",
    "cluster_random_result.export(\"cluster_random_result\", format=\"datumaro\", save_media=True)\n",
    "query_clust_result.export(\"query_clust_result\", format=\"datumaro\", save_media=True)\n",
    "centroid_result.export(\"centroid_result\", format=\"datumaro\", save_media=True)\n",
    "entropy_result.export(\"entropy_result\", format=\"datumaro\", save_media=True)\n",
    "ndr_result.export(\"ndr_result\", format=\"datumaro\", save_media=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "745395cc",
   "metadata": {},
   "source": [
    "## Train Model and Export the Trained Classification Model Using OpenVINO™ Training Extensions\n",
    "\n",
    "In this step, we train a classification model using OTX. To see the detail guides for OpenVINO™ Training Extensions usage, please see [How-To-Train](https://openvinotoolkit.github.io/training_extensions/latest/guide/tutorials/base/how_to_train/classification.html). In this example, we use the CLI command to train the model and choose `EfficientNet-B0` model supported by OpenVINO™ Training Extensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba8c6fdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*] Workspace Path: otx-workspace-CLASSIFICATION\n",
      "[*] Load Model Template ID: Custom_Image_Classification_EfficinetNet-B0\n",
      "[*] Load Model Name: EfficientNet-B0\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details.\n",
      "  warnings.warn(\n",
      "2023-07-10 18:25:50,278 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model.py\n",
      "2023-07-10 18:25:50,307 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model_multilabel.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/data_pipeline.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/deployment.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/hpo_config.yaml\n",
      "2023-07-10 18:25:50,407 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model_hierarchical.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/compression_config.json\n",
      "[*] Update data configuration file to: otx-workspace-CLASSIFICATION/data.yaml\n",
      "2023-07-10 18:25:50.679934: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-07-10 18:25:51.257037: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/openvino/pyopenvino/__init__.py:10: FutureWarning: The module is private and following namespace `pyopenvino` will be removed in the future\n",
      "  warnings.warn(message=\"The module is private and following namespace \" \"`pyopenvino` will be removed in the future\", category=FutureWarning)\n",
      "2023-07-10 18:25:54,091 | INFO : Classification mode: multiclass\n",
      "2023-07-10 18:25:54,091 | INFO : train()\n",
      "2023-07-10 18:25:54,104 | INFO : Training seed was set to 5 w/ deterministic=False.\n",
      "2023-07-10 18:25:54,106 | INFO : Try to create a 0 size memory pool.\n",
      "2023-07-10 18:25:55,390 | INFO : configure!: training=True\n",
      "2023-07-10 18:25:55,504 | INFO : init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n",
      "2023-07-10 18:25:55,527 | INFO : 'in_channels' config in model.head is updated from -1 to 1280\n",
      "2023-07-10 18:25:55,528 | INFO : configure_data()\n",
      "2023-07-10 18:25:55,528 | INFO : task config!!!!: training=True\n",
      "2023-07-10 18:25:55,528 | INFO : train!\n",
      "2023-07-10 18:25:55,528 | INFO : cfg.gpu_ids = range(0, 1), distributed = False\n",
      "2023-07-10 18:25:55,547 | INFO : Environment info:\n",
      "------------------------------------------------------------\n",
      "sys.platform: linux\n",
      "Python: 3.10.0 (default, Mar  3 2022, 09:58:08) [GCC 7.5.0]\n",
      "CUDA available: True\n",
      "GPU 0,1: GeForce RTX 3090\n",
      "CUDA_HOME: /usr/local/cuda\n",
      "NVCC: Cuda compilation tools, release 11.1, V11.1.74\n",
      "GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
      "PyTorch: 1.13.1+cu117\n",
      "PyTorch compiling details: PyTorch built with:\n",
      "  - GCC 9.3\n",
      "  - C++ Version: 201402\n",
      "  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n",
      "  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\n",
      "  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n",
      "  - LAPACK is enabled (usually provided by MKL)\n",
      "  - NNPACK is enabled\n",
      "  - CPU capability usage: AVX2\n",
      "  - CUDA Runtime 11.7\n",
      "  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n",
      "  - CuDNN 8.5\n",
      "  - Magma 2.6.1\n",
      "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n",
      "\n",
      "TorchVision: 0.14.1+cu117\n",
      "OpenCV: 4.7.0\n",
      "MMCV: 1.7.1\n",
      "MMCV Compiler: GCC 7.5\n",
      "MMCV CUDA Compiler: 11.1\n",
      "MMClassification: 0.25.0+c5ac764\n",
      "------------------------------------------------------------\n",
      "\n",
      "2023-07-10 18:25:55,910 | INFO : init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n",
      "2023-07-10 18:25:55,919 - mmcv - INFO - initialize CustomLinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.conv.weight - torch.Size([32, 3, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.bn.weight - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.bn.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.conv.weight - torch.Size([32, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.bn.weight - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.bn.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv1.weight - torch.Size([8, 32, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv1.bias - torch.Size([8]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv2.weight - torch.Size([32, 8, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv2.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.conv.weight - torch.Size([16, 32, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.bn.weight - torch.Size([16]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.bn.bias - torch.Size([16]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.conv.weight - torch.Size([96, 16, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.bn.weight - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.bn.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.conv.weight - torch.Size([96, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.bn.weight - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.bn.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv1.weight - torch.Size([4, 96, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,920 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv1.bias - torch.Size([4]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv2.weight - torch.Size([96, 4, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv2.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.conv.weight - torch.Size([24, 96, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.bn.weight - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.bn.bias - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.conv.weight - torch.Size([144, 24, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.conv.weight - torch.Size([144, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv1.weight - torch.Size([6, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv1.bias - torch.Size([6]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv2.weight - torch.Size([144, 6, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv2.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.conv.weight - torch.Size([24, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.bn.weight - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.bn.bias - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.conv.weight - torch.Size([144, 24, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.conv.weight - torch.Size([144, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv1.weight - torch.Size([6, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv1.bias - torch.Size([6]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv2.weight - torch.Size([144, 6, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv2.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.conv.weight - torch.Size([40, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,921 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.bn.weight - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.bn.bias - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.conv.weight - torch.Size([240, 40, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.conv.weight - torch.Size([240, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv1.weight - torch.Size([10, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv1.bias - torch.Size([10]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv2.weight - torch.Size([240, 10, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv2.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.conv.weight - torch.Size([40, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.bn.weight - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.bn.bias - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.conv.weight - torch.Size([240, 40, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.conv.weight - torch.Size([240, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv1.weight - torch.Size([10, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv1.bias - torch.Size([10]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv2.weight - torch.Size([240, 10, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv2.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.conv.weight - torch.Size([80, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,922 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.conv.weight - torch.Size([480, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.conv.weight - torch.Size([80, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.conv.weight - torch.Size([480, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.conv.weight - torch.Size([80, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.conv.weight - torch.Size([480, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,923 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.conv.weight - torch.Size([112, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.conv.weight - torch.Size([112, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.conv.weight - torch.Size([112, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,924 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.conv.weight - torch.Size([192, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.conv.weight - torch.Size([1152, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv3.conv.weight - torch.Size([192, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,925 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv2.conv.weight - torch.Size([1152, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv3.conv.weight - torch.Size([192, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv2.conv.weight - torch.Size([1152, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv3.conv.weight - torch.Size([192, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv2.conv.weight - torch.Size([1152, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,926 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv3.conv.weight - torch.Size([320, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv3.bn.weight - torch.Size([320]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv3.bn.bias - torch.Size([320]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.final_block.conv.weight - torch.Size([1280, 320, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.final_block.bn.weight - torch.Size([1280]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "backbone.features.final_block.bn.bias - torch.Size([1280]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "head.fc.weight - torch.Size([102, 1280]): \n",
      "NormalInit: mean=0, std=0.01, bias=0 \n",
      " \n",
      "2023-07-10 18:25:55,927 - mmcv - INFO - \n",
      "head.fc.bias - torch.Size([102]): \n",
      "NormalInit: mean=0, std=0.01, bias=0 \n",
      " \n",
      "2023-07-10 18:25:57,371 | INFO : Task Adaptation: [] => ['BACKGROUND_Google', 'Faces', 'Faces_easy', 'Leopards', 'Motorbikes', 'accordion', 'airplanes', 'anchor', 'ant', 'barrel', 'bass', 'beaver', 'binocular', 'bonsai', 'brain', 'brontosaurus', 'buddha', 'butterfly', 'camera', 'cannon', 'car_side', 'ceiling_fan', 'cellphone', 'chair', 'chandelier', 'cougar_body', 'cougar_face', 'crab', 'crayfish', 'crocodile', 'crocodile_head', 'cup', 'dalmatian', 'dollar_bill', 'dolphin', 'dragonfly', 'electric_guitar', 'elephant', 'emu', 'euphonium', 'ewer', 'ferry', 'flamingo', 'flamingo_head', 'garfield', 'gerenuk', 'gramophone', 'grand_piano', 'hawksbill', 'headphone', 'hedgehog', 'helicopter', 'ibis', 'inline_skate', 'joshua_tree', 'kangaroo', 'ketch', 'lamp', 'laptop', 'llama', 'lobster', 'lotus', 'mandolin', 'mayfly', 'menorah', 'metronome', 'minaret', 'nautilus', 'octopus', 'okapi', 'pagoda', 'panda', 'pigeon', 'pizza', 'platypus', 'pyramid', 'revolver', 'rhino', 'rooster', 'saxophone', 'schooner', 'scissors', 'scorpion', 'sea_horse', 'snoopy', 'soccer_ball', 'stapler', 'starfish', 'stegosaurus', 'stop_sign', 'strawberry', 'sunflower', 'tick', 'trilobite', 'umbrella', 'watch', 'water_lilly', 'wheelchair', 'wild_cat', 'windsor_chair', 'wrench', 'yin_yang']\n",
      "2023-07-10 18:25:57,371 | INFO : - Efficient Mode: True\n",
      "2023-07-10 18:25:57,371 | INFO : - Sampler type: balanced\n",
      "2023-07-10 18:25:57,371 | INFO : - Sampler flag: False\n",
      "2023-07-10 18:25:57,372 - mmcls - INFO - Start running, host: dwekr@sooah-desktop, work_dir: /home/dwekr/workspace/datum/outputs/logs\n",
      "2023-07-10 18:25:57,372 - mmcls - INFO - Hooks will be executed in the following order:\n",
      "before_run:\n",
      "(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       \n",
      "(ABOVE_NORMAL) Fp16SAMOptimizerHook               \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) CheckpointHookWithValResults       \n",
      "(NORMAL      ) CancelInterfaceHook                \n",
      "(NORMAL      ) AdaptiveTrainSchedulingHook        \n",
      "(NORMAL      ) LoggerReplaceHook                  \n",
      "(71          ) OTXProgressHook                    \n",
      "(75          ) LazyEarlyStoppingHook              \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      " -------------------- \n",
      "before_train_epoch:\n",
      "(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) TaskAdaptHook                      \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      "(LOWEST      ) ForceTrainModeHook                 \n",
      " -------------------- \n",
      "before_train_iter:\n",
      "(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) AdaptiveTrainSchedulingHook        \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      " -------------------- \n",
      "after_train_iter:\n",
      "(ABOVE_NORMAL) Fp16SAMOptimizerHook               \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) CheckpointHookWithValResults       \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      "(75          ) LazyEarlyStoppingHook              \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      " -------------------- \n",
      "after_train_epoch:\n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) CheckpointHookWithValResults       \n",
      "(71          ) OTXProgressHook                    \n",
      "(75          ) LazyEarlyStoppingHook              \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      " -------------------- \n",
      "before_val_epoch:\n",
      "(NORMAL      ) TaskAdaptHook                      \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      " -------------------- \n",
      "before_val_iter:\n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      " -------------------- \n",
      "after_val_iter:\n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      " -------------------- \n",
      "after_val_epoch:\n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      " -------------------- \n",
      "after_run:\n",
      "(NORMAL      ) CancelInterfaceHook                \n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      " -------------------- \n",
      "2023-07-10 18:25:57,372 - mmcls - INFO - workflow: [('train', 1)], max: 90 epochs\n",
      "2023-07-10 18:25:57,373 | INFO : cancel hook is initialized\n",
      "2023-07-10 18:25:57,373 | INFO : logger in the runner is replaced to the MPA logger\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/torch/utils/tensorboard/__init__.py:4: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  if not hasattr(tensorboard, \"__version__\") or LooseVersion(\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/torch/utils/tensorboard/__init__.py:6: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  ) < LooseVersion(\"1.15\"):\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1170.6 task/s, elapsed: 8s, ETA:     0s\n",
      "2023-07-10 18:26:18,923 | INFO : Epoch [1][100/143]\tlr: 4.900e-03, eta: 0:29:09, time: 0.137, data_time: 0.004, memory: 3668, current_iters: 99, loss: 1.8926, sharpness: 0.2329, max_loss: 2.1255\n",
      "2023-07-10 18:26:22,562 | WARNING : training progress 1%\n",
      "2023-07-10 18:26:24,335 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:26:24,335 | INFO : Epoch [1][143/143]\tlr: 4.900e-03, eta: 0:39:08, time: 0.127, data_time: 0.000, memory: 3668, current_iters: 142, loss: 0.8262, sharpness: 0.2382, max_loss: 1.0639\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1378.6 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:26:31,004 | INFO : Saving best checkpoint at 1 epochs\n",
      "2023-07-10 18:26:31,133 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:26:31,133 | INFO : Epoch(val) [1][143]\taccuracy_top-1: 0.9555, accuracy_top-5: 0.9953, BACKGROUND_Google accuracy: 0.8651, Faces accuracy: 0.9954, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 0.8571, ant accuracy: 0.7381, barrel accuracy: 0.9574, bass accuracy: 0.8889, beaver accuracy: 0.9348, binocular accuracy: 0.9697, bonsai accuracy: 0.9922, brain accuracy: 0.9898, brontosaurus accuracy: 0.7674, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 0.9800, cannon accuracy: 0.9302, car_side accuracy: 1.0000, ceiling_fan accuracy: 0.9149, cellphone accuracy: 1.0000, chair accuracy: 0.8710, chandelier accuracy: 0.9720, cougar_body accuracy: 0.9574, cougar_face accuracy: 0.9420, crab accuracy: 0.9863, crayfish accuracy: 0.9000, crocodile accuracy: 0.9400, crocodile_head accuracy: 0.4510, cup accuracy: 1.0000, dalmatian accuracy: 0.9851, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 0.9200, elephant accuracy: 0.9219, emu accuracy: 0.9623, euphonium accuracy: 0.9844, ewer accuracy: 0.9882, ferry accuracy: 1.0000, flamingo accuracy: 0.9851, flamingo_head accuracy: 0.9778, garfield accuracy: 0.9118, gerenuk accuracy: 0.7941, gramophone accuracy: 1.0000, grand_piano accuracy: 0.9899, hawksbill accuracy: 1.0000, headphone accuracy: 0.9524, hedgehog accuracy: 0.9815, helicopter accuracy: 0.9886, ibis accuracy: 0.9875, inline_skate accuracy: 0.8710, joshua_tree accuracy: 0.9844, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 0.9615, lobster accuracy: 0.5854, lotus accuracy: 0.8939, mandolin accuracy: 0.8140, mayfly accuracy: 0.8750, menorah accuracy: 0.9885, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 0.9455, octopus accuracy: 0.2857, okapi accuracy: 0.9744, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 0.9778, pizza accuracy: 0.9623, platypus accuracy: 0.8529, pyramid accuracy: 1.0000, revolver accuracy: 0.9756, rhino accuracy: 0.9661, rooster accuracy: 0.9388, saxophone accuracy: 1.0000, schooner accuracy: 0.2381, scissors accuracy: 1.0000, scorpion accuracy: 0.9286, sea_horse accuracy: 0.9474, snoopy accuracy: 0.6571, soccer_ball accuracy: 1.0000, stapler accuracy: 0.9333, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 0.9429, sunflower accuracy: 0.9882, tick accuracy: 0.9796, trilobite accuracy: 0.9884, umbrella accuracy: 0.9867, watch accuracy: 0.9958, water_lilly accuracy: 0.5946, wheelchair accuracy: 0.9661, wild_cat accuracy: 0.8235, windsor_chair accuracy: 1.0000, wrench accuracy: 0.8974, yin_yang accuracy: 0.9500, mean accuracy: 0.9293, accuracy: 0.9555, current_iters: 143\n",
      "2023-07-10 18:26:31,139 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:26:44,023 | INFO : Epoch [2][100/143]\tlr: 4.899e-03, eta: 0:34:00, time: 0.129, data_time: 0.003, memory: 3668, current_iters: 242, loss: 0.2356, sharpness: 0.1889, max_loss: 0.4245\n",
      "2023-07-10 18:26:49,454 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:26:49,454 | INFO : Epoch [2][143/143]\tlr: 4.899e-03, eta: 0:38:04, time: 0.126, data_time: 0.000, memory: 3668, current_iters: 285, loss: 0.2175, sharpness: 0.1879, max_loss: 0.4054\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1342.2 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:26:56,302 | INFO : Saving best checkpoint at 2 epochs\n",
      "2023-07-10 18:26:56,454 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:26:56,454 | INFO : Epoch(val) [2][143]\taccuracy_top-1: 0.9843, accuracy_top-5: 0.9996, BACKGROUND_Google accuracy: 0.9422, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 0.9762, ant accuracy: 0.9524, barrel accuracy: 1.0000, bass accuracy: 0.9815, beaver accuracy: 0.9783, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 0.9070, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 0.9535, car_side accuracy: 1.0000, ceiling_fan accuracy: 0.9787, cellphone accuracy: 1.0000, chair accuracy: 0.9839, chandelier accuracy: 0.9813, cougar_body accuracy: 0.9787, cougar_face accuracy: 0.9710, crab accuracy: 0.9589, crayfish accuracy: 0.9714, crocodile accuracy: 0.9400, crocodile_head accuracy: 0.9412, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 0.9333, garfield accuracy: 0.8824, gerenuk accuracy: 0.9706, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 0.9524, hedgehog accuracy: 0.9815, helicopter accuracy: 0.9886, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 0.9688, kangaroo accuracy: 1.0000, ketch accuracy: 0.8947, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.7805, lotus accuracy: 0.9242, mandolin accuracy: 0.9070, mayfly accuracy: 0.9750, menorah accuracy: 0.9885, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 0.8857, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 0.9778, pizza accuracy: 1.0000, platypus accuracy: 0.8824, pyramid accuracy: 0.9825, revolver accuracy: 0.9878, rhino accuracy: 0.9831, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.9524, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 0.9825, snoopy accuracy: 0.9714, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 0.9867, watch accuracy: 1.0000, water_lilly accuracy: 0.5135, wheelchair accuracy: 1.0000, wild_cat accuracy: 0.9706, windsor_chair accuracy: 0.9821, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9764, accuracy: 0.9843, current_iters: 286\n",
      "2023-07-10 18:26:56,459 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:27:09,381 | INFO : Epoch [3][100/143]\tlr: 4.894e-03, eta: 0:34:56, time: 0.129, data_time: 0.003, memory: 3668, current_iters: 385, loss: 0.1279, sharpness: 0.1522, max_loss: 0.2801\n",
      "2023-07-10 18:27:14,812 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:27:14,813 | INFO : Epoch [3][143/143]\tlr: 4.894e-03, eta: 0:37:26, time: 0.126, data_time: 0.000, memory: 3668, current_iters: 428, loss: 0.1352, sharpness: 0.1578, max_loss: 0.2931\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1350.0 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:27:21,623 | INFO : Saving best checkpoint at 3 epochs\n",
      "2023-07-10 18:27:21,769 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:27:21,769 | INFO : Epoch(val) [3][143]\taccuracy_top-1: 0.9892, accuracy_top-5: 0.9998, BACKGROUND_Google accuracy: 0.9893, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 0.9762, barrel accuracy: 1.0000, bass accuracy: 0.9630, beaver accuracy: 1.0000, binocular accuracy: 0.9697, bonsai accuracy: 0.9922, brain accuracy: 1.0000, brontosaurus accuracy: 0.9535, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 0.9787, cellphone accuracy: 1.0000, chair accuracy: 0.9194, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 0.9710, crab accuracy: 0.9863, crayfish accuracy: 1.0000, crocodile accuracy: 0.9000, crocodile_head accuracy: 0.9608, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 0.8667, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 0.9706, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.8772, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 0.9872, lobster accuracy: 0.7561, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 0.9885, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 0.9412, pyramid accuracy: 1.0000, revolver accuracy: 0.9878, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.9841, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 0.9825, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 0.9831, stop_sign accuracy: 1.0000, strawberry accuracy: 0.9714, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.4324, wheelchair accuracy: 0.9831, wild_cat accuracy: 0.9706, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9827, accuracy: 0.9892, current_iters: 429\n",
      "2023-07-10 18:27:21,774 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:27:34,766 | INFO : Epoch [4][100/143]\tlr: 4.887e-03, eta: 0:35:10, time: 0.130, data_time: 0.003, memory: 3668, current_iters: 528, loss: 0.0870, sharpness: 0.1324, max_loss: 0.2194\n",
      "2023-07-10 18:27:40,221 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:27:40,221 | INFO : Epoch [4][143/143]\tlr: 4.887e-03, eta: 0:36:58, time: 0.127, data_time: 0.000, memory: 3668, current_iters: 571, loss: 0.0855, sharpness: 0.1296, max_loss: 0.2151\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1329.8 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:27:47,138 | INFO : Saving best checkpoint at 4 epochs\n",
      "2023-07-10 18:27:47,296 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:27:47,297 | INFO : Epoch(val) [4][143]\taccuracy_top-1: 0.9929, accuracy_top-5: 0.9999, BACKGROUND_Google accuracy: 0.9893, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 0.9762, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 0.9767, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 0.9787, cellphone accuracy: 1.0000, chair accuracy: 0.9839, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 0.9710, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.7600, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9211, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.7561, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 0.9750, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 0.9714, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 0.9878, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.5946, wheelchair accuracy: 1.0000, wild_cat accuracy: 0.9706, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9883, accuracy: 0.9929, current_iters: 572\n",
      "2023-07-10 18:27:47,303 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:28:00,299 | INFO : Epoch [5][100/143]\tlr: 4.876e-03, eta: 0:35:08, time: 0.130, data_time: 0.002, memory: 3668, current_iters: 671, loss: 0.0630, sharpness: 0.1146, max_loss: 0.1776\n",
      "2023-07-10 18:28:05,805 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:28:05,806 | INFO : Epoch [5][143/143]\tlr: 4.876e-03, eta: 0:36:32, time: 0.128, data_time: 0.000, memory: 3668, current_iters: 714, loss: 0.0684, sharpness: 0.1209, max_loss: 0.1893\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1331.3 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:28:12,712 | INFO : Saving best checkpoint at 5 epochs\n",
      "2023-07-10 18:28:12,872 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:28:12,872 | INFO : Epoch(val) [5][143]\taccuracy_top-1: 0.9942, accuracy_top-5: 0.9998, BACKGROUND_Google accuracy: 0.9936, Faces accuracy: 0.9977, Faces_easy accuracy: 0.9931, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 0.9839, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 0.9855, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9800, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.8860, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.7805, lotus accuracy: 0.7879, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.9841, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 0.8857, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.9459, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9922, accuracy: 0.9942, current_iters: 715\n",
      "2023-07-10 18:28:12,878 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:28:25,985 | INFO : Epoch [6][100/143]\tlr: 4.863e-03, eta: 0:35:01, time: 0.131, data_time: 0.003, memory: 3668, current_iters: 814, loss: 0.0465, sharpness: 0.1021, max_loss: 0.1486\n",
      "2023-07-10 18:28:31,501 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:28:31,501 | INFO : Epoch [6][143/143]\tlr: 4.863e-03, eta: 0:36:08, time: 0.128, data_time: 0.000, memory: 3668, current_iters: 857, loss: 0.0486, sharpness: 0.1058, max_loss: 0.1545\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1336.2 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:28:38,381 | INFO : Saving best checkpoint at 6 epochs\n",
      "2023-07-10 18:28:38,533 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:28:38,533 | INFO : Epoch(val) [6][143]\taccuracy_top-1: 0.9960, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 0.9979, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 0.9855, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9600, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 0.9851, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9912, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.8293, lotus accuracy: 0.8485, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 0.9714, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.8730, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.9730, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9942, accuracy: 0.9960, current_iters: 858\n",
      "2023-07-10 18:28:38,539 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:28:51,778 | INFO : Epoch [7][100/143]\tlr: 4.846e-03, eta: 0:34:50, time: 0.132, data_time: 0.003, memory: 3668, current_iters: 957, loss: 0.0439, sharpness: 0.0961, max_loss: 0.1400\n",
      "2023-07-10 18:28:57,353 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:28:57,354 | INFO : Epoch [7][143/143]\tlr: 4.846e-03, eta: 0:35:47, time: 0.130, data_time: 0.000, memory: 3668, current_iters: 1000, loss: 0.0388, sharpness: 0.0918, max_loss: 0.1306\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1314.9 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:29:04,348 | INFO : Saving best checkpoint at 7 epochs\n",
      "2023-07-10 18:29:04,504 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:29:04,504 | INFO : Epoch(val) [7][143]\taccuracy_top-1: 0.9968, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 0.9979, Faces accuracy: 0.9977, Faces_easy accuracy: 0.9931, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 0.9839, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 0.9855, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9600, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9561, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.9756, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.6216, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9948, accuracy: 0.9968, current_iters: 1001\n",
      "2023-07-10 18:29:04,510 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:29:17,693 | INFO : Epoch [8][100/143]\tlr: 4.827e-03, eta: 0:34:36, time: 0.132, data_time: 0.003, memory: 3668, current_iters: 1100, loss: 0.0333, sharpness: 0.0873, max_loss: 0.1206\n",
      "2023-07-10 18:29:23,292 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:29:23,292 | INFO : Epoch [8][143/143]\tlr: 4.827e-03, eta: 0:35:24, time: 0.130, data_time: 0.000, memory: 3668, current_iters: 1143, loss: 0.0333, sharpness: 0.0879, max_loss: 0.1212\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1311.3 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:29:30,300 | INFO : Saving best checkpoint at 8 epochs\n",
      "2023-07-10 18:29:30,449 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:29:30,449 | INFO : Epoch(val) [8][143]\taccuracy_top-1: 0.9981, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 0.9855, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9800, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9737, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.9756, lotus accuracy: 0.9545, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 0.9434, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.9730, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9978, accuracy: 0.9981, current_iters: 1144\n",
      "2023-07-10 18:29:30,455 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:29:43,771 | INFO : Epoch [9][100/143]\tlr: 4.805e-03, eta: 0:34:21, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 1243, loss: 0.0331, sharpness: 0.0866, max_loss: 0.1196\n",
      "2023-07-10 18:29:49,375 | WARNING : training progress 10%\n",
      "2023-07-10 18:29:49,376 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:29:49,376 | INFO : Epoch [9][143/143]\tlr: 4.805e-03, eta: 0:35:02, time: 0.131, data_time: 0.000, memory: 3668, current_iters: 1286, loss: 0.0321, sharpness: 0.0835, max_loss: 0.1156\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1320.3 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:29:56,340 | INFO : Saving best checkpoint at 9 epochs\n",
      "2023-07-10 18:29:56,490 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:29:56,490 | INFO : Epoch(val) [9][143]\taccuracy_top-1: 0.9986, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9800, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9825, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.9756, lotus accuracy: 0.9394, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 0.9825, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9985, accuracy: 0.9986, current_iters: 1287\n",
      "2023-07-10 18:29:56,496 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:30:09,767 | INFO : Epoch [10][100/143]\tlr: 4.780e-03, eta: 0:34:03, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 1386, loss: 0.0246, sharpness: 0.0816, max_loss: 0.1062\n",
      "2023-07-10 18:30:15,371 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:30:15,371 | INFO : Epoch [10][143/143]\tlr: 4.780e-03, eta: 0:34:39, time: 0.130, data_time: 0.000, memory: 3668, current_iters: 1429, loss: 0.0256, sharpness: 0.0801, max_loss: 0.1056\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1318.8 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:30:22,344 | INFO : Saving best checkpoint at 10 epochs\n",
      "2023-07-10 18:30:22,482 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:30:22,482 | INFO : Epoch(val) [10][143]\taccuracy_top-1: 0.9988, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 0.9800, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9800, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 0.9884, ketch accuracy: 0.9912, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.9756, lotus accuracy: 0.9697, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9988, accuracy: 0.9988, current_iters: 1430\n",
      "2023-07-10 18:30:22,488 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:30:35,822 | INFO : Epoch [11][100/143]\tlr: 4.752e-03, eta: 0:33:45, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 1529, loss: 0.0211, sharpness: 0.0742, max_loss: 0.0953\n",
      "2023-07-10 18:30:41,414 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:30:41,414 | INFO : Epoch [11][143/143]\tlr: 4.752e-03, eta: 0:34:16, time: 0.131, data_time: 0.000, memory: 3668, current_iters: 1572, loss: 0.0230, sharpness: 0.0762, max_loss: 0.0992\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1296.6 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:30:48,503 | INFO : Saving best checkpoint at 11 epochs\n",
      "2023-07-10 18:30:48,654 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:30:48,654 | INFO : Epoch(val) [11][143]\taccuracy_top-1: 0.9991, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9825, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 0.9697, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9994, accuracy: 0.9991, current_iters: 1573\n",
      "2023-07-10 18:30:48,660 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:31:01,987 | INFO : Epoch [12][100/143]\tlr: 4.722e-03, eta: 0:33:25, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 1672, loss: 0.0193, sharpness: 0.0685, max_loss: 0.0878\n",
      "2023-07-10 18:31:07,641 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:31:07,641 | INFO : Epoch [12][143/143]\tlr: 4.722e-03, eta: 0:33:53, time: 0.131, data_time: 0.000, memory: 3668, current_iters: 1715, loss: 0.0197, sharpness: 0.0717, max_loss: 0.0915\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1311.1 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:31:14,723 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:31:14,723 | INFO : Epoch(val) [12][143]\taccuracy_top-1: 0.9988, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9561, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 0.9697, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9992, accuracy: 0.9988, current_iters: 1716\n",
      "2023-07-10 18:31:14,729 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:31:28,106 | INFO : Epoch [13][100/143]\tlr: 4.688e-03, eta: 0:33:05, time: 0.134, data_time: 0.003, memory: 3668, current_iters: 1815, loss: 0.0182, sharpness: 0.0729, max_loss: 0.0911\n",
      "2023-07-10 18:31:33,745 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:31:33,746 | INFO : Epoch [13][143/143]\tlr: 4.688e-03, eta: 0:33:29, time: 0.131, data_time: 0.000, memory: 3668, current_iters: 1858, loss: 0.0175, sharpness: 0.0714, max_loss: 0.0890\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1291.1 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:31:40,865 | INFO : Saving best checkpoint at 13 epochs\n",
      "2023-07-10 18:31:41,009 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:31:41,009 | INFO : Epoch(val) [13][143]\taccuracy_top-1: 0.9992, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9800, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 0.9697, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9994, accuracy: 0.9992, current_iters: 1859\n",
      "2023-07-10 18:31:41,014 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:31:54,354 | INFO : Epoch [14][100/143]\tlr: 4.652e-03, eta: 0:32:44, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 1958, loss: 0.0161, sharpness: 0.0669, max_loss: 0.0830\n",
      "2023-07-10 18:31:59,981 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:31:59,981 | INFO : Epoch [14][143/143]\tlr: 4.652e-03, eta: 0:33:05, time: 0.131, data_time: 0.000, memory: 3668, current_iters: 2001, loss: 0.0157, sharpness: 0.0655, max_loss: 0.0813\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1287.1 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:32:07,122 | INFO : Saving best checkpoint at 14 epochs\n",
      "2023-07-10 18:32:07,258 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:32:07,258 | INFO : Epoch(val) [14][143]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9999, accuracy: 0.9996, current_iters: 2002\n",
      "2023-07-10 18:32:07,264 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:32:20,583 | INFO : Epoch [15][100/143]\tlr: 4.613e-03, eta: 0:32:21, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 2101, loss: 0.0148, sharpness: 0.0600, max_loss: 0.0748\n",
      "2023-07-10 18:32:26,199 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:32:26,200 | INFO : Epoch [15][143/143]\tlr: 4.613e-03, eta: 0:32:40, time: 0.131, data_time: 0.000, memory: 3668, current_iters: 2144, loss: 0.0143, sharpness: 0.0609, max_loss: 0.0753\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1302.0 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:32:33,334 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:32:33,334 | INFO : Epoch(val) [15][143]\taccuracy_top-1: 0.9992, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 0.9697, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.9841, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9995, accuracy: 0.9992, current_iters: 2145\n",
      "2023-07-10 18:32:33,340 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:32:46,782 | INFO : Epoch [16][100/143]\tlr: 4.572e-03, eta: 0:31:59, time: 0.134, data_time: 0.003, memory: 3668, current_iters: 2244, loss: 0.0135, sharpness: 0.0594, max_loss: 0.0730\n",
      "2023-07-10 18:32:52,416 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:32:52,416 | INFO : Epoch [16][143/143]\tlr: 4.572e-03, eta: 0:32:16, time: 0.132, data_time: 0.000, memory: 3668, current_iters: 2287, loss: 0.0139, sharpness: 0.0622, max_loss: 0.0761\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1285.5 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:32:59,656 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:32:59,656 | INFO : Epoch(val) [16][143]\taccuracy_top-1: 0.9992, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 0.9977, Faces_easy accuracy: 0.9931, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 0.9697, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 0.9821, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9994, accuracy: 0.9992, current_iters: 2288\n",
      "2023-07-10 18:32:59,663 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:33:13,068 | INFO : Epoch [17][100/143]\tlr: 4.528e-03, eta: 0:31:36, time: 0.134, data_time: 0.003, memory: 3668, current_iters: 2387, loss: 0.0123, sharpness: 0.0587, max_loss: 0.0710\n",
      "2023-07-10 18:33:18,743 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:33:18,743 | INFO : Epoch [17][143/143]\tlr: 4.528e-03, eta: 0:31:52, time: 0.132, data_time: 0.000, memory: 3668, current_iters: 2430, loss: 0.0127, sharpness: 0.0581, max_loss: 0.0708\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1304.0 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:33:25,793 | INFO : Saving best checkpoint at 17 epochs\n",
      "2023-07-10 18:33:25,945 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:33:25,945 | INFO : Epoch(val) [17][143]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 0.9931, Faces_easy accuracy: 0.9977, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9999, accuracy: 0.9996, current_iters: 2431\n",
      "2023-07-10 18:33:25,951 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:33:39,372 | INFO : Epoch [18][100/143]\tlr: 4.481e-03, eta: 0:31:13, time: 0.134, data_time: 0.003, memory: 3668, current_iters: 2530, loss: 0.0110, sharpness: 0.0569, max_loss: 0.0680\n",
      "2023-07-10 18:33:45,053 | WARNING : training progress 20%\n",
      "2023-07-10 18:33:45,054 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:33:45,054 | INFO : Epoch [18][143/143]\tlr: 4.481e-03, eta: 0:31:27, time: 0.132, data_time: 0.000, memory: 3668, current_iters: 2573, loss: 0.0121, sharpness: 0.0599, max_loss: 0.0719\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1295.6 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:33:52,227 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:33:52,227 | INFO : Epoch(val) [18][143]\taccuracy_top-1: 0.9995, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 0.9855, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9998, accuracy: 0.9995, current_iters: 2574\n",
      "2023-07-10 18:33:52,233 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:34:05,654 | INFO : Epoch [19][100/143]\tlr: 4.432e-03, eta: 0:30:50, time: 0.134, data_time: 0.003, memory: 3668, current_iters: 2673, loss: 0.0125, sharpness: 0.0554, max_loss: 0.0679\n",
      "2023-07-10 18:34:11,362 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:34:11,363 | INFO : Epoch [19][143/143]\tlr: 4.432e-03, eta: 0:31:02, time: 0.132, data_time: 0.000, memory: 3668, current_iters: 2716, loss: 0.0126, sharpness: 0.0569, max_loss: 0.0696\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1296.8 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:34:18,528 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:34:18,528 | INFO : Epoch(val) [19][143]\taccuracy_top-1: 0.9993, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 0.9412, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9993, accuracy: 0.9993, current_iters: 2717\n",
      "2023-07-10 18:34:18,534 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:34:31,987 | INFO : Epoch [20][100/143]\tlr: 4.381e-03, eta: 0:30:26, time: 0.135, data_time: 0.003, memory: 3668, current_iters: 2816, loss: 0.0096, sharpness: 0.0592, max_loss: 0.0688\n",
      "2023-07-10 18:34:37,766 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:34:37,766 | INFO : Epoch [20][143/143]\tlr: 4.381e-03, eta: 0:30:38, time: 0.133, data_time: 0.000, memory: 3668, current_iters: 2859, loss: 0.0099, sharpness: 0.0539, max_loss: 0.0639\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1295.7 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:34:44,863 | INFO : Saving best checkpoint at 20 epochs\n",
      "2023-07-10 18:34:45,014 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:34:45,014 | INFO : Epoch(val) [20][143]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9999, accuracy: 0.9996, current_iters: 2860\n",
      "2023-07-10 18:34:45,020 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:34:58,512 | INFO : Epoch [21][100/143]\tlr: 4.327e-03, eta: 0:30:03, time: 0.135, data_time: 0.003, memory: 3668, current_iters: 2959, loss: 0.0098, sharpness: 0.0531, max_loss: 0.0629\n",
      "2023-07-10 18:35:04,235 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:35:04,235 | INFO : Epoch [21][143/143]\tlr: 4.327e-03, eta: 0:30:13, time: 0.133, data_time: 0.000, memory: 3668, current_iters: 3002, loss: 0.0119, sharpness: 0.0539, max_loss: 0.0657\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1285.6 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:35:11,387 | INFO : Saving best checkpoint at 21 epochs\n",
      "2023-07-10 18:35:11,604 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:35:11,604 | INFO : Epoch(val) [21][143]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9999, accuracy: 0.9996, current_iters: 3003\n",
      "2023-07-10 18:35:11,609 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:35:25,076 | INFO : Epoch [22][100/143]\tlr: 4.271e-03, eta: 0:29:39, time: 0.135, data_time: 0.003, memory: 3668, current_iters: 3102, loss: 0.0088, sharpness: 0.0485, max_loss: 0.0572\n",
      "2023-07-10 18:35:30,824 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:35:30,824 | INFO : Epoch [22][143/143]\tlr: 4.271e-03, eta: 0:29:48, time: 0.133, data_time: 0.000, memory: 3668, current_iters: 3145, loss: 0.0083, sharpness: 0.0470, max_loss: 0.0553\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 9144/9144, 1277.8 task/s, elapsed: 7s, ETA:     0s\n",
      "2023-07-10 18:35:38,162 | INFO : \n",
      "Early Stopping at :21 with best accuracy: 0.9995625305175782\n",
      "2023-07-10 18:35:38,163 | INFO : Exp name: outputs/logs\n",
      "2023-07-10 18:35:38,163 | INFO : Epoch(val) [22][143]\taccuracy_top-1: 0.9993, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9908, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 0.9697, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9996, accuracy: 0.9993, current_iters: 3146\n",
      "2023-07-10 18:35:38,176 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-10 18:35:39,248 | INFO : called save_model\n",
      "2023-07-10 18:35:39,356 | INFO : Final model performance: Performance(score: 0.9995625305175782, dashboard: (115 metric groups))\n",
      "2023-07-10 18:35:39,357 | INFO : train done.\n",
      "otx train time elapsed:  0:09:51.762842\n",
      "otx train CLI report has been generated: outputs/cli_report.log\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/tempfile.py:833: ResourceWarning: Implicitly cleaning up <TemporaryDirectory '/tmp/img-cache-iqz52wv7'>\n",
      "  _warnings.warn(warn_message, ResourceWarning)\n"
     ]
    }
   ],
   "source": [
    "!otx train EfficientNet-B0 \\\n",
    "    --train-data-roots caltech-101 \\\n",
    "    --val-data-roots caltech-101 \\\n",
    "    -o outputs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9957f4b9",
   "metadata": {},
   "source": [
    "When training with the entire caltech-101 dataset, the best accuracy is `0.9995625305175782` and the training time is 9 minutes and 51 seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2a677df4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*] Workspace Path: otx-workspace-CLASSIFICATION\n",
      "[*] Load Model Template ID: Custom_Image_Classification_EfficinetNet-B0\n",
      "[*] Load Model Name: EfficientNet-B0\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details.\n",
      "  warnings.warn(\n",
      "2023-07-08 03:58:52,794 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model.py\n",
      "2023-07-08 03:58:52,823 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model_multilabel.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/data_pipeline.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/deployment.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/hpo_config.yaml\n",
      "2023-07-08 03:58:52,923 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model_hierarchical.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/compression_config.json\n",
      "[*] Update data configuration file to: otx-workspace-CLASSIFICATION/data.yaml\n",
      "2023-07-08 03:58:53.770452: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/openvino/pyopenvino/__init__.py:10: FutureWarning: The module is private and following namespace `pyopenvino` will be removed in the future\n",
      "  warnings.warn(message=\"The module is private and following namespace \" \"`pyopenvino` will be removed in the future\", category=FutureWarning)\n",
      "2023-07-08 03:58:55,877 | INFO : Classification mode: multiclass\n",
      "2023-07-08 03:58:55,877 | INFO : train()\n",
      "2023-07-08 03:58:55,887 | INFO : Training seed was set to 5 w/ deterministic=False.\n",
      "2023-07-08 03:58:55,889 | INFO : Try to create a 0 size memory pool.\n",
      "2023-07-08 03:58:56,669 | INFO : configure!: training=True\n",
      "2023-07-08 03:58:56,783 | INFO : init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n",
      "2023-07-08 03:58:56,807 | INFO : 'in_channels' config in model.head is updated from -1 to 1280\n",
      "2023-07-08 03:58:56,808 | INFO : configure_data()\n",
      "2023-07-08 03:58:56,808 | INFO : task config!!!!: training=True\n",
      "2023-07-08 03:58:56,808 | INFO : train!\n",
      "2023-07-08 03:58:56,808 | INFO : cfg.gpu_ids = range(0, 1), distributed = False\n",
      "2023-07-08 03:58:56,826 | INFO : Environment info:\n",
      "------------------------------------------------------------\n",
      "sys.platform: linux\n",
      "Python: 3.10.0 (default, Mar  3 2022, 09:58:08) [GCC 7.5.0]\n",
      "CUDA available: True\n",
      "GPU 0,1: GeForce RTX 3090\n",
      "CUDA_HOME: /usr/local/cuda\n",
      "NVCC: Cuda compilation tools, release 11.1, V11.1.74\n",
      "GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
      "PyTorch: 1.13.1+cu117\n",
      "PyTorch compiling details: PyTorch built with:\n",
      "  - GCC 9.3\n",
      "  - C++ Version: 201402\n",
      "  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n",
      "  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\n",
      "  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n",
      "  - LAPACK is enabled (usually provided by MKL)\n",
      "  - NNPACK is enabled\n",
      "  - CPU capability usage: AVX2\n",
      "  - CUDA Runtime 11.7\n",
      "  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n",
      "  - CuDNN 8.5\n",
      "  - Magma 2.6.1\n",
      "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n",
      "\n",
      "TorchVision: 0.14.1+cu117\n",
      "OpenCV: 4.7.0\n",
      "MMCV: 1.7.1\n",
      "MMCV Compiler: GCC 7.5\n",
      "MMCV CUDA Compiler: 11.1\n",
      "MMClassification: 0.25.0+c5ac764\n",
      "------------------------------------------------------------\n",
      "\n",
      "2023-07-08 03:58:57,061 | INFO : init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n",
      "2023-07-08 03:58:57,072 - mmcv - INFO - initialize CustomLinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.conv.weight - torch.Size([32, 3, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.bn.weight - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.bn.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.conv.weight - torch.Size([32, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.bn.weight - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.bn.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv1.weight - torch.Size([8, 32, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv1.bias - torch.Size([8]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv2.weight - torch.Size([32, 8, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,073 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv2.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.conv.weight - torch.Size([16, 32, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.bn.weight - torch.Size([16]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.bn.bias - torch.Size([16]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.conv.weight - torch.Size([96, 16, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.bn.weight - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.bn.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.conv.weight - torch.Size([96, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.bn.weight - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.bn.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv1.weight - torch.Size([4, 96, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv1.bias - torch.Size([4]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv2.weight - torch.Size([96, 4, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv2.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.conv.weight - torch.Size([24, 96, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.bn.weight - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.bn.bias - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.conv.weight - torch.Size([144, 24, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.conv.weight - torch.Size([144, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv1.weight - torch.Size([6, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv1.bias - torch.Size([6]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv2.weight - torch.Size([144, 6, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,074 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv2.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.conv.weight - torch.Size([24, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.bn.weight - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.bn.bias - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.conv.weight - torch.Size([144, 24, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.conv.weight - torch.Size([144, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv1.weight - torch.Size([6, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv1.bias - torch.Size([6]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv2.weight - torch.Size([144, 6, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv2.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.conv.weight - torch.Size([40, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.bn.weight - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.bn.bias - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.conv.weight - torch.Size([240, 40, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.conv.weight - torch.Size([240, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv1.weight - torch.Size([10, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv1.bias - torch.Size([10]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv2.weight - torch.Size([240, 10, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,075 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv2.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.conv.weight - torch.Size([40, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.bn.weight - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.bn.bias - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.conv.weight - torch.Size([240, 40, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.conv.weight - torch.Size([240, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv1.weight - torch.Size([10, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv1.bias - torch.Size([10]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv2.weight - torch.Size([240, 10, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv2.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.conv.weight - torch.Size([80, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.conv.weight - torch.Size([480, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,076 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.conv.weight - torch.Size([80, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.conv.weight - torch.Size([480, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.conv.weight - torch.Size([80, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.conv.weight - torch.Size([480, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,077 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.conv.weight - torch.Size([112, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.conv.weight - torch.Size([112, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,078 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.conv.weight - torch.Size([112, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.conv.weight - torch.Size([192, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.conv.weight - torch.Size([1152, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,079 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv3.conv.weight - torch.Size([192, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv2.conv.weight - torch.Size([1152, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv3.conv.weight - torch.Size([192, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv2.conv.weight - torch.Size([1152, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,080 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv3.conv.weight - torch.Size([192, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv2.conv.weight - torch.Size([1152, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv3.conv.weight - torch.Size([320, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv3.bn.weight - torch.Size([320]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv3.bn.bias - torch.Size([320]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.final_block.conv.weight - torch.Size([1280, 320, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.final_block.bn.weight - torch.Size([1280]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "backbone.features.final_block.bn.bias - torch.Size([1280]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "head.fc.weight - torch.Size([102, 1280]): \n",
      "NormalInit: mean=0, std=0.01, bias=0 \n",
      " \n",
      "2023-07-08 03:58:57,081 - mmcv - INFO - \n",
      "head.fc.bias - torch.Size([102]): \n",
      "NormalInit: mean=0, std=0.01, bias=0 \n",
      " \n",
      "2023-07-08 03:58:58,462 | INFO : Task Adaptation: [] => ['BACKGROUND_Google', 'Faces', 'Faces_easy', 'Leopards', 'Motorbikes', 'accordion', 'airplanes', 'anchor', 'ant', 'barrel', 'bass', 'beaver', 'binocular', 'bonsai', 'brain', 'brontosaurus', 'buddha', 'butterfly', 'camera', 'cannon', 'car_side', 'ceiling_fan', 'cellphone', 'chair', 'chandelier', 'cougar_body', 'cougar_face', 'crab', 'crayfish', 'crocodile', 'crocodile_head', 'cup', 'dalmatian', 'dollar_bill', 'dolphin', 'dragonfly', 'electric_guitar', 'elephant', 'emu', 'euphonium', 'ewer', 'ferry', 'flamingo', 'flamingo_head', 'garfield', 'gerenuk', 'gramophone', 'grand_piano', 'hawksbill', 'headphone', 'hedgehog', 'helicopter', 'ibis', 'inline_skate', 'joshua_tree', 'kangaroo', 'ketch', 'lamp', 'laptop', 'llama', 'lobster', 'lotus', 'mandolin', 'mayfly', 'menorah', 'metronome', 'minaret', 'nautilus', 'octopus', 'okapi', 'pagoda', 'panda', 'pigeon', 'pizza', 'platypus', 'pyramid', 'revolver', 'rhino', 'rooster', 'saxophone', 'schooner', 'scissors', 'scorpion', 'sea_horse', 'snoopy', 'soccer_ball', 'stapler', 'starfish', 'stegosaurus', 'stop_sign', 'strawberry', 'sunflower', 'tick', 'trilobite', 'umbrella', 'watch', 'water_lilly', 'wheelchair', 'wild_cat', 'windsor_chair', 'wrench', 'yin_yang']\n",
      "2023-07-08 03:58:58,463 | INFO : - Efficient Mode: True\n",
      "2023-07-08 03:58:58,463 | INFO : - Sampler type: balanced\n",
      "2023-07-08 03:58:58,463 | INFO : - Sampler flag: False\n",
      "2023-07-08 03:58:58,463 - mmcls - INFO - Start running, host: dwekr@sooah-desktop, work_dir: /home/dwekr/workspace/datum/outputs_random/logs\n",
      "2023-07-08 03:58:58,463 - mmcls - INFO - Hooks will be executed in the following order:\n",
      "before_run:\n",
      "(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       \n",
      "(ABOVE_NORMAL) Fp16SAMOptimizerHook               \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) CheckpointHookWithValResults       \n",
      "(NORMAL      ) CancelInterfaceHook                \n",
      "(NORMAL      ) AdaptiveTrainSchedulingHook        \n",
      "(NORMAL      ) LoggerReplaceHook                  \n",
      "(71          ) OTXProgressHook                    \n",
      "(75          ) LazyEarlyStoppingHook              \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      " -------------------- \n",
      "before_train_epoch:\n",
      "(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) TaskAdaptHook                      \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      "(LOWEST      ) ForceTrainModeHook                 \n",
      " -------------------- \n",
      "before_train_iter:\n",
      "(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) AdaptiveTrainSchedulingHook        \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      " -------------------- \n",
      "after_train_iter:\n",
      "(ABOVE_NORMAL) Fp16SAMOptimizerHook               \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) CheckpointHookWithValResults       \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      "(75          ) LazyEarlyStoppingHook              \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      " -------------------- \n",
      "after_train_epoch:\n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) CheckpointHookWithValResults       \n",
      "(71          ) OTXProgressHook                    \n",
      "(75          ) LazyEarlyStoppingHook              \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      " -------------------- \n",
      "before_val_epoch:\n",
      "(NORMAL      ) TaskAdaptHook                      \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      " -------------------- \n",
      "before_val_iter:\n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      " -------------------- \n",
      "after_val_iter:\n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      " -------------------- \n",
      "after_val_epoch:\n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      " -------------------- \n",
      "after_run:\n",
      "(NORMAL      ) CancelInterfaceHook                \n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      " -------------------- \n",
      "2023-07-08 03:58:58,463 - mmcls - INFO - workflow: [('train', 1)], max: 90 epochs\n",
      "2023-07-08 03:58:58,464 | INFO : cancel hook is initialized\n",
      "2023-07-08 03:58:58,464 | INFO : logger in the runner is replaced to the MPA logger\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/torch/utils/tensorboard/__init__.py:4: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  if not hasattr(tensorboard, \"__version__\") or LooseVersion(\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/torch/utils/tensorboard/__init__.py:6: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  ) < LooseVersion(\"1.15\"):\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1036.6 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 03:59:11,588 | WARNING : training progress 1%\n",
      "2023-07-08 03:59:12,469 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 03:59:12,469 | INFO : Epoch [1][72/72]\tlr: 4.900e-03, eta: 0:19:42, time: 0.133, data_time: 0.005, memory: 3668, current_iters: 71, loss: 2.3541, sharpness: 0.2387, max_loss: 2.5822\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1374.8 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 03:59:15,815 | INFO : Saving best checkpoint at 1 epochs\n",
      "2023-07-08 03:59:15,948 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 03:59:15,948 | INFO : Epoch(val) [1][72]\taccuracy_top-1: 0.8633, accuracy_top-5: 0.9748, BACKGROUND_Google accuracy: 0.9352, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 0.0000, ant accuracy: 0.9167, barrel accuracy: 0.9130, bass accuracy: 0.7917, beaver accuracy: 0.8000, binocular accuracy: 0.2500, bonsai accuracy: 0.9672, brain accuracy: 0.8958, brontosaurus accuracy: 0.0000, buddha accuracy: 0.9615, butterfly accuracy: 0.9778, camera accuracy: 1.0000, cannon accuracy: 0.5789, car_side accuracy: 1.0000, ceiling_fan accuracy: 0.9167, cellphone accuracy: 0.9630, chair accuracy: 0.5862, chandelier accuracy: 0.9434, cougar_body accuracy: 0.1818, cougar_face accuracy: 0.9167, crab accuracy: 0.8108, crayfish accuracy: 0.8810, crocodile accuracy: 0.0000, crocodile_head accuracy: 0.8400, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 0.9286, electric_guitar accuracy: 0.9730, elephant accuracy: 0.9744, emu accuracy: 0.2800, euphonium accuracy: 0.9706, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 0.3333, flamingo_head accuracy: 1.0000, garfield accuracy: 0.0000, gerenuk accuracy: 0.0000, gramophone accuracy: 0.5862, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 0.6957, hedgehog accuracy: 1.0000, helicopter accuracy: 0.9375, ibis accuracy: 0.9737, inline_skate accuracy: 0.3529, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9796, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 0.9787, lobster accuracy: 0.0000, lotus accuracy: 0.5588, mandolin accuracy: 0.0500, mayfly accuracy: 0.0435, menorah accuracy: 0.9048, metronome accuracy: 0.3125, minaret accuracy: 1.0000, nautilus accuracy: 0.9600, octopus accuracy: 0.0000, okapi accuracy: 0.0000, pagoda accuracy: 0.9231, panda accuracy: 0.6818, pigeon accuracy: 0.7037, pizza accuracy: 0.9394, platypus accuracy: 0.6111, pyramid accuracy: 0.4074, revolver accuracy: 0.9767, rhino accuracy: 0.5185, rooster accuracy: 0.9615, saxophone accuracy: 0.7222, schooner accuracy: 0.0000, scissors accuracy: 0.6522, scorpion accuracy: 0.9744, sea_horse accuracy: 0.1923, snoopy accuracy: 0.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 0.7692, starfish accuracy: 0.9783, stegosaurus accuracy: 0.9697, stop_sign accuracy: 0.9600, strawberry accuracy: 0.6316, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 0.9744, umbrella accuracy: 0.9412, watch accuracy: 0.9919, water_lilly accuracy: 0.8889, wheelchair accuracy: 0.9032, wild_cat accuracy: 0.3000, windsor_chair accuracy: 1.0000, wrench accuracy: 0.0000, yin_yang accuracy: 0.8000, mean accuracy: 0.7371, accuracy: 0.8633, current_iters: 72\n",
      "2023-07-08 03:59:15,954 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 03:59:25,430 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 03:59:25,430 | INFO : Epoch [2][72/72]\tlr: 4.899e-03, eta: 0:19:23, time: 0.132, data_time: 0.004, memory: 3668, current_iters: 143, loss: 0.4784, sharpness: 0.2138, max_loss: 0.6899\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1386.5 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 03:59:28,747 | INFO : Saving best checkpoint at 2 epochs\n",
      "2023-07-08 03:59:28,903 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 03:59:28,903 | INFO : Epoch(val) [2][72]\taccuracy_top-1: 0.9685, accuracy_top-5: 0.9987, BACKGROUND_Google accuracy: 0.9636, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 0.9500, ant accuracy: 0.9583, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 0.9167, brontosaurus accuracy: 0.7000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 0.7241, chandelier accuracy: 0.9811, cougar_body accuracy: 0.5455, cougar_face accuracy: 1.0000, crab accuracy: 0.8919, crayfish accuracy: 1.0000, crocodile accuracy: 0.3810, crocodile_head accuracy: 0.8800, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 0.9714, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 0.9744, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 0.9744, flamingo_head accuracy: 1.0000, garfield accuracy: 0.9474, gerenuk accuracy: 0.9474, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 0.9130, hedgehog accuracy: 0.9643, helicopter accuracy: 0.9792, ibis accuracy: 1.0000, inline_skate accuracy: 0.9412, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9796, lamp accuracy: 0.9655, laptop accuracy: 1.0000, llama accuracy: 0.9362, lobster accuracy: 0.7500, lotus accuracy: 0.7353, mandolin accuracy: 0.6000, mayfly accuracy: 0.9565, menorah accuracy: 0.9762, metronome accuracy: 0.9375, minaret accuracy: 1.0000, nautilus accuracy: 0.9600, octopus accuracy: 0.2353, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.3929, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 0.9231, snoopy accuracy: 0.7778, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 0.9600, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 0.9706, watch accuracy: 0.9919, water_lilly accuracy: 0.9444, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 0.9444, yin_yang accuracy: 0.9600, mean accuracy: 0.9451, accuracy: 0.9685, current_iters: 144\n",
      "2023-07-08 03:59:28,909 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 03:59:38,225 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 03:59:38,225 | INFO : Epoch [3][72/72]\tlr: 4.894e-03, eta: 0:19:02, time: 0.129, data_time: 0.004, memory: 3668, current_iters: 215, loss: 0.1965, sharpness: 0.1831, max_loss: 0.3798\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1372.7 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 03:59:41,576 | INFO : Saving best checkpoint at 3 epochs\n",
      "2023-07-08 03:59:41,724 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 03:59:41,724 | INFO : Epoch(val) [3][72]\taccuracy_top-1: 0.9882, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 0.9838, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 0.9583, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 0.9200, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 0.9500, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.7619, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 0.9643, electric_guitar accuracy: 0.9459, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 0.9744, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 0.9565, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9388, lamp accuracy: 0.9655, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.7000, lotus accuracy: 0.6176, mandolin accuracy: 0.9500, mayfly accuracy: 0.9565, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 0.7647, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.8571, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 0.9231, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9812, accuracy: 0.9882, current_iters: 216\n",
      "2023-07-08 03:59:41,730 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 03:59:51,049 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 03:59:51,049 | INFO : Epoch [4][72/72]\tlr: 4.887e-03, eta: 0:18:44, time: 0.129, data_time: 0.004, memory: 3668, current_iters: 287, loss: 0.1137, sharpness: 0.1440, max_loss: 0.2576\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1371.4 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 03:59:54,402 | INFO : Saving best checkpoint at 4 epochs\n",
      "2023-07-08 03:59:54,551 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 03:59:54,551 | INFO : Epoch(val) [4][72]\taccuracy_top-1: 0.9937, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 0.9838, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 0.9762, crocodile accuracy: 1.0000, crocodile_head accuracy: 0.8000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 0.9459, elephant accuracy: 0.9744, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 0.9744, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.7500, lotus accuracy: 0.9118, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.8214, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.9444, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9910, accuracy: 0.9937, current_iters: 288\n",
      "2023-07-08 03:59:54,557 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:00:03,840 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:00:03,840 | INFO : Epoch [5][72/72]\tlr: 4.876e-03, eta: 0:18:28, time: 0.129, data_time: 0.003, memory: 3668, current_iters: 359, loss: 0.0832, sharpness: 0.1344, max_loss: 0.2180\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1373.4 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:00:07,189 | INFO : Saving best checkpoint at 5 epochs\n",
      "2023-07-08 04:00:07,342 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:00:07,342 | INFO : Epoch(val) [5][72]\taccuracy_top-1: 0.9956, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 0.9500, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 0.9643, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9388, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.9500, lotus accuracy: 0.7059, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 0.9630, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 0.9744, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 0.9500, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9940, accuracy: 0.9956, current_iters: 360\n",
      "2023-07-08 04:00:07,348 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:00:16,703 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:00:16,703 | INFO : Epoch [6][72/72]\tlr: 4.863e-03, eta: 0:18:14, time: 0.130, data_time: 0.004, memory: 3668, current_iters: 431, loss: 0.0578, sharpness: 0.1157, max_loss: 0.1733\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1362.1 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:00:20,079 | INFO : Saving best checkpoint at 6 epochs\n",
      "2023-07-08 04:00:20,214 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:00:20,214 | INFO : Epoch(val) [6][72]\taccuracy_top-1: 0.9976, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 0.9744, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.8776, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.9500, lotus accuracy: 0.9412, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9974, accuracy: 0.9976, current_iters: 432\n",
      "2023-07-08 04:00:20,221 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:00:29,591 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:00:29,591 | INFO : Epoch [7][72/72]\tlr: 4.846e-03, eta: 0:18:01, time: 0.130, data_time: 0.004, memory: 3668, current_iters: 503, loss: 0.0431, sharpness: 0.1033, max_loss: 0.1471\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1363.6 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:00:32,964 | INFO : Saving best checkpoint at 7 epochs\n",
      "2023-07-08 04:00:33,116 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:00:33,116 | INFO : Epoch(val) [7][72]\taccuracy_top-1: 0.9985, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.9500, lotus accuracy: 0.9118, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.9286, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9979, accuracy: 0.9985, current_iters: 504\n",
      "2023-07-08 04:00:33,122 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:00:42,515 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:00:42,515 | INFO : Epoch [8][72/72]\tlr: 4.827e-03, eta: 0:17:48, time: 0.130, data_time: 0.004, memory: 3668, current_iters: 575, loss: 0.0410, sharpness: 0.1120, max_loss: 0.1532\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1352.4 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:00:45,918 | INFO : Saving best checkpoint at 8 epochs\n",
      "2023-07-08 04:00:46,067 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:00:46,067 | INFO : Epoch(val) [8][72]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.9500, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9995, accuracy: 0.9996, current_iters: 576\n",
      "2023-07-08 04:00:46,073 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:00:55,460 | WARNING : training progress 10%\n",
      "2023-07-08 04:00:55,460 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:00:55,460 | INFO : Epoch [9][72/72]\tlr: 4.805e-03, eta: 0:17:35, time: 0.130, data_time: 0.004, memory: 3668, current_iters: 647, loss: 0.0328, sharpness: 0.0913, max_loss: 0.1241\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1334.2 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:00:58,908 | INFO : Saving best checkpoint at 9 epochs\n",
      "2023-07-08 04:00:59,071 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:00:59,071 | INFO : Epoch(val) [9][72]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.9444, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9994, accuracy: 0.9996, current_iters: 648\n",
      "2023-07-08 04:00:59,077 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:01:08,505 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:01:08,505 | INFO : Epoch [10][72/72]\tlr: 4.780e-03, eta: 0:17:23, time: 0.131, data_time: 0.004, memory: 3668, current_iters: 719, loss: 0.0297, sharpness: 0.0828, max_loss: 0.1124\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1345.5 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:01:11,924 | INFO : Saving best checkpoint at 10 epochs\n",
      "2023-07-08 04:01:12,067 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:01:12,067 | INFO : Epoch(val) [10][72]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 0.9762, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9997, accuracy: 0.9996, current_iters: 720\n",
      "2023-07-08 04:01:12,073 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:01:21,558 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:01:21,558 | INFO : Epoch [11][72/72]\tlr: 4.752e-03, eta: 0:17:10, time: 0.132, data_time: 0.004, memory: 3668, current_iters: 791, loss: 0.0259, sharpness: 0.0823, max_loss: 0.1085\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1308.0 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:01:25,147 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:01:25,148 | INFO : Epoch(val) [11][72]\taccuracy_top-1: 0.9989, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9184, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9992, accuracy: 0.9989, current_iters: 792\n",
      "2023-07-08 04:01:25,153 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:01:34,649 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:01:34,649 | INFO : Epoch [12][72/72]\tlr: 4.722e-03, eta: 0:16:58, time: 0.132, data_time: 0.003, memory: 3668, current_iters: 863, loss: 0.0219, sharpness: 0.0751, max_loss: 0.0971\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1329.9 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:01:38,109 | INFO : Saving best checkpoint at 12 epochs\n",
      "2023-07-08 04:01:38,260 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:01:38,260 | INFO : Epoch(val) [12][72]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9796, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9998, accuracy: 0.9996, current_iters: 864\n",
      "2023-07-08 04:01:38,266 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:01:47,834 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:01:47,834 | INFO : Epoch [13][72/72]\tlr: 4.688e-03, eta: 0:16:47, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 935, loss: 0.0174, sharpness: 0.0714, max_loss: 0.0894\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1340.7 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:01:51,265 | INFO : Saving best checkpoint at 13 epochs\n",
      "2023-07-08 04:01:51,419 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:01:51,419 | INFO : Epoch(val) [13][72]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 0.9706, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9997, accuracy: 0.9996, current_iters: 936\n",
      "2023-07-08 04:01:51,425 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:02:00,995 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:02:00,995 | INFO : Epoch [14][72/72]\tlr: 4.652e-03, eta: 0:16:35, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 1007, loss: 0.0168, sharpness: 0.0707, max_loss: 0.0875\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1316.6 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:02:04,488 | INFO : Saving best checkpoint at 14 epochs\n",
      "2023-07-08 04:02:04,636 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:02:04,636 | INFO : Epoch(val) [14][72]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 0.9706, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9997, accuracy: 0.9996, current_iters: 1008\n",
      "2023-07-08 04:02:04,642 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:02:14,402 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:02:14,402 | INFO : Epoch [15][72/72]\tlr: 4.613e-03, eta: 0:16:24, time: 0.136, data_time: 0.004, memory: 3668, current_iters: 1079, loss: 0.0143, sharpness: 0.0672, max_loss: 0.0815\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1322.3 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:02:17,880 | INFO : Saving best checkpoint at 15 epochs\n",
      "2023-07-08 04:02:18,040 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:02:18,040 | INFO : Epoch(val) [15][72]\taccuracy_top-1: 0.9998, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 0.9998, current_iters: 1080\n",
      "2023-07-08 04:02:18,046 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:02:27,683 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:02:27,683 | INFO : Epoch [16][72/72]\tlr: 4.572e-03, eta: 0:16:12, time: 0.134, data_time: 0.003, memory: 3668, current_iters: 1151, loss: 0.0135, sharpness: 0.0682, max_loss: 0.0817\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1339.0 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:02:31,116 | INFO : Saving best checkpoint at 16 epochs\n",
      "2023-07-08 04:02:31,262 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:02:31,262 | INFO : Epoch(val) [16][72]\taccuracy_top-1: 0.9998, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 0.9998, current_iters: 1152\n",
      "2023-07-08 04:02:31,268 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:02:40,962 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:02:40,962 | INFO : Epoch [17][72/72]\tlr: 4.528e-03, eta: 0:16:00, time: 0.135, data_time: 0.004, memory: 3668, current_iters: 1223, loss: 0.0133, sharpness: 0.0634, max_loss: 0.0767\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1324.7 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:02:44,437 | INFO : Saving best checkpoint at 17 epochs\n",
      "2023-07-08 04:02:44,604 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:02:44,604 | INFO : Epoch(val) [17][72]\taccuracy_top-1: 0.9998, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 0.9952, Faces_easy accuracy: 1.0000, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 0.9998, current_iters: 1224\n",
      "2023-07-08 04:02:44,610 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:02:54,321 | WARNING : training progress 20%\n",
      "2023-07-08 04:02:54,322 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:02:54,322 | INFO : Epoch [18][72/72]\tlr: 4.481e-03, eta: 0:15:48, time: 0.135, data_time: 0.004, memory: 3668, current_iters: 1295, loss: 0.0124, sharpness: 0.0689, max_loss: 0.0813\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1326.1 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:02:57,790 | INFO : Saving best checkpoint at 18 epochs\n",
      "2023-07-08 04:02:57,946 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:02:57,946 | INFO : Epoch(val) [18][72]\taccuracy_top-1: 0.9998, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 0.9952, Faces_easy accuracy: 1.0000, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 0.9998, current_iters: 1296\n",
      "2023-07-08 04:02:57,952 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:03:07,601 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:03:07,601 | INFO : Epoch [19][72/72]\tlr: 4.432e-03, eta: 0:15:36, time: 0.134, data_time: 0.004, memory: 3668, current_iters: 1367, loss: 0.0088, sharpness: 0.0531, max_loss: 0.0618\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1310.1 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:03:11,113 | INFO : Saving best checkpoint at 19 epochs\n",
      "2023-07-08 04:03:11,267 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:03:11,268 | INFO : Epoch(val) [19][72]\taccuracy_top-1: 0.9998, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 0.9952, Faces_easy accuracy: 1.0000, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 0.9998, current_iters: 1368\n",
      "2023-07-08 04:03:11,274 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:03:20,969 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:03:20,970 | INFO : Epoch [20][72/72]\tlr: 4.381e-03, eta: 0:15:24, time: 0.135, data_time: 0.004, memory: 3668, current_iters: 1439, loss: 0.0110, sharpness: 0.0645, max_loss: 0.0757\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1303.1 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:03:24,500 | INFO : Saving best checkpoint at 20 epochs\n",
      "2023-07-08 04:03:24,671 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:03:24,672 | INFO : Epoch(val) [20][72]\taccuracy_top-1: 0.9998, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 0.9998, current_iters: 1440\n",
      "2023-07-08 04:03:24,678 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:03:34,420 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:03:34,420 | INFO : Epoch [21][72/72]\tlr: 4.327e-03, eta: 0:15:12, time: 0.135, data_time: 0.004, memory: 3668, current_iters: 1511, loss: 0.0109, sharpness: 0.0560, max_loss: 0.0670\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1312.5 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:03:37,924 | INFO : Saving best checkpoint at 21 epochs\n",
      "2023-07-08 04:03:38,082 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:03:38,083 | INFO : Epoch(val) [21][72]\taccuracy_top-1: 0.9998, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 0.9998, current_iters: 1512\n",
      "2023-07-08 04:03:38,089 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:03:47,847 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:03:47,847 | INFO : Epoch [22][72/72]\tlr: 4.271e-03, eta: 0:14:59, time: 0.136, data_time: 0.004, memory: 3668, current_iters: 1583, loss: 0.0085, sharpness: 0.0544, max_loss: 0.0629\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1303.2 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:03:51,375 | INFO : Saving best checkpoint at 22 epochs\n",
      "2023-07-08 04:03:51,528 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:03:51,528 | INFO : Epoch(val) [22][72]\taccuracy_top-1: 0.9998, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 0.9998, current_iters: 1584\n",
      "2023-07-08 04:03:51,534 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:04:01,305 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:04:01,305 | INFO : Epoch [23][72/72]\tlr: 4.212e-03, eta: 0:14:47, time: 0.136, data_time: 0.004, memory: 3668, current_iters: 1655, loss: 0.0080, sharpness: 0.0496, max_loss: 0.0576\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1296.6 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:04:04,852 | INFO : Saving best checkpoint at 23 epochs\n",
      "2023-07-08 04:04:05,008 | INFO : \n",
      "Early Stopping at :22 with best accuracy: 0.9997812652587891\n",
      "2023-07-08 04:04:05,008 | INFO : Exp name: outputs_random/logs\n",
      "2023-07-08 04:04:05,008 | INFO : Epoch(val) [23][72]\taccuracy_top-1: 0.9998, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9952, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 0.9998, current_iters: 1656\n",
      "2023-07-08 04:04:05,014 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:04:06,083 | INFO : called save_model\n",
      "2023-07-08 04:04:06,187 | INFO : Final model performance: Performance(score: 0.9997812652587891, dashboard: (115 metric groups))\n",
      "2023-07-08 04:04:06,187 | INFO : train done.\n",
      "otx train time elapsed:  0:05:16.034499\n",
      "otx train CLI report has been generated: outputs_random/cli_report.log\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/tempfile.py:833: ResourceWarning: Implicitly cleaning up <TemporaryDirectory '/tmp/img-cache-on7bagrf'>\n",
      "  _warnings.warn(warn_message, ResourceWarning)\n"
     ]
    }
   ],
   "source": [
    "!otx train EfficientNet-B0 \\\n",
    "    --train-data-roots random_result \\\n",
    "    --val-data-roots random_result \\\n",
    "    -o outputs_random"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c7888114",
   "metadata": {},
   "source": [
    "When pruning the dataset using the `random` method with a ratio of `0.5`, the best accuracy achieved is `0.9997812652587891` and the training time is 5 minutes and 16 seconds. There was a 46.55% reduction in time while maintaining similar performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9a2fb104",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*] Workspace Path: otx-workspace-CLASSIFICATION\n",
      "[*] Load Model Template ID: Custom_Image_Classification_EfficinetNet-B0\n",
      "[*] Load Model Name: EfficientNet-B0\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details.\n",
      "  warnings.warn(\n",
      "2023-07-08 04:14:17,920 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model.py\n",
      "2023-07-08 04:14:17,949 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model_multilabel.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/data_pipeline.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/deployment.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/hpo_config.yaml\n",
      "2023-07-08 04:14:18,048 | WARNING : Duplicate key is detected among bases [{'model'}]\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/model_hierarchical.py\n",
      "[*] \t- Updated: otx-workspace-CLASSIFICATION/compression_config.json\n",
      "[*] Update data configuration file to: otx-workspace-CLASSIFICATION/data.yaml\n",
      "2023-07-08 04:14:18.894798: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/openvino/pyopenvino/__init__.py:10: FutureWarning: The module is private and following namespace `pyopenvino` will be removed in the future\n",
      "  warnings.warn(message=\"The module is private and following namespace \" \"`pyopenvino` will be removed in the future\", category=FutureWarning)\n",
      "2023-07-08 04:14:21,041 | INFO : Classification mode: multiclass\n",
      "2023-07-08 04:14:21,041 | INFO : train()\n",
      "2023-07-08 04:14:21,052 | INFO : Training seed was set to 5 w/ deterministic=False.\n",
      "2023-07-08 04:14:21,054 | INFO : Try to create a 0 size memory pool.\n",
      "2023-07-08 04:14:21,834 | INFO : configure!: training=True\n",
      "2023-07-08 04:14:21,943 | INFO : init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n",
      "2023-07-08 04:14:21,969 | INFO : 'in_channels' config in model.head is updated from -1 to 1280\n",
      "2023-07-08 04:14:21,970 | INFO : configure_data()\n",
      "2023-07-08 04:14:21,970 | INFO : task config!!!!: training=True\n",
      "2023-07-08 04:14:21,971 | INFO : train!\n",
      "2023-07-08 04:14:21,971 | INFO : cfg.gpu_ids = range(0, 1), distributed = False\n",
      "2023-07-08 04:14:21,989 | INFO : Environment info:\n",
      "------------------------------------------------------------\n",
      "sys.platform: linux\n",
      "Python: 3.10.0 (default, Mar  3 2022, 09:58:08) [GCC 7.5.0]\n",
      "CUDA available: True\n",
      "GPU 0,1: GeForce RTX 3090\n",
      "CUDA_HOME: /usr/local/cuda\n",
      "NVCC: Cuda compilation tools, release 11.1, V11.1.74\n",
      "GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
      "PyTorch: 1.13.1+cu117\n",
      "PyTorch compiling details: PyTorch built with:\n",
      "  - GCC 9.3\n",
      "  - C++ Version: 201402\n",
      "  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n",
      "  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\n",
      "  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n",
      "  - LAPACK is enabled (usually provided by MKL)\n",
      "  - NNPACK is enabled\n",
      "  - CPU capability usage: AVX2\n",
      "  - CUDA Runtime 11.7\n",
      "  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n",
      "  - CuDNN 8.5\n",
      "  - Magma 2.6.1\n",
      "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n",
      "\n",
      "TorchVision: 0.14.1+cu117\n",
      "OpenCV: 4.7.0\n",
      "MMCV: 1.7.1\n",
      "MMCV Compiler: GCC 7.5\n",
      "MMCV CUDA Compiler: 11.1\n",
      "MMClassification: 0.25.0+c5ac764\n",
      "------------------------------------------------------------\n",
      "\n",
      "2023-07-08 04:14:22,225 | INFO : init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n",
      "2023-07-08 04:14:22,234 - mmcv - INFO - initialize CustomLinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\n",
      "2023-07-08 04:14:22,235 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.conv.weight - torch.Size([32, 3, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,235 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.bn.weight - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,235 - mmcv - INFO - \n",
      "backbone.features.init_block.conv.bn.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,235 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.conv.weight - torch.Size([32, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,235 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.bn.weight - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,235 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.dw_conv.bn.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv1.weight - torch.Size([8, 32, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv1.bias - torch.Size([8]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv2.weight - torch.Size([32, 8, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.se.conv2.bias - torch.Size([32]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.conv.weight - torch.Size([16, 32, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.bn.weight - torch.Size([16]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage1.unit1.pw_conv.bn.bias - torch.Size([16]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.conv.weight - torch.Size([96, 16, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.bn.weight - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv1.bn.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.conv.weight - torch.Size([96, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.bn.weight - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv2.bn.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv1.weight - torch.Size([4, 96, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv1.bias - torch.Size([4]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv2.weight - torch.Size([96, 4, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.se.conv2.bias - torch.Size([96]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.conv.weight - torch.Size([24, 96, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.bn.weight - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit1.conv3.bn.bias - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.conv.weight - torch.Size([144, 24, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv1.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.conv.weight - torch.Size([144, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv2.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv1.weight - torch.Size([6, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv1.bias - torch.Size([6]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv2.weight - torch.Size([144, 6, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,236 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.se.conv2.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.conv.weight - torch.Size([24, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.bn.weight - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage2.unit2.conv3.bn.bias - torch.Size([24]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.conv.weight - torch.Size([144, 24, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv1.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.conv.weight - torch.Size([144, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.bn.weight - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv2.bn.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv1.weight - torch.Size([6, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv1.bias - torch.Size([6]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv2.weight - torch.Size([144, 6, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.se.conv2.bias - torch.Size([144]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.conv.weight - torch.Size([40, 144, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.bn.weight - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit1.conv3.bn.bias - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.conv.weight - torch.Size([240, 40, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv1.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.conv.weight - torch.Size([240, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv2.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv1.weight - torch.Size([10, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv1.bias - torch.Size([10]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv2.weight - torch.Size([240, 10, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.se.conv2.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.conv.weight - torch.Size([40, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.bn.weight - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage3.unit2.conv3.bn.bias - torch.Size([40]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,237 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.conv.weight - torch.Size([240, 40, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv1.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.conv.weight - torch.Size([240, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.bn.weight - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv2.bn.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv1.weight - torch.Size([10, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv1.bias - torch.Size([10]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv2.weight - torch.Size([240, 10, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.se.conv2.bias - torch.Size([240]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.conv.weight - torch.Size([80, 240, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit1.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.conv.weight - torch.Size([480, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.conv.weight - torch.Size([80, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit2.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.conv.weight - torch.Size([480, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,238 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.conv.weight - torch.Size([80, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.bn.weight - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit3.conv3.bn.bias - torch.Size([80]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.conv.weight - torch.Size([480, 80, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv1.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.conv.weight - torch.Size([480, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.bn.weight - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv2.bn.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv1.weight - torch.Size([20, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv1.bias - torch.Size([20]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv2.weight - torch.Size([480, 20, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.se.conv2.bias - torch.Size([480]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.conv.weight - torch.Size([112, 480, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit4.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,239 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.conv.weight - torch.Size([112, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit5.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.conv.weight - torch.Size([112, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.bn.weight - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage4.unit6.conv3.bn.bias - torch.Size([112]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.conv.weight - torch.Size([672, 112, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv1.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.conv.weight - torch.Size([672, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.bn.weight - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv2.bn.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv1.weight - torch.Size([28, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv1.bias - torch.Size([28]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv2.weight - torch.Size([672, 28, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.se.conv2.bias - torch.Size([672]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.conv.weight - torch.Size([192, 672, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit1.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,240 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.conv.weight - torch.Size([1152, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv3.conv.weight - torch.Size([192, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit2.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv2.conv.weight - torch.Size([1152, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv3.conv.weight - torch.Size([192, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit3.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv2.conv.weight - torch.Size([1152, 1, 5, 5]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,241 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv3.conv.weight - torch.Size([192, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv3.bn.weight - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit4.conv3.bn.bias - torch.Size([192]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv1.conv.weight - torch.Size([1152, 192, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv1.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv1.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv2.conv.weight - torch.Size([1152, 1, 3, 3]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv2.bn.weight - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv2.bn.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv1.weight - torch.Size([48, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv1.bias - torch.Size([48]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv2.weight - torch.Size([1152, 48, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.se.conv2.bias - torch.Size([1152]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv3.conv.weight - torch.Size([320, 1152, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv3.bn.weight - torch.Size([320]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.stage5.unit5.conv3.bn.bias - torch.Size([320]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.final_block.conv.weight - torch.Size([1280, 320, 1, 1]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.final_block.bn.weight - torch.Size([1280]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "backbone.features.final_block.bn.bias - torch.Size([1280]): \n",
      "The value is the same before and after calling `init_weights` of SAMImageClassifier  \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "head.fc.weight - torch.Size([102, 1280]): \n",
      "NormalInit: mean=0, std=0.01, bias=0 \n",
      " \n",
      "2023-07-08 04:14:22,242 - mmcv - INFO - \n",
      "head.fc.bias - torch.Size([102]): \n",
      "NormalInit: mean=0, std=0.01, bias=0 \n",
      " \n",
      "2023-07-08 04:14:23,625 | INFO : Task Adaptation: [] => ['BACKGROUND_Google', 'Faces', 'Faces_easy', 'Leopards', 'Motorbikes', 'accordion', 'airplanes', 'anchor', 'ant', 'barrel', 'bass', 'beaver', 'binocular', 'bonsai', 'brain', 'brontosaurus', 'buddha', 'butterfly', 'camera', 'cannon', 'car_side', 'ceiling_fan', 'cellphone', 'chair', 'chandelier', 'cougar_body', 'cougar_face', 'crab', 'crayfish', 'crocodile', 'crocodile_head', 'cup', 'dalmatian', 'dollar_bill', 'dolphin', 'dragonfly', 'electric_guitar', 'elephant', 'emu', 'euphonium', 'ewer', 'ferry', 'flamingo', 'flamingo_head', 'garfield', 'gerenuk', 'gramophone', 'grand_piano', 'hawksbill', 'headphone', 'hedgehog', 'helicopter', 'ibis', 'inline_skate', 'joshua_tree', 'kangaroo', 'ketch', 'lamp', 'laptop', 'llama', 'lobster', 'lotus', 'mandolin', 'mayfly', 'menorah', 'metronome', 'minaret', 'nautilus', 'octopus', 'okapi', 'pagoda', 'panda', 'pigeon', 'pizza', 'platypus', 'pyramid', 'revolver', 'rhino', 'rooster', 'saxophone', 'schooner', 'scissors', 'scorpion', 'sea_horse', 'snoopy', 'soccer_ball', 'stapler', 'starfish', 'stegosaurus', 'stop_sign', 'strawberry', 'sunflower', 'tick', 'trilobite', 'umbrella', 'watch', 'water_lilly', 'wheelchair', 'wild_cat', 'windsor_chair', 'wrench', 'yin_yang']\n",
      "2023-07-08 04:14:23,625 | INFO : - Efficient Mode: True\n",
      "2023-07-08 04:14:23,625 | INFO : - Sampler type: balanced\n",
      "2023-07-08 04:14:23,625 | INFO : - Sampler flag: False\n",
      "2023-07-08 04:14:23,625 - mmcls - INFO - Start running, host: dwekr@sooah-desktop, work_dir: /home/dwekr/workspace/datum/output_centroid/logs\n",
      "2023-07-08 04:14:23,626 - mmcls - INFO - Hooks will be executed in the following order:\n",
      "before_run:\n",
      "(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       \n",
      "(ABOVE_NORMAL) Fp16SAMOptimizerHook               \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) CheckpointHookWithValResults       \n",
      "(NORMAL      ) CancelInterfaceHook                \n",
      "(NORMAL      ) AdaptiveTrainSchedulingHook        \n",
      "(NORMAL      ) LoggerReplaceHook                  \n",
      "(71          ) OTXProgressHook                    \n",
      "(75          ) LazyEarlyStoppingHook              \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      " -------------------- \n",
      "before_train_epoch:\n",
      "(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) TaskAdaptHook                      \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      "(LOWEST      ) ForceTrainModeHook                 \n",
      " -------------------- \n",
      "before_train_iter:\n",
      "(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) AdaptiveTrainSchedulingHook        \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      " -------------------- \n",
      "after_train_iter:\n",
      "(ABOVE_NORMAL) Fp16SAMOptimizerHook               \n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) CheckpointHookWithValResults       \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      "(75          ) LazyEarlyStoppingHook              \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      " -------------------- \n",
      "after_train_epoch:\n",
      "(ABOVE_NORMAL) CustomEvalHook                     \n",
      "(NORMAL      ) CheckpointHookWithValResults       \n",
      "(71          ) OTXProgressHook                    \n",
      "(75          ) LazyEarlyStoppingHook              \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      " -------------------- \n",
      "before_val_epoch:\n",
      "(NORMAL      ) TaskAdaptHook                      \n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      " -------------------- \n",
      "before_val_iter:\n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      " -------------------- \n",
      "after_val_iter:\n",
      "(LOW         ) IterTimerHook                      \n",
      "(71          ) OTXProgressHook                    \n",
      " -------------------- \n",
      "after_val_epoch:\n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      "(VERY_LOW    ) OTXLoggerHook                      \n",
      "(VERY_LOW    ) MemCacheHook                       \n",
      " -------------------- \n",
      "after_run:\n",
      "(NORMAL      ) CancelInterfaceHook                \n",
      "(71          ) OTXProgressHook                    \n",
      "(VERY_LOW    ) TextLoggerHook                     \n",
      "(VERY_LOW    ) TensorboardLoggerHook              \n",
      " -------------------- \n",
      "2023-07-08 04:14:23,626 - mmcls - INFO - workflow: [('train', 1)], max: 90 epochs\n",
      "2023-07-08 04:14:23,626 | INFO : cancel hook is initialized\n",
      "2023-07-08 04:14:23,626 | INFO : logger in the runner is replaced to the MPA logger\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/torch/utils/tensorboard/__init__.py:4: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  if not hasattr(tensorboard, \"__version__\") or LooseVersion(\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/site-packages/torch/utils/tensorboard/__init__.py:6: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  ) < LooseVersion(\"1.15\"):\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 999.7 task/s, elapsed: 5s, ETA:     0s\n",
      "2023-07-08 04:14:36,941 | WARNING : training progress 1%\n",
      "2023-07-08 04:14:37,821 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:14:37,821 | INFO : Epoch [1][72/72]\tlr: 4.900e-03, eta: 0:19:46, time: 0.133, data_time: 0.005, memory: 3668, current_iters: 71, loss: 2.2499, sharpness: 0.2340, max_loss: 2.4720\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1318.3 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:14:41,309 | INFO : Saving best checkpoint at 1 epochs\n",
      "2023-07-08 04:14:41,443 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:14:41,443 | INFO : Epoch(val) [1][72]\taccuracy_top-1: 0.8497, accuracy_top-5: 0.9538, BACKGROUND_Google accuracy: 0.8674, Faces accuracy: 0.9930, Faces_easy accuracy: 0.9701, Leopards accuracy: 1.0000, Motorbikes accuracy: 0.9962, accordion accuracy: 0.1667, airplanes accuracy: 0.9967, anchor accuracy: 0.0000, ant accuracy: 0.0000, barrel accuracy: 0.0000, bass accuracy: 0.6786, beaver accuracy: 0.3750, binocular accuracy: 0.7059, bonsai accuracy: 0.9545, brain accuracy: 0.8958, brontosaurus accuracy: 0.0000, buddha accuracy: 1.0000, butterfly accuracy: 0.7333, camera accuracy: 0.9524, cannon accuracy: 0.5882, car_side accuracy: 1.0000, ceiling_fan accuracy: 0.3158, cellphone accuracy: 1.0000, chair accuracy: 0.0000, chandelier accuracy: 0.9706, cougar_body accuracy: 0.0000, cougar_face accuracy: 1.0000, crab accuracy: 0.0000, crayfish accuracy: 0.8222, crocodile accuracy: 0.0909, crocodile_head accuracy: 0.7000, cup accuracy: 0.8261, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 0.8333, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 0.3929, garfield accuracy: 0.0000, gerenuk accuracy: 0.0000, gramophone accuracy: 0.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 0.2500, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 0.9000, inline_skate accuracy: 0.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 0.2000, ketch accuracy: 0.9259, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 0.9630, lobster accuracy: 0.0000, lotus accuracy: 0.0870, mandolin accuracy: 0.7500, mayfly accuracy: 0.0000, menorah accuracy: 0.9492, metronome accuracy: 0.0000, minaret accuracy: 1.0000, nautilus accuracy: 0.9615, octopus accuracy: 0.0000, okapi accuracy: 0.0000, pagoda accuracy: 0.0000, panda accuracy: 0.7917, pigeon accuracy: 0.0000, pizza accuracy: 0.3636, platypus accuracy: 0.8621, pyramid accuracy: 0.9722, revolver accuracy: 0.9722, rhino accuracy: 0.4000, rooster accuracy: 0.0000, saxophone accuracy: 0.0000, schooner accuracy: 0.8393, scissors accuracy: 0.9583, scorpion accuracy: 0.9839, sea_horse accuracy: 0.0000, snoopy accuracy: 0.1579, soccer_ball accuracy: 1.0000, stapler accuracy: 0.0526, starfish accuracy: 1.0000, stegosaurus accuracy: 0.0588, stop_sign accuracy: 1.0000, strawberry accuracy: 0.7931, sunflower accuracy: 0.9744, tick accuracy: 1.0000, trilobite accuracy: 0.9778, umbrella accuracy: 0.9811, watch accuracy: 1.0000, water_lilly accuracy: 0.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 0.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 0.8667, yin_yang accuracy: 0.9487, mean accuracy: 0.6350, accuracy: 0.8497, current_iters: 72\n",
      "2023-07-08 04:14:41,449 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:14:50,681 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:14:50,681 | INFO : Epoch [2][72/72]\tlr: 4.899e-03, eta: 0:19:10, time: 0.128, data_time: 0.003, memory: 3668, current_iters: 143, loss: 0.5256, sharpness: 0.2175, max_loss: 0.7429\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1331.2 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:14:54,136 | INFO : Saving best checkpoint at 2 epochs\n",
      "2023-07-08 04:14:54,259 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:14:54,259 | INFO : Epoch(val) [2][72]\taccuracy_top-1: 0.9626, accuracy_top-5: 0.9974, BACKGROUND_Google accuracy: 0.9432, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9701, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 0.9412, ant accuracy: 0.9375, barrel accuracy: 0.8889, bass accuracy: 0.9643, beaver accuracy: 0.5000, binocular accuracy: 1.0000, bonsai accuracy: 0.9773, brain accuracy: 1.0000, brontosaurus accuracy: 0.8421, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 0.9412, car_side accuracy: 1.0000, ceiling_fan accuracy: 0.9474, cellphone accuracy: 1.0000, chair accuracy: 0.9231, chandelier accuracy: 0.9853, cougar_body accuracy: 0.0000, cougar_face accuracy: 1.0000, crab accuracy: 0.9375, crayfish accuracy: 0.9111, crocodile accuracy: 0.7727, crocodile_head accuracy: 1.0000, cup accuracy: 0.9565, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 0.8333, elephant accuracy: 1.0000, emu accuracy: 0.9722, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 0.4286, garfield accuracy: 0.2727, gerenuk accuracy: 0.0000, gramophone accuracy: 0.9412, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 0.8750, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 0.8182, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.8642, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 0.9815, lobster accuracy: 0.7222, lotus accuracy: 0.6522, mandolin accuracy: 0.9643, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 0.9615, octopus accuracy: 0.0769, okapi accuracy: 0.3846, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 0.9231, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 0.9722, rhino accuracy: 1.0000, rooster accuracy: 0.9000, saxophone accuracy: 1.0000, schooner accuracy: 0.9643, scissors accuracy: 1.0000, scorpion accuracy: 0.9677, sea_horse accuracy: 0.9565, snoopy accuracy: 0.8947, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 0.9906, water_lilly accuracy: 0.9231, wheelchair accuracy: 1.0000, wild_cat accuracy: 0.6364, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9139, accuracy: 0.9626, current_iters: 144\n",
      "2023-07-08 04:14:54,265 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:15:03,562 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:15:03,562 | INFO : Epoch [3][72/72]\tlr: 4.894e-03, eta: 0:18:52, time: 0.129, data_time: 0.003, memory: 3668, current_iters: 215, loss: 0.2172, sharpness: 0.1805, max_loss: 0.3978\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1328.5 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:15:07,024 | INFO : Saving best checkpoint at 3 epochs\n",
      "2023-07-08 04:15:07,147 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:15:07,147 | INFO : Epoch(val) [3][72]\taccuracy_top-1: 0.9880, accuracy_top-5: 0.9996, BACKGROUND_Google accuracy: 0.9886, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9851, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 0.9984, anchor accuracy: 1.0000, ant accuracy: 0.9375, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 0.9167, binocular accuracy: 1.0000, bonsai accuracy: 0.9773, brain accuracy: 1.0000, brontosaurus accuracy: 0.9474, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 0.9474, cellphone accuracy: 1.0000, chair accuracy: 0.9231, chandelier accuracy: 1.0000, cougar_body accuracy: 0.8000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.7727, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 0.9091, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.8765, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.7778, lotus accuracy: 0.5652, mandolin accuracy: 1.0000, mayfly accuracy: 0.9524, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 0.3846, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9771, accuracy: 0.9880, current_iters: 216\n",
      "2023-07-08 04:15:07,153 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:15:16,476 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:15:16,476 | INFO : Epoch [4][72/72]\tlr: 4.887e-03, eta: 0:18:38, time: 0.129, data_time: 0.004, memory: 3668, current_iters: 287, loss: 0.1223, sharpness: 0.1482, max_loss: 0.2723\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1328.9 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:15:19,936 | INFO : Saving best checkpoint at 4 epochs\n",
      "2023-07-08 04:15:20,056 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:15:20,056 | INFO : Epoch(val) [4][72]\taccuracy_top-1: 0.9923, accuracy_top-5: 0.9998, BACKGROUND_Google accuracy: 0.9962, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9776, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 0.9375, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 0.9583, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 0.9778, crocodile accuracy: 0.6364, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 0.9167, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 0.9875, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9877, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.8333, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 0.9231, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 0.8929, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.6154, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9867, accuracy: 0.9923, current_iters: 288\n",
      "2023-07-08 04:15:20,063 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:15:29,429 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:15:29,429 | INFO : Epoch [5][72/72]\tlr: 4.876e-03, eta: 0:18:25, time: 0.130, data_time: 0.004, memory: 3668, current_iters: 359, loss: 0.0850, sharpness: 0.1300, max_loss: 0.2150\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1323.2 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:15:32,904 | INFO : Saving best checkpoint at 5 epochs\n",
      "2023-07-08 04:15:33,029 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:15:33,029 | INFO : Epoch(val) [5][72]\taccuracy_top-1: 0.9976, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9925, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 0.9375, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 0.9615, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9630, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.9444, lotus accuracy: 0.9130, mandolin accuracy: 0.9643, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 0.9231, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9961, accuracy: 0.9976, current_iters: 360\n",
      "2023-07-08 04:15:33,035 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:15:42,458 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:15:42,458 | INFO : Epoch [6][72/72]\tlr: 4.863e-03, eta: 0:18:13, time: 0.131, data_time: 0.004, memory: 3668, current_iters: 431, loss: 0.0607, sharpness: 0.1179, max_loss: 0.1790\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1307.7 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:15:46,032 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:15:46,032 | INFO : Epoch(val) [6][72]\taccuracy_top-1: 0.9963, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9776, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 0.9375, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9545, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9630, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.8333, lotus accuracy: 1.0000, mandolin accuracy: 0.9643, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 0.9231, okapi accuracy: 1.0000, pagoda accuracy: 0.9375, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.7692, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9927, accuracy: 0.9963, current_iters: 432\n",
      "2023-07-08 04:15:46,038 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:15:55,469 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:15:55,469 | INFO : Epoch [7][72/72]\tlr: 4.846e-03, eta: 0:18:01, time: 0.131, data_time: 0.003, memory: 3668, current_iters: 503, loss: 0.0472, sharpness: 0.1066, max_loss: 0.1538\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1303.8 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:15:59,056 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:15:59,057 | INFO : Epoch(val) [7][72]\taccuracy_top-1: 0.9974, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9776, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 0.9545, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9383, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.8462, wheelchair accuracy: 1.0000, wild_cat accuracy: 0.9091, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9963, accuracy: 0.9974, current_iters: 504\n",
      "2023-07-08 04:15:59,062 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:16:08,495 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:16:08,495 | INFO : Epoch [8][72/72]\tlr: 4.827e-03, eta: 0:17:48, time: 0.131, data_time: 0.003, memory: 3668, current_iters: 575, loss: 0.0408, sharpness: 0.1056, max_loss: 0.1471\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1306.6 task/s, elapsed: 3s, ETA:     0s\n",
      "2023-07-08 04:16:12,014 | INFO : Saving best checkpoint at 8 epochs\n",
      "2023-07-08 04:16:12,139 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:16:12,139 | INFO : Epoch(val) [8][72]\taccuracy_top-1: 0.9985, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9925, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 0.9444, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 0.9444, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.8462, wheelchair accuracy: 1.0000, wild_cat accuracy: 0.9091, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9964, accuracy: 0.9985, current_iters: 576\n",
      "2023-07-08 04:16:12,145 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:16:21,603 | WARNING : training progress 10%\n",
      "2023-07-08 04:16:21,604 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:16:21,604 | INFO : Epoch [9][72/72]\tlr: 4.805e-03, eta: 0:17:36, time: 0.131, data_time: 0.004, memory: 3668, current_iters: 647, loss: 0.0395, sharpness: 0.0994, max_loss: 0.1389\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1293.7 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:16:25,159 | INFO : Saving best checkpoint at 9 epochs\n",
      "2023-07-08 04:16:25,285 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:16:25,285 | INFO : Epoch(val) [9][72]\taccuracy_top-1: 0.9991, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 0.9851, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 0.8462, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9983, accuracy: 0.9991, current_iters: 648\n",
      "2023-07-08 04:16:25,291 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:16:34,725 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:16:34,725 | INFO : Epoch [10][72/72]\tlr: 4.780e-03, eta: 0:17:24, time: 0.131, data_time: 0.004, memory: 3668, current_iters: 719, loss: 0.0317, sharpness: 0.0884, max_loss: 0.1215\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1275.3 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:16:38,330 | INFO : Saving best checkpoint at 10 epochs\n",
      "2023-07-08 04:16:38,454 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:16:38,454 | INFO : Epoch(val) [10][72]\taccuracy_top-1: 0.9996, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 1.0000, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 0.9753, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9998, accuracy: 0.9996, current_iters: 720\n",
      "2023-07-08 04:16:38,460 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:16:47,905 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:16:47,905 | INFO : Epoch [11][72/72]\tlr: 4.752e-03, eta: 0:17:11, time: 0.131, data_time: 0.004, memory: 3668, current_iters: 791, loss: 0.0247, sharpness: 0.0786, max_loss: 0.1035\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1284.3 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:16:51,484 | INFO : Saving best checkpoint at 11 epochs\n",
      "2023-07-08 04:16:51,605 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:16:51,605 | INFO : Epoch(val) [11][72]\taccuracy_top-1: 1.0000, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 1.0000, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 1.0000, current_iters: 792\n",
      "2023-07-08 04:16:51,611 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:17:01,058 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:17:01,058 | INFO : Epoch [12][72/72]\tlr: 4.722e-03, eta: 0:16:58, time: 0.131, data_time: 0.003, memory: 3668, current_iters: 863, loss: 0.0198, sharpness: 0.0739, max_loss: 0.0936\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1286.1 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:17:04,632 | INFO : Saving best checkpoint at 12 epochs\n",
      "2023-07-08 04:17:04,759 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:17:04,759 | INFO : Epoch(val) [12][72]\taccuracy_top-1: 1.0000, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 1.0000, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 1.0000, current_iters: 864\n",
      "2023-07-08 04:17:04,765 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:17:14,314 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:17:14,314 | INFO : Epoch [13][72/72]\tlr: 4.688e-03, eta: 0:16:47, time: 0.133, data_time: 0.003, memory: 3668, current_iters: 935, loss: 0.0211, sharpness: 0.0776, max_loss: 0.0986\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1288.6 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:17:17,883 | INFO : Saving best checkpoint at 13 epochs\n",
      "2023-07-08 04:17:18,011 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:17:18,011 | INFO : Epoch(val) [13][72]\taccuracy_top-1: 1.0000, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 1.0000, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 1.0000, current_iters: 936\n",
      "2023-07-08 04:17:18,017 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:17:27,684 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:17:27,684 | INFO : Epoch [14][72/72]\tlr: 4.652e-03, eta: 0:16:35, time: 0.134, data_time: 0.003, memory: 3668, current_iters: 1007, loss: 0.0185, sharpness: 0.0689, max_loss: 0.0872\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1279.8 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:17:31,340 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:17:31,340 | INFO : Epoch(val) [14][72]\taccuracy_top-1: 0.9998, accuracy_top-5: 0.9998, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 1.0000, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 0.9773, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9998, accuracy: 0.9998, current_iters: 1008\n",
      "2023-07-08 04:17:31,346 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:17:41,053 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:17:41,053 | INFO : Epoch [15][72/72]\tlr: 4.613e-03, eta: 0:16:24, time: 0.135, data_time: 0.004, memory: 3668, current_iters: 1079, loss: 0.0160, sharpness: 0.0755, max_loss: 0.0914\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1297.8 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:17:44,657 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:17:44,658 | INFO : Epoch(val) [15][72]\taccuracy_top-1: 0.9998, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 1.0000, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 0.9773, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 0.9998, accuracy: 0.9998, current_iters: 1080\n",
      "2023-07-08 04:17:44,663 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:17:54,273 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:17:54,273 | INFO : Epoch [16][72/72]\tlr: 4.572e-03, eta: 0:16:12, time: 0.133, data_time: 0.004, memory: 3668, current_iters: 1151, loss: 0.0141, sharpness: 0.0684, max_loss: 0.0826\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1270.9 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:17:57,891 | INFO : Saving best checkpoint at 16 epochs\n",
      "2023-07-08 04:17:58,021 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:17:58,021 | INFO : Epoch(val) [16][72]\taccuracy_top-1: 1.0000, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 1.0000, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 1.0000, current_iters: 1152\n",
      "2023-07-08 04:17:58,027 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:18:07,665 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:18:07,665 | INFO : Epoch [17][72/72]\tlr: 4.528e-03, eta: 0:16:00, time: 0.134, data_time: 0.004, memory: 3668, current_iters: 1223, loss: 0.0151, sharpness: 0.0687, max_loss: 0.0837\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1251.7 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:18:11,339 | INFO : Saving best checkpoint at 17 epochs\n",
      "2023-07-08 04:18:11,462 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:18:11,462 | INFO : Epoch(val) [17][72]\taccuracy_top-1: 1.0000, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 1.0000, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 1.0000, current_iters: 1224\n",
      "2023-07-08 04:18:11,468 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:18:21,134 | WARNING : training progress 20%\n",
      "2023-07-08 04:18:21,134 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:18:21,134 | INFO : Epoch [18][72/72]\tlr: 4.481e-03, eta: 0:15:48, time: 0.134, data_time: 0.003, memory: 3668, current_iters: 1295, loss: 0.0123, sharpness: 0.0603, max_loss: 0.0727\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1260.0 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:18:24,782 | INFO : Saving best checkpoint at 18 epochs\n",
      "2023-07-08 04:18:24,911 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:18:24,911 | INFO : Epoch(val) [18][72]\taccuracy_top-1: 1.0000, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 1.0000, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 1.0000, current_iters: 1296\n",
      "2023-07-08 04:18:24,917 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:18:34,623 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:18:34,624 | INFO : Epoch [19][72/72]\tlr: 4.432e-03, eta: 0:15:36, time: 0.135, data_time: 0.003, memory: 3668, current_iters: 1367, loss: 0.0105, sharpness: 0.0553, max_loss: 0.0659\n",
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 4572/4572, 1260.4 task/s, elapsed: 4s, ETA:     0s\n",
      "2023-07-08 04:18:38,272 | INFO : Saving best checkpoint at 19 epochs\n",
      "2023-07-08 04:18:38,403 | INFO : \n",
      "Early Stopping at :18 with best accuracy: 1.0\n",
      "2023-07-08 04:18:38,403 | INFO : Exp name: output_centroid/logs\n",
      "2023-07-08 04:18:38,403 | INFO : Epoch(val) [19][72]\taccuracy_top-1: 1.0000, accuracy_top-5: 1.0000, BACKGROUND_Google accuracy: 1.0000, Faces accuracy: 1.0000, Faces_easy accuracy: 1.0000, Leopards accuracy: 1.0000, Motorbikes accuracy: 1.0000, accordion accuracy: 1.0000, airplanes accuracy: 1.0000, anchor accuracy: 1.0000, ant accuracy: 1.0000, barrel accuracy: 1.0000, bass accuracy: 1.0000, beaver accuracy: 1.0000, binocular accuracy: 1.0000, bonsai accuracy: 1.0000, brain accuracy: 1.0000, brontosaurus accuracy: 1.0000, buddha accuracy: 1.0000, butterfly accuracy: 1.0000, camera accuracy: 1.0000, cannon accuracy: 1.0000, car_side accuracy: 1.0000, ceiling_fan accuracy: 1.0000, cellphone accuracy: 1.0000, chair accuracy: 1.0000, chandelier accuracy: 1.0000, cougar_body accuracy: 1.0000, cougar_face accuracy: 1.0000, crab accuracy: 1.0000, crayfish accuracy: 1.0000, crocodile accuracy: 1.0000, crocodile_head accuracy: 1.0000, cup accuracy: 1.0000, dalmatian accuracy: 1.0000, dollar_bill accuracy: 1.0000, dolphin accuracy: 1.0000, dragonfly accuracy: 1.0000, electric_guitar accuracy: 1.0000, elephant accuracy: 1.0000, emu accuracy: 1.0000, euphonium accuracy: 1.0000, ewer accuracy: 1.0000, ferry accuracy: 1.0000, flamingo accuracy: 1.0000, flamingo_head accuracy: 1.0000, garfield accuracy: 1.0000, gerenuk accuracy: 1.0000, gramophone accuracy: 1.0000, grand_piano accuracy: 1.0000, hawksbill accuracy: 1.0000, headphone accuracy: 1.0000, hedgehog accuracy: 1.0000, helicopter accuracy: 1.0000, ibis accuracy: 1.0000, inline_skate accuracy: 1.0000, joshua_tree accuracy: 1.0000, kangaroo accuracy: 1.0000, ketch accuracy: 1.0000, lamp accuracy: 1.0000, laptop accuracy: 1.0000, llama accuracy: 1.0000, lobster accuracy: 1.0000, lotus accuracy: 1.0000, mandolin accuracy: 1.0000, mayfly accuracy: 1.0000, menorah accuracy: 1.0000, metronome accuracy: 1.0000, minaret accuracy: 1.0000, nautilus accuracy: 1.0000, octopus accuracy: 1.0000, okapi accuracy: 1.0000, pagoda accuracy: 1.0000, panda accuracy: 1.0000, pigeon accuracy: 1.0000, pizza accuracy: 1.0000, platypus accuracy: 1.0000, pyramid accuracy: 1.0000, revolver accuracy: 1.0000, rhino accuracy: 1.0000, rooster accuracy: 1.0000, saxophone accuracy: 1.0000, schooner accuracy: 1.0000, scissors accuracy: 1.0000, scorpion accuracy: 1.0000, sea_horse accuracy: 1.0000, snoopy accuracy: 1.0000, soccer_ball accuracy: 1.0000, stapler accuracy: 1.0000, starfish accuracy: 1.0000, stegosaurus accuracy: 1.0000, stop_sign accuracy: 1.0000, strawberry accuracy: 1.0000, sunflower accuracy: 1.0000, tick accuracy: 1.0000, trilobite accuracy: 1.0000, umbrella accuracy: 1.0000, watch accuracy: 1.0000, water_lilly accuracy: 1.0000, wheelchair accuracy: 1.0000, wild_cat accuracy: 1.0000, windsor_chair accuracy: 1.0000, wrench accuracy: 1.0000, yin_yang accuracy: 1.0000, mean accuracy: 1.0000, accuracy: 1.0000, current_iters: 1368\n",
      "2023-07-08 04:18:38,410 | INFO : MemCacheHandlerBase uses 0 / 0 (0.0%) memory pool and store 0 items.\n",
      "2023-07-08 04:18:39,479 | INFO : called save_model\n",
      "2023-07-08 04:18:39,575 | INFO : Final model performance: Performance(score: 1.0, dashboard: (115 metric groups))\n",
      "2023-07-08 04:18:39,575 | INFO : train done.\n",
      "otx train time elapsed:  0:04:24.300252\n",
      "otx train CLI report has been generated: output_centroid/cli_report.log\n",
      "/home/dwekr/miniconda3/envs/datum/lib/python3.10/tempfile.py:833: ResourceWarning: Implicitly cleaning up <TemporaryDirectory '/tmp/img-cache-yla9hve1'>\n",
      "  _warnings.warn(warn_message, ResourceWarning)\n"
     ]
    }
   ],
   "source": [
    "!otx train EfficientNet-B0 \\\n",
    "    --train-data-roots centroid_result \\\n",
    "    --val-data-roots centroid_result \\\n",
    "    -o output_centroid"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a5e6811f",
   "metadata": {},
   "source": [
    "When pruning the dataset using the `cluster_random` method with a ratio of `0.5`, the best accuracy achieved is `1.0` and the training time is 4 minutes and 24 seconds. We achieved a time reduction of 46.55% while maintaining similar performance."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "da0789e5",
   "metadata": {},
   "source": [
    "When using only 50% of the entire dataset, both `random` and `cluster_random` methods show performance that is sufficiently good compared to using the entire dataset. Therefore, by creating a representative subset, we can train the model in less time without significant drops in performance."
   ]
  }
 ],
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