Training - Effnet + Resnet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "29d84b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '6'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "190953ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: failed with initial frozen solve. Retrying with flexible solve.\n",
      "Solving environment: done\n",
      "Collecting package metadata (repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /opt/conda\n",
      "\n",
      "  added / updated specs:\n",
      "    - albumentations\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    albumentations-1.2.1       |     pyhd8ed1ab_0          86 KB  conda-forge\n",
      "    alsa-lib-1.2.3.2           |       h166bdaf_0         554 KB  conda-forge\n",
      "    blosc-1.21.1               |       hd32f23e_0         955 KB  conda-forge\n",
      "    brotli-1.0.9               |       h166bdaf_7          18 KB  conda-forge\n",
      "    brotli-bin-1.0.9           |       h166bdaf_7          19 KB  conda-forge\n",
      "    brunsli-0.1                |       h9c3ff4c_0         200 KB  conda-forge\n",
      "    c-blosc2-2.0.4             |       h5f21a17_1         183 KB  conda-forge\n",
      "    ca-certificates-2022.6.15  |       ha878542_0         149 KB  conda-forge\n",
      "    cairo-1.16.0               |    ha12eb4b_1010         1.5 MB  conda-forge\n",
      "    certifi-2022.6.15          |   py38h578d9bd_0         155 KB  conda-forge\n",
      "    cfitsio-4.0.0              |       h9a35b8e_0         1.3 MB  conda-forge\n",
      "    charls-2.2.0               |       h9c3ff4c_0         138 KB  conda-forge\n",
      "    cloudpickle-2.1.0          |     pyhd8ed1ab_0          25 KB  conda-forge\n",
      "    conda-4.12.0               |   py38h578d9bd_0         1.0 MB  conda-forge\n",
      "    cytoolz-0.11.2             |   py38h0a891b7_2         398 KB  conda-forge\n",
      "    dask-core-2022.7.1         |     pyhd8ed1ab_0         840 KB  conda-forge\n",
      "    dbus-1.13.6                |       h5008d03_3         604 KB  conda-forge\n",
      "    expat-2.4.8                |       h27087fc_0         187 KB  conda-forge\n",
      "    ffmpeg-4.3.2               |       h37c90e5_3         9.9 MB  conda-forge\n",
      "    font-ttf-dejavu-sans-mono-2.37|       hab24e00_0         388 KB  conda-forge\n",
      "    font-ttf-inconsolata-3.000 |       h77eed37_0          94 KB  conda-forge\n",
      "    font-ttf-source-code-pro-2.038|       h77eed37_0         684 KB  conda-forge\n",
      "    font-ttf-ubuntu-0.83       |       hab24e00_0         1.9 MB  conda-forge\n",
      "    fontconfig-2.14.0          |       h8e229c2_0         305 KB  conda-forge\n",
      "    fonts-conda-ecosystem-1    |                0           4 KB  conda-forge\n",
      "    fonts-conda-forge-1        |                0           4 KB  conda-forge\n",
      "    freeglut-3.2.2             |       h9c3ff4c_1         162 KB  conda-forge\n",
      "    freetype-2.10.4            |       h0708190_1         890 KB  conda-forge\n",
      "    fribidi-1.0.10             |       h36c2ea0_0         112 KB  conda-forge\n",
      "    fsspec-2022.5.0            |     pyhd8ed1ab_0          96 KB  conda-forge\n",
      "    gettext-0.19.8.1           |    h73d1719_1008         3.6 MB  conda-forge\n",
      "    giflib-5.2.1               |       h36c2ea0_2          77 KB  conda-forge\n",
      "    gmp-6.2.1                  |       h58526e2_0         806 KB  conda-forge\n",
      "    gnutls-3.6.13              |       h85f3911_1         2.0 MB  conda-forge\n",
      "    graphite2-1.3.13           |    h58526e2_1001         102 KB  conda-forge\n",
      "    gst-plugins-base-1.18.5    |       hf529b03_3         2.6 MB  conda-forge\n",
      "    gstreamer-1.18.5           |       h9f60fe5_3         2.0 MB  conda-forge\n",
      "    harfbuzz-4.2.0             |       h40b6f09_0         2.0 MB  conda-forge\n",
      "    hdf5-1.12.1                |nompi_h2750804_103         3.6 MB  conda-forge\n",
      "    imagecodecs-2021.11.20     |   py38h678ac2f_1         7.7 MB  conda-forge\n",
      "    imageio-2.19.3             |     pyhcf75d05_0         3.2 MB  conda-forge\n",
      "    jasper-2.0.33              |       ha77e612_0         1.1 MB  conda-forge\n",
      "    jbig-2.1                   |    h7f98852_2003          43 KB  conda-forge\n",
      "    joblib-1.1.0               |     pyhd8ed1ab_0         210 KB  conda-forge\n",
      "    jpeg-9e                    |       h166bdaf_1         268 KB  conda-forge\n",
      "    jxrlib-1.1                 |       h7f98852_2         235 KB  conda-forge\n",
      "    lame-3.100                 |    h7f98852_1001         496 KB  conda-forge\n",
      "    lcms2-2.12                 |       hddcbb42_0         443 KB  conda-forge\n",
      "    lerc-3.0                   |       h9c3ff4c_0         216 KB  conda-forge\n",
      "    libaec-1.0.6               |       h9c3ff4c_0          45 KB  conda-forge\n",
      "    libbrotlicommon-1.0.9      |       h166bdaf_7          65 KB  conda-forge\n",
      "    libbrotlidec-1.0.9         |       h166bdaf_7          33 KB  conda-forge\n",
      "    libbrotlienc-1.0.9         |       h166bdaf_7         287 KB  conda-forge\n",
      "    libclang-13.0.1            |default_hc23dcda_0        11.1 MB  conda-forge\n",
      "    libdeflate-1.8             |       h7f98852_0          67 KB  conda-forge\n",
      "    libevent-2.1.10            |       h9b69904_4         1.1 MB  conda-forge\n",
      "    libglib-2.70.2             |       h174f98d_4         3.1 MB  conda-forge\n",
      "    libglu-9.0.0               |    he1b5a44_1001         413 KB  conda-forge\n",
      "    libimagequant-2.17.0       |       h7f98852_1          48 KB  conda-forge\n",
      "    liblapacke-3.9.0           |12_linux64_openblas          12 KB  conda-forge\n",
      "    libllvm13-13.0.1           |       hf817b99_2        33.6 MB  conda-forge\n",
      "    libogg-1.3.4               |       h7f98852_1         206 KB  conda-forge\n",
      "    libopencv-4.5.5            |   py38h9b278ad_6        35.1 MB  conda-forge\n",
      "    libopus-1.3.1              |       h7f98852_1         255 KB  conda-forge\n",
      "    libpng-1.6.37              |       h21135ba_2         306 KB  conda-forge\n",
      "    libpq-14.2                 |       hd57d9b9_0         3.0 MB  conda-forge\n",
      "    libprotobuf-3.19.4         |       h780b84a_0         2.6 MB  conda-forge\n",
      "    libtiff-4.3.0              |       h6f004c6_2         614 KB  conda-forge\n",
      "    libuuid-2.32.1             |    h7f98852_1000          28 KB  conda-forge\n",
      "    libvorbis-1.3.7            |       h9c3ff4c_0         280 KB  conda-forge\n",
      "    libwebp-1.2.2              |       h3452ae3_0          85 KB  conda-forge\n",
      "    libwebp-base-1.2.2         |       h7f98852_1         824 KB  conda-forge\n",
      "    libxcb-1.13                |    h7f98852_1004         391 KB  conda-forge\n",
      "    libxkbcommon-1.0.3         |       he3ba5ed_0         581 KB  conda-forge\n",
      "    libzopfli-1.0.3            |       h9c3ff4c_0         164 KB  conda-forge\n",
      "    locket-1.0.0               |     pyhd8ed1ab_0           8 KB  conda-forge\n",
      "    mysql-common-8.0.27        |       ha770c72_3         1.8 MB  conda-forge\n",
      "    mysql-libs-8.0.27          |       hfa10184_3         1.9 MB  conda-forge\n",
      "    nettle-3.6                 |       he412f7d_0         6.5 MB  conda-forge\n",
      "    networkx-2.8.5             |     pyhd8ed1ab_0         1.5 MB  conda-forge\n",
      "    nspr-4.32                  |       h9c3ff4c_1         233 KB  conda-forge\n",
      "    nss-3.74                   |       hb5efdd6_0         2.1 MB  conda-forge\n",
      "    opencv-4.5.5               |   py38h578d9bd_6          22 KB  conda-forge\n",
      "    openh264-2.1.1             |       h780b84a_0         1.5 MB  conda-forge\n",
      "    openjpeg-2.4.0             |       hb52868f_1         444 KB  conda-forge\n",
      "    openssl-1.1.1o             |       h166bdaf_0         2.1 MB  conda-forge\n",
      "    partd-1.2.0                |     pyhd8ed1ab_0          18 KB  conda-forge\n",
      "    pcre-8.45                  |       h9c3ff4c_0         253 KB  conda-forge\n",
      "    pillow-9.0.1               |   py38he2f12e7_1        44.7 MB  conda-forge\n",
      "    pixman-0.40.0              |       h36c2ea0_0         627 KB  conda-forge\n",
      "    pthread-stubs-0.4          |    h36c2ea0_1001           5 KB  conda-forge\n",
      "    py-opencv-4.5.5            |   py38h7f3c49e_6         1.1 MB  conda-forge\n",
      "    pywavelets-1.3.0           |   py38h71d37f0_1         4.4 MB  conda-forge\n",
      "    qt-5.12.9                  |       ha98a1a1_5        98.8 MB  conda-forge\n",
      "    qudida-0.0.4               |     pyhd8ed1ab_0           7 KB  conda-forge\n",
      "    scikit-image-0.19.2        |   py38h43a58ef_0        12.1 MB  conda-forge\n",
      "    scikit-learn-1.1.1         |   py38hf80bbf7_0         8.3 MB  conda-forge\n",
      "    snappy-1.1.9               |       hbd366e4_1          35 KB  conda-forge\n",
      "    sqlite-3.37.0              |       h9cd32fc_0         1.5 MB  conda-forge\n",
      "    threadpoolctl-3.1.0        |     pyh8a188c0_0          18 KB  conda-forge\n",
      "    tifffile-2022.5.4          |     pyhd8ed1ab_0         149 KB  conda-forge\n",
      "    toolz-0.12.0               |     pyhd8ed1ab_0          48 KB  conda-forge\n",
      "    x264-1!161.3030            |       h7f98852_1         2.5 MB  conda-forge\n",
      "    xorg-fixesproto-5.0        |    h7f98852_1002           9 KB  conda-forge\n",
      "    xorg-inputproto-2.3.2      |    h7f98852_1002          19 KB  conda-forge\n",
      "    xorg-kbproto-1.0.7         |    h7f98852_1002          27 KB  conda-forge\n",
      "    xorg-libice-1.0.10         |       h7f98852_0          58 KB  conda-forge\n",
      "    xorg-libsm-1.2.3           |    hd9c2040_1000          26 KB  conda-forge\n",
      "    xorg-libx11-1.7.2          |       h7f98852_0         941 KB  conda-forge\n",
      "    xorg-libxau-1.0.9          |       h7f98852_0          13 KB  conda-forge\n",
      "    xorg-libxdmcp-1.1.3        |       h7f98852_0          19 KB  conda-forge\n",
      "    xorg-libxext-1.3.4         |       h7f98852_1          54 KB  conda-forge\n",
      "    xorg-libxfixes-5.0.3       |    h7f98852_1004          18 KB  conda-forge\n",
      "    xorg-libxi-1.7.10          |       h7f98852_0          46 KB  conda-forge\n",
      "    xorg-libxrender-0.9.10     |    h7f98852_1003          32 KB  conda-forge\n",
      "    xorg-renderproto-0.11.1    |    h7f98852_1002           9 KB  conda-forge\n",
      "    xorg-xextproto-7.3.0       |    h7f98852_1002          28 KB  conda-forge\n",
      "    xorg-xproto-7.0.31         |    h7f98852_1007          73 KB  conda-forge\n",
      "    zfp-0.5.5                  |       h9c3ff4c_8         190 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:       341.5 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  albumentations     conda-forge/noarch::albumentations-1.2.1-pyhd8ed1ab_0\n",
      "  alsa-lib           conda-forge/linux-64::alsa-lib-1.2.3.2-h166bdaf_0\n",
      "  blosc              conda-forge/linux-64::blosc-1.21.1-hd32f23e_0\n",
      "  brotli             conda-forge/linux-64::brotli-1.0.9-h166bdaf_7\n",
      "  brotli-bin         conda-forge/linux-64::brotli-bin-1.0.9-h166bdaf_7\n",
      "  brunsli            conda-forge/linux-64::brunsli-0.1-h9c3ff4c_0\n",
      "  c-blosc2           conda-forge/linux-64::c-blosc2-2.0.4-h5f21a17_1\n",
      "  cairo              conda-forge/linux-64::cairo-1.16.0-ha12eb4b_1010\n",
      "  cfitsio            conda-forge/linux-64::cfitsio-4.0.0-h9a35b8e_0\n",
      "  charls             conda-forge/linux-64::charls-2.2.0-h9c3ff4c_0\n",
      "  cloudpickle        conda-forge/noarch::cloudpickle-2.1.0-pyhd8ed1ab_0\n",
      "  cytoolz            conda-forge/linux-64::cytoolz-0.11.2-py38h0a891b7_2\n",
      "  dask-core          conda-forge/noarch::dask-core-2022.7.1-pyhd8ed1ab_0\n",
      "  dbus               conda-forge/linux-64::dbus-1.13.6-h5008d03_3\n",
      "  ffmpeg             conda-forge/linux-64::ffmpeg-4.3.2-h37c90e5_3\n",
      "  font-ttf-dejavu-s~ conda-forge/noarch::font-ttf-dejavu-sans-mono-2.37-hab24e00_0\n",
      "  font-ttf-inconsol~ conda-forge/noarch::font-ttf-inconsolata-3.000-h77eed37_0\n",
      "  font-ttf-source-c~ conda-forge/noarch::font-ttf-source-code-pro-2.038-h77eed37_0\n",
      "  font-ttf-ubuntu    conda-forge/noarch::font-ttf-ubuntu-0.83-hab24e00_0\n",
      "  fontconfig         conda-forge/linux-64::fontconfig-2.14.0-h8e229c2_0\n",
      "  fonts-conda-ecosy~ conda-forge/noarch::fonts-conda-ecosystem-1-0\n",
      "  fonts-conda-forge  conda-forge/noarch::fonts-conda-forge-1-0\n",
      "  freeglut           conda-forge/linux-64::freeglut-3.2.2-h9c3ff4c_1\n",
      "  freetype           conda-forge/linux-64::freetype-2.10.4-h0708190_1\n",
      "  fribidi            conda-forge/linux-64::fribidi-1.0.10-h36c2ea0_0\n",
      "  fsspec             conda-forge/noarch::fsspec-2022.5.0-pyhd8ed1ab_0\n",
      "  gettext            conda-forge/linux-64::gettext-0.19.8.1-h73d1719_1008\n",
      "  giflib             conda-forge/linux-64::giflib-5.2.1-h36c2ea0_2\n",
      "  gmp                conda-forge/linux-64::gmp-6.2.1-h58526e2_0\n",
      "  gnutls             conda-forge/linux-64::gnutls-3.6.13-h85f3911_1\n",
      "  graphite2          conda-forge/linux-64::graphite2-1.3.13-h58526e2_1001\n",
      "  gst-plugins-base   conda-forge/linux-64::gst-plugins-base-1.18.5-hf529b03_3\n",
      "  gstreamer          conda-forge/linux-64::gstreamer-1.18.5-h9f60fe5_3\n",
      "  harfbuzz           conda-forge/linux-64::harfbuzz-4.2.0-h40b6f09_0\n",
      "  hdf5               conda-forge/linux-64::hdf5-1.12.1-nompi_h2750804_103\n",
      "  imagecodecs        conda-forge/linux-64::imagecodecs-2021.11.20-py38h678ac2f_1\n",
      "  imageio            conda-forge/noarch::imageio-2.19.3-pyhcf75d05_0\n",
      "  jasper             conda-forge/linux-64::jasper-2.0.33-ha77e612_0\n",
      "  jbig               conda-forge/linux-64::jbig-2.1-h7f98852_2003\n",
      "  joblib             conda-forge/noarch::joblib-1.1.0-pyhd8ed1ab_0\n",
      "  jpeg               conda-forge/linux-64::jpeg-9e-h166bdaf_1\n",
      "  jxrlib             conda-forge/linux-64::jxrlib-1.1-h7f98852_2\n",
      "  lame               conda-forge/linux-64::lame-3.100-h7f98852_1001\n",
      "  lcms2              conda-forge/linux-64::lcms2-2.12-hddcbb42_0\n",
      "  lerc               conda-forge/linux-64::lerc-3.0-h9c3ff4c_0\n",
      "  libaec             conda-forge/linux-64::libaec-1.0.6-h9c3ff4c_0\n",
      "  libbrotlicommon    conda-forge/linux-64::libbrotlicommon-1.0.9-h166bdaf_7\n",
      "  libbrotlidec       conda-forge/linux-64::libbrotlidec-1.0.9-h166bdaf_7\n",
      "  libbrotlienc       conda-forge/linux-64::libbrotlienc-1.0.9-h166bdaf_7\n",
      "  libclang           conda-forge/linux-64::libclang-13.0.1-default_hc23dcda_0\n",
      "  libdeflate         conda-forge/linux-64::libdeflate-1.8-h7f98852_0\n",
      "  libevent           conda-forge/linux-64::libevent-2.1.10-h9b69904_4\n",
      "  libglib            conda-forge/linux-64::libglib-2.70.2-h174f98d_4\n",
      "  libglu             conda-forge/linux-64::libglu-9.0.0-he1b5a44_1001\n",
      "  libimagequant      conda-forge/linux-64::libimagequant-2.17.0-h7f98852_1\n",
      "  liblapacke         conda-forge/linux-64::liblapacke-3.9.0-12_linux64_openblas\n",
      "  libllvm13          conda-forge/linux-64::libllvm13-13.0.1-hf817b99_2\n",
      "  libogg             conda-forge/linux-64::libogg-1.3.4-h7f98852_1\n",
      "  libopencv          conda-forge/linux-64::libopencv-4.5.5-py38h9b278ad_6\n",
      "  libopus            conda-forge/linux-64::libopus-1.3.1-h7f98852_1\n",
      "  libpng             conda-forge/linux-64::libpng-1.6.37-h21135ba_2\n",
      "  libpq              conda-forge/linux-64::libpq-14.2-hd57d9b9_0\n",
      "  libprotobuf        conda-forge/linux-64::libprotobuf-3.19.4-h780b84a_0\n",
      "  libtiff            conda-forge/linux-64::libtiff-4.3.0-h6f004c6_2\n",
      "  libuuid            conda-forge/linux-64::libuuid-2.32.1-h7f98852_1000\n",
      "  libvorbis          conda-forge/linux-64::libvorbis-1.3.7-h9c3ff4c_0\n",
      "  libwebp            conda-forge/linux-64::libwebp-1.2.2-h3452ae3_0\n",
      "  libwebp-base       conda-forge/linux-64::libwebp-base-1.2.2-h7f98852_1\n",
      "  libxcb             conda-forge/linux-64::libxcb-1.13-h7f98852_1004\n",
      "  libxkbcommon       conda-forge/linux-64::libxkbcommon-1.0.3-he3ba5ed_0\n",
      "  libzopfli          conda-forge/linux-64::libzopfli-1.0.3-h9c3ff4c_0\n",
      "  locket             conda-forge/noarch::locket-1.0.0-pyhd8ed1ab_0\n",
      "  mysql-common       conda-forge/linux-64::mysql-common-8.0.27-ha770c72_3\n",
      "  mysql-libs         conda-forge/linux-64::mysql-libs-8.0.27-hfa10184_3\n",
      "  nettle             conda-forge/linux-64::nettle-3.6-he412f7d_0\n",
      "  networkx           conda-forge/noarch::networkx-2.8.5-pyhd8ed1ab_0\n",
      "  nspr               conda-forge/linux-64::nspr-4.32-h9c3ff4c_1\n",
      "  nss                conda-forge/linux-64::nss-3.74-hb5efdd6_0\n",
      "  opencv             conda-forge/linux-64::opencv-4.5.5-py38h578d9bd_6\n",
      "  openh264           conda-forge/linux-64::openh264-2.1.1-h780b84a_0\n",
      "  openjpeg           conda-forge/linux-64::openjpeg-2.4.0-hb52868f_1\n",
      "  partd              conda-forge/noarch::partd-1.2.0-pyhd8ed1ab_0\n",
      "  pcre               conda-forge/linux-64::pcre-8.45-h9c3ff4c_0\n",
      "  pillow             conda-forge/linux-64::pillow-9.0.1-py38he2f12e7_1\n",
      "  pixman             conda-forge/linux-64::pixman-0.40.0-h36c2ea0_0\n",
      "  pthread-stubs      conda-forge/linux-64::pthread-stubs-0.4-h36c2ea0_1001\n",
      "  py-opencv          conda-forge/linux-64::py-opencv-4.5.5-py38h7f3c49e_6\n",
      "  pywavelets         conda-forge/linux-64::pywavelets-1.3.0-py38h71d37f0_1\n",
      "  qt                 conda-forge/linux-64::qt-5.12.9-ha98a1a1_5\n",
      "  qudida             conda-forge/noarch::qudida-0.0.4-pyhd8ed1ab_0\n",
      "  scikit-image       conda-forge/linux-64::scikit-image-0.19.2-py38h43a58ef_0\n",
      "  scikit-learn       conda-forge/linux-64::scikit-learn-1.1.1-py38hf80bbf7_0\n",
      "  snappy             conda-forge/linux-64::snappy-1.1.9-hbd366e4_1\n",
      "  threadpoolctl      conda-forge/noarch::threadpoolctl-3.1.0-pyh8a188c0_0\n",
      "  tifffile           conda-forge/noarch::tifffile-2022.5.4-pyhd8ed1ab_0\n",
      "  toolz              conda-forge/noarch::toolz-0.12.0-pyhd8ed1ab_0\n",
      "  x264               conda-forge/linux-64::x264-1!161.3030-h7f98852_1\n",
      "  xorg-fixesproto    conda-forge/linux-64::xorg-fixesproto-5.0-h7f98852_1002\n",
      "  xorg-inputproto    conda-forge/linux-64::xorg-inputproto-2.3.2-h7f98852_1002\n",
      "  xorg-kbproto       conda-forge/linux-64::xorg-kbproto-1.0.7-h7f98852_1002\n",
      "  xorg-libice        conda-forge/linux-64::xorg-libice-1.0.10-h7f98852_0\n",
      "  xorg-libsm         conda-forge/linux-64::xorg-libsm-1.2.3-hd9c2040_1000\n",
      "  xorg-libx11        conda-forge/linux-64::xorg-libx11-1.7.2-h7f98852_0\n",
      "  xorg-libxau        conda-forge/linux-64::xorg-libxau-1.0.9-h7f98852_0\n",
      "  xorg-libxdmcp      conda-forge/linux-64::xorg-libxdmcp-1.1.3-h7f98852_0\n",
      "  xorg-libxext       conda-forge/linux-64::xorg-libxext-1.3.4-h7f98852_1\n",
      "  xorg-libxfixes     conda-forge/linux-64::xorg-libxfixes-5.0.3-h7f98852_1004\n",
      "  xorg-libxi         conda-forge/linux-64::xorg-libxi-1.7.10-h7f98852_0\n",
      "  xorg-libxrender    conda-forge/linux-64::xorg-libxrender-0.9.10-h7f98852_1003\n",
      "  xorg-renderproto   conda-forge/linux-64::xorg-renderproto-0.11.1-h7f98852_1002\n",
      "  xorg-xextproto     conda-forge/linux-64::xorg-xextproto-7.3.0-h7f98852_1002\n",
      "  xorg-xproto        conda-forge/linux-64::xorg-xproto-7.0.31-h7f98852_1007\n",
      "  zfp                conda-forge/linux-64::zfp-0.5.5-h9c3ff4c_8\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  ca-certificates                      2021.10.8-ha878542_0 --> 2022.6.15-ha878542_0\n",
      "  certifi                          2021.10.8-py38h578d9bd_1 --> 2022.6.15-py38h578d9bd_0\n",
      "  conda                               4.10.3-py38h578d9bd_3 --> 4.12.0-py38h578d9bd_0\n",
      "  expat                                    2.4.1-h9c3ff4c_0 --> 2.4.8-h27087fc_0\n",
      "  openssl                                 1.1.1l-h7f98852_0 --> 1.1.1o-h166bdaf_0\n",
      "  sqlite                                  3.36.0-h9cd32fc_2 --> 3.37.0-h9cd32fc_0\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "imagecodecs-2021.11. | 7.7 MB    | ##################################### | 100% \n",
      "libprotobuf-3.19.4   | 2.6 MB    | ##################################### | 100% \n",
      "openjpeg-2.4.0       | 444 KB    | ##################################### | 100% \n",
      "xorg-libxext-1.3.4   | 54 KB     | ##################################### | 100% \n",
      "dask-core-2022.7.1   | 840 KB    | ##################################### | 100% \n",
      "mysql-libs-8.0.27    | 1.9 MB    | ##################################### | 100% \n",
      "font-ttf-ubuntu-0.83 | 1.9 MB    | ##################################### | 100% \n",
      "font-ttf-dejavu-sans | 388 KB    | ##################################### | 100% \n",
      "xorg-inputproto-2.3. | 19 KB     | ##################################### | 100% \n",
      "x264-1!161.3030      | 2.5 MB    | ##################################### | 100% \n",
      "xorg-fixesproto-5.0  | 9 KB      | ##################################### | 100% \n",
      "pthread-stubs-0.4    | 5 KB      | ##################################### | 100% \n",
      "partd-1.2.0          | 18 KB     | ##################################### | 100% \n",
      "harfbuzz-4.2.0       | 2.0 MB    | ##################################### | 100% \n",
      "expat-2.4.8          | 187 KB    | ##################################### | 100% \n",
      "imageio-2.19.3       | 3.2 MB    | ##################################### | 100% \n",
      "libwebp-1.2.2        | 85 KB     | ##################################### | 100% \n",
      "libzopfli-1.0.3      | 164 KB    | ##################################### | 100% \n",
      "lerc-3.0             | 216 KB    | ##################################### | 100% \n",
      "c-blosc2-2.0.4       | 183 KB    | ##################################### | 100% \n",
      "snappy-1.1.9         | 35 KB     | ##################################### | 100% \n",
      "libclang-13.0.1      | 11.1 MB   | ##################################### | 100% \n",
      "libopus-1.3.1        | 255 KB    | ##################################### | 100% \n",
      "py-opencv-4.5.5      | 1.1 MB    | ##################################### | 100% \n",
      "mysql-common-8.0.27  | 1.8 MB    | ##################################### | 100% \n",
      "gnutls-3.6.13        | 2.0 MB    | ##################################### | 100% \n",
      "nss-3.74             | 2.1 MB    | ##################################### | 100% \n",
      "xorg-libxau-1.0.9    | 13 KB     | ##################################### | 100% \n",
      "libxkbcommon-1.0.3   | 581 KB    | ##################################### | 100% \n",
      "opencv-4.5.5         | 22 KB     | ##################################### | 100% \n",
      "giflib-5.2.1         | 77 KB     | ##################################### | 100% \n",
      "libevent-2.1.10      | 1.1 MB    | ##################################### | 100% \n",
      "locket-1.0.0         | 8 KB      | ##################################### | 100% \n",
      "libpng-1.6.37        | 306 KB    | ##################################### | 100% \n",
      "charls-2.2.0         | 138 KB    | ##################################### | 100% \n",
      "openh264-2.1.1       | 1.5 MB    | ##################################### | 100% \n",
      "sqlite-3.37.0        | 1.5 MB    | ##################################### | 100% \n",
      "fontconfig-2.14.0    | 305 KB    | ##################################### | 100% \n",
      "xorg-libxi-1.7.10    | 46 KB     | ##################################### | 100% \n",
      "openssl-1.1.1o       | 2.1 MB    | ##################################### | 100% \n",
      "libopencv-4.5.5      | 35.1 MB   | ##################################### | 100% \n",
      "networkx-2.8.5       | 1.5 MB    | ##################################### | 100% \n",
      "libpq-14.2           | 3.0 MB    | ##################################### | 100% \n",
      "zfp-0.5.5            | 190 KB    | ##################################### | 100% \n",
      "pillow-9.0.1         | 44.7 MB   | ##################################### | 100% \n",
      "fonts-conda-ecosyste | 4 KB      | ##################################### | 100% \n",
      "libglib-2.70.2       | 3.1 MB    | ##################################### | 100% \n",
      "nspr-4.32            | 233 KB    | ##################################### | 100% \n",
      "cytoolz-0.11.2       | 398 KB    | ##################################### | 100% \n",
      "font-ttf-source-code | 684 KB    | ##################################### | 100% \n",
      "pcre-8.45            | 253 KB    | ##################################### | 100% \n",
      "jpeg-9e              | 268 KB    | ##################################### | 100% \n",
      "cairo-1.16.0         | 1.5 MB    | ##################################### | 100% \n",
      "lame-3.100           | 496 KB    | ##################################### | 100% \n",
      "xorg-renderproto-0.1 | 9 KB      | ##################################### | 100% \n",
      "fsspec-2022.5.0      | 96 KB     | ##################################### | 100% \n",
      "libimagequant-2.17.0 | 48 KB     | ##################################### | 100% \n",
      "xorg-libice-1.0.10   | 58 KB     | ##################################### | 100% \n",
      "libxcb-1.13          | 391 KB    | ##################################### | 100% \n",
      "gst-plugins-base-1.1 | 2.6 MB    | ##################################### | 100% \n",
      "albumentations-1.2.1 | 86 KB     | ##################################### | 100% \n",
      "brunsli-0.1          | 200 KB    | ##################################### | 100% \n",
      "conda-4.12.0         | 1.0 MB    | ##################################### | 100% \n",
      "threadpoolctl-3.1.0  | 18 KB     | ##################################### | 100% \n",
      "qudida-0.0.4         | 7 KB      | ##################################### | 100% \n",
      "pywavelets-1.3.0     | 4.4 MB    | ##################################### | 100% \n",
      "blosc-1.21.1         | 955 KB    | ##################################### | 100% \n",
      "gmp-6.2.1            | 806 KB    | ##################################### | 100% \n",
      "pixman-0.40.0        | 627 KB    | ##################################### | 100% \n",
      "xorg-xproto-7.0.31   | 73 KB     | ##################################### | 100% \n",
      "tifffile-2022.5.4    | 149 KB    | ##################################### | 100% \n",
      "xorg-xextproto-7.3.0 | 28 KB     | ##################################### | 100% \n",
      "alsa-lib-1.2.3.2     | 554 KB    | ##################################### | 100% \n",
      "libwebp-base-1.2.2   | 824 KB    | ##################################### | 100% \n",
      "cfitsio-4.0.0        | 1.3 MB    | ##################################### | 100% \n",
      "libvorbis-1.3.7      | 280 KB    | ##################################### | 100% \n",
      "font-ttf-inconsolata | 94 KB     | ##################################### | 100% \n",
      "scikit-image-0.19.2  | 12.1 MB   | ##################################### | 100% \n",
      "ffmpeg-4.3.2         | 9.9 MB    | ##################################### | 100% \n",
      "libbrotlicommon-1.0. | 65 KB     | ##################################### | 100% \n",
      "xorg-libsm-1.2.3     | 26 KB     | ##################################### | 100% \n",
      "ca-certificates-2022 | 149 KB    | ##################################### | 100% \n",
      "scikit-learn-1.1.1   | 8.3 MB    | ##################################### | 100% \n",
      "jbig-2.1             | 43 KB     | ##################################### | 100% \n",
      "xorg-libxdmcp-1.1.3  | 19 KB     | ##################################### | 100% \n",
      "brotli-1.0.9         | 18 KB     | ##################################### | 100% \n",
      "jxrlib-1.1           | 235 KB    | ##################################### | 100% \n",
      "xorg-kbproto-1.0.7   | 27 KB     | ##################################### | 100% \n",
      "brotli-bin-1.0.9     | 19 KB     | ##################################### | 100% \n",
      "libbrotlidec-1.0.9   | 33 KB     | ##################################### | 100% \n",
      "libdeflate-1.8       | 67 KB     | ##################################### | 100% \n",
      "qt-5.12.9            | 98.8 MB   | ##################################### | 100% \n",
      "libglu-9.0.0         | 413 KB    | ##################################### | 100% \n",
      "joblib-1.1.0         | 210 KB    | ##################################### | 100% \n",
      "hdf5-1.12.1          | 3.6 MB    | ##################################### | 100% \n",
      "cloudpickle-2.1.0    | 25 KB     | ##################################### | 100% \n",
      "jasper-2.0.33        | 1.1 MB    | ##################################### | 100% \n",
      "libllvm13-13.0.1     | 33.6 MB   | ##################################### | 100% \n",
      "toolz-0.12.0         | 48 KB     | ##################################### | 100% \n",
      "gettext-0.19.8.1     | 3.6 MB    | ##################################### | 100% \n",
      "certifi-2022.6.15    | 155 KB    | ##################################### | 100% \n",
      "xorg-libx11-1.7.2    | 941 KB    | ##################################### | 100% \n",
      "libuuid-2.32.1       | 28 KB     | ##################################### | 100% \n",
      "xorg-libxfixes-5.0.3 | 18 KB     | ##################################### | 100% \n",
      "nettle-3.6           | 6.5 MB    | ##################################### | 100% \n",
      "xorg-libxrender-0.9. | 32 KB     | ##################################### | 100% \n",
      "libtiff-4.3.0        | 614 KB    | ##################################### | 100% \n",
      "libbrotlienc-1.0.9   | 287 KB    | ##################################### | 100% \n",
      "dbus-1.13.6          | 604 KB    | ##################################### | 100% \n",
      "libogg-1.3.4         | 206 KB    | ##################################### | 100% \n",
      "liblapacke-3.9.0     | 12 KB     | ##################################### | 100% \n",
      "lcms2-2.12           | 443 KB    | ##################################### | 100% \n",
      "freetype-2.10.4      | 890 KB    | ##################################### | 100% \n",
      "libaec-1.0.6         | 45 KB     | ##################################### | 100% \n",
      "fribidi-1.0.10       | 112 KB    | ##################################### | 100% \n",
      "graphite2-1.3.13     | 102 KB    | ##################################### | 100% \n",
      "gstreamer-1.18.5     | 2.0 MB    | ##################################### | 100% \n",
      "fonts-conda-forge-1  | 4 KB      | ##################################### | 100% \n",
      "freeglut-3.2.2       | 162 KB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "conda install -c conda-forge albumentations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "397f788e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
      "Collecting rasterio\n",
      "  Downloading rasterio-1.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (20.7 MB)\n",
      "\u001b[K     |████████████████████████████████| 20.7 MB 3.0 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: attrs in /opt/conda/lib/python3.8/site-packages (from rasterio) (21.2.0)\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from rasterio) (1.21.4)\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.8/site-packages (from rasterio) (58.5.3)\n",
      "Collecting click-plugins\n",
      "  Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)\n",
      "Requirement already satisfied: certifi in /opt/conda/lib/python3.8/site-packages (from rasterio) (2022.6.15)\n",
      "Requirement already satisfied: click>=4.0 in /opt/conda/lib/python3.8/site-packages (from rasterio) (8.0.3)\n",
      "Collecting snuggs>=1.4.1\n",
      "  Downloading snuggs-1.4.7-py3-none-any.whl (5.4 kB)\n",
      "Collecting cligj>=0.5\n",
      "  Downloading cligj-0.7.2-py3-none-any.whl (7.1 kB)\n",
      "Collecting affine\n",
      "  Downloading affine-2.3.1-py2.py3-none-any.whl (16 kB)\n",
      "Requirement already satisfied: pyparsing>=2.1.6 in /opt/conda/lib/python3.8/site-packages (from snuggs>=1.4.1->rasterio) (3.0.5)\n",
      "Installing collected packages: snuggs, cligj, click-plugins, affine, rasterio\n",
      "Successfully installed affine-2.3.1 click-plugins-1.1.1 cligj-0.7.2 rasterio-1.3.0 snuggs-1.4.7\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
      "Collecting Shapely==1.8.1.post1\n",
      "  Downloading Shapely-1.8.1.post1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (2.1 MB)\n",
      "\u001b[K     |████████████████████████████████| 2.1 MB 3.0 MB/s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: Shapely\n",
      "Successfully installed Shapely-1.8.1.post1\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
      "Requirement already satisfied: scikit-image in /opt/conda/lib/python3.8/site-packages (0.19.2)\n",
      "Requirement already satisfied: networkx>=2.2 in /opt/conda/lib/python3.8/site-packages (from scikit-image) (2.8.5)\n",
      "Requirement already satisfied: numpy>=1.17.0 in /opt/conda/lib/python3.8/site-packages (from scikit-image) (1.21.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.8/site-packages (from scikit-image) (21.0)\n",
      "Requirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.8/site-packages (from scikit-image) (1.3.0)\n",
      "Requirement already satisfied: scipy>=1.4.1 in /opt/conda/lib/python3.8/site-packages (from scikit-image) (1.6.3)\n",
      "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /opt/conda/lib/python3.8/site-packages (from scikit-image) (9.0.1)\n",
      "Requirement already satisfied: tifffile>=2019.7.26 in /opt/conda/lib/python3.8/site-packages (from scikit-image) (2022.5.4)\n",
      "Requirement already satisfied: imageio>=2.4.1 in /opt/conda/lib/python3.8/site-packages (from scikit-image) (2.19.3)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging>=20.0->scikit-image) (3.0.5)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
      "Collecting efficientnet_pytorch\n",
      "  Downloading efficientnet_pytorch-0.7.1.tar.gz (21 kB)\n",
      "Requirement already satisfied: torch in /opt/conda/lib/python3.8/site-packages (from efficientnet_pytorch) (1.11.0a0+b6df043)\n",
      "Requirement already satisfied: typing_extensions in /opt/conda/lib/python3.8/site-packages (from torch->efficientnet_pytorch) (3.10.0.2)\n",
      "Building wheels for collected packages: efficientnet-pytorch\n",
      "  Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.7.1-py3-none-any.whl size=16446 sha256=b336610ee2e6d7c5471f6eecf3d634b4ad2fbcbf0766ef14cddcfeaa80d986e5\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-a6azk_7o/wheels/84/b9/90/25a0195cf95fb5533db96f1c77ea3f296b7cc86ae8ae48e3dc\n",
      "Successfully built efficientnet-pytorch\n",
      "Installing collected packages: efficientnet-pytorch\n",
      "Successfully installed efficientnet-pytorch-0.7.1\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
      "Collecting pytorch-metric-learning\n",
      "  Downloading pytorch_metric_learning-1.5.1-py3-none-any.whl (110 kB)\n",
      "\u001b[K     |████████████████████████████████| 110 kB 3.2 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from pytorch-metric-learning) (1.21.4)\n",
      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.8/site-packages (from pytorch-metric-learning) (1.1.1)\n",
      "Requirement already satisfied: torch>=1.6.0 in /opt/conda/lib/python3.8/site-packages (from pytorch-metric-learning) (1.11.0a0+b6df043)\n",
      "Requirement already satisfied: torchvision in /opt/conda/lib/python3.8/site-packages (from pytorch-metric-learning) (0.11.0a0)\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.8/site-packages (from pytorch-metric-learning) (4.62.3)\n",
      "Requirement already satisfied: typing_extensions in /opt/conda/lib/python3.8/site-packages (from torch>=1.6.0->pytorch-metric-learning) (3.10.0.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from scikit-learn->pytorch-metric-learning) (3.1.0)\n",
      "Requirement already satisfied: scipy>=1.3.2 in /opt/conda/lib/python3.8/site-packages (from scikit-learn->pytorch-metric-learning) (1.6.3)\n",
      "Requirement already satisfied: joblib>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from scikit-learn->pytorch-metric-learning) (1.1.0)\n",
      "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /opt/conda/lib/python3.8/site-packages (from torchvision->pytorch-metric-learning) (9.0.1)\n",
      "Installing collected packages: pytorch-metric-learning\n",
      "Successfully installed pytorch-metric-learning-1.5.1\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.8/site-packages (4.62.3)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
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      "Collecting ipywidgets\n",
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      "\u001b[K     |████████████████████████████████| 123 kB 3.1 MB/s eta 0:00:01\n",
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      "\u001b[K     |████████████████████████████████| 245 kB 22.3 MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting widgetsnbextension~=3.6.0\n",
      "  Downloading widgetsnbextension-3.6.1-py2.py3-none-any.whl (1.6 MB)\n",
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      "Installing collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets\n",
      "Successfully installed ipywidgets-7.7.1 jupyterlab-widgets-1.1.1 widgetsnbextension-3.6.1\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
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      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Download Libraries \n",
    "!pip install rasterio\n",
    "!pip install Shapely==1.8.1.post1\n",
    "!pip install scikit-image\n",
    "!pip install -U efficientnet_pytorch\n",
    "!pip install pytorch-metric-learning\n",
    "!pip install -q timm\n",
    "!pip install tqdm\n",
    "!pip install ipywidgets \n",
    "!pip install torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "53b75705",
   "metadata": {},
   "outputs": [],
   "source": [
    "from model.resnet import *\n",
    "from model.effnet import *\n",
    "from model.unet import *\n",
    "from utils.metrics import *\n",
    "from utils.losses import *\n",
    "\n",
    "import os\n",
    "import torch\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import numpy as np\n",
    "from skimage import io, transform\n",
    "from rasterio.features import rasterize\n",
    "from shapely.ops import cascaded_union, unary_union\n",
    "from shapely.geometry import Polygon\n",
    "from shapely.errors import ShapelyDeprecationWarning\n",
    "from torch.utils.data import Dataset\n",
    "import pickle\n",
    "import albumentations as A\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import transforms, utils, datasets, models\n",
    "from torch.autograd import Variable\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "from pytorch_metric_learning import losses\n",
    "import copy\n",
    "\n",
    "from collections import OrderedDict, defaultdict\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "import timm\n",
    "from torch.cuda import amp\n",
    "from tqdm import tqdm\n",
    "import torch.optim as optim\n",
    "from time import time\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=ShapelyDeprecationWarning) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c9b48ebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>merged_label</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>year</th>\n",
       "      <th>example_path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Timber plantation</td>\n",
       "      <td>Plantation</td>\n",
       "      <td>4.430849</td>\n",
       "      <td>96.101634</td>\n",
       "      <td>2014</td>\n",
       "      <td>examples/4.430849118860583_96.1016343478138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>1.332341</td>\n",
       "      <td>109.374229</td>\n",
       "      <td>2012</td>\n",
       "      <td>examples/1.3323406178609702_109.37422873130464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grassland shrubland</td>\n",
       "      <td>Grassland shrubland</td>\n",
       "      <td>-1.720266</td>\n",
       "      <td>115.006996</td>\n",
       "      <td>2016</td>\n",
       "      <td>examples/-1.7202663845775041_115.00699582064483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Small-scale agriculture</td>\n",
       "      <td>Smallholder agriculture</td>\n",
       "      <td>-2.248346</td>\n",
       "      <td>104.135786</td>\n",
       "      <td>2011</td>\n",
       "      <td>examples/-2.248346072674411_104.1357857482906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other large-scale plantations</td>\n",
       "      <td>Plantation</td>\n",
       "      <td>-2.100800</td>\n",
       "      <td>113.022376</td>\n",
       "      <td>2008</td>\n",
       "      <td>examples/-2.100800102991412_113.02237632340159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           label             merged_label  latitude  \\\n",
       "0              Timber plantation               Plantation  4.430849   \n",
       "1                          Other                    Other  1.332341   \n",
       "2            Grassland shrubland      Grassland shrubland -1.720266   \n",
       "3        Small-scale agriculture  Smallholder agriculture -2.248346   \n",
       "4  Other large-scale plantations               Plantation -2.100800   \n",
       "\n",
       "    longitude  year                                     example_path  \n",
       "0   96.101634  2014      examples/4.430849118860583_96.1016343478138  \n",
       "1  109.374229  2012   examples/1.3323406178609702_109.37422873130464  \n",
       "2  115.006996  2016  examples/-1.7202663845775041_115.00699582064483  \n",
       "3  104.135786  2011    examples/-2.248346072674411_104.1357857482906  \n",
       "4  113.022376  2008   examples/-2.100800102991412_113.02237632340159  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check file or directory is exist\n",
    "train_df = pd.read_csv('../../dataset/ForestNetDataset /train.csv')\n",
    "val_df = pd.read_csv('../../dataset/ForestNetDataset /val.csv')\n",
    "test_df = pd.read_csv('../../dataset/ForestNetDataset /test.csv')\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6eab5e0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 332, 332)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_array = np.load('../../dataset/ForestNetDataset /examples/4.430849118860583_96.1016343478138/auxiliary/gain.npy')\n",
    "print(data_array.shape)\n",
    "fig = plt.figure(figsize = (10, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.contour(data_array[0], cmap = \"viridis\", \n",
    "            )\n",
    "plt.title(\"Gain Contours at (4.430849118860583,96.1016343478138)\")\n",
    "cbar = plt.colorbar()\n",
    "plt.gca().set_aspect('equal', adjustable='box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "19a522a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i4nMVCmszyzWzNWa20syyvbYWZjbfzDZ5/8/z2s3MnjOzzWa22szSqnMFREREarvK7Flf7ZxLdc6le7czgQXOufbAAu82wHVAe+9vNDCpqooVERGpi87kMPggYIo3PAW4Mah9qgtYCjQ3s/gzWI6IiEidVtGwdsA8M1thZqO9tjjn3A5v+GsgzhtOAPKCps332sows9Fmlm1m2YWFhadRuoiISN0QUcF+P3LObTOzWGC+ma0PHumcc2bmKrNg59xkYDJAenp6paYVERGpSyq0Z+2c2+b93wnMAC4DCkoOb3v/d3rdtwFtgiZP9NpERETkNJwyrM2siZk1KxkGrgU+B2YDGV63DGCWNzwbuMM7K7wHsC/ocLmIiIhUUkUOg8cBM8yspP8bzrm/m9lyYJqZjQS+Am7x+s8FBgCbgSLgziqvWkREpA45ZVg757YAXUO07wb6hGh3wJgqqU5ERER0BTMRERG/U1iLiIj4nMJaRETE5xTWIiIiPqewFhER8TmFtYiIiM8prEVERHxOYS0iIuJzCmsRERGfU1iLiIj4nMJaRETE5xTWIiIiPqewFhER8TmFtYiIiM8prEVERHxOYS0iIuJzCmsRERGfU1iLiIj4nMJaRETE5xTWIiIiPqewFhER8TmFtYiIiM8prEVERHxOYS0iIuJzCmsRERGfU1iLiIj4nMJaRETE5xTWIiIiPqewFhER8TmFtYiIiM8prEVERHxOYS0iIuJzCmsRERGfU1iLiIj4nMJaRETE5xTWIiIiPlfhsDaz+mb2mZm9591uZ2afmtlmM/ubmTXw2ht6tzd745OqqXYREZE6oTJ71vcD64Ju/wF41jl3EbAHGOm1jwT2eO3Pev1ERETkNFUorM0sEbge+C/vtgHXAG97XaYAN3rDg7zbeOP7eP1FRETkNFR0z3oC8CjwvXc7GtjrnCv2bucDCd5wApAH4I3f5/UXERGR03DKsDazG4CdzrkVVblgMxttZtlmll1YWFiVsxYREalVKrJnfSUw0MxygbcIHP6eCDQ3swivTyKwzRveBrQB8MZHAbuPn6lzbrJzLt05l96yZcszWgkREZHa7JRh7Zz7uXMu0TmXBNwGLHTODQMWAUO9bhnALG94tncbb/xC55yr0qpFRETqkDP5nvVjwINmtpnAZ9Ivee0vAdFe+4NA5pmVKCIiUrdFnLrLD5xzi4HF3vAW4LIQfQ4BN1dBbSIiIoKuYCYiIuJ7CmsRERGfU1iLiIj4nMJaRETE5xTWIiIiPqewFhER8TmFtYiIiM8prEVERHxOYS0iIuJzCmsRERGfU1iLiIj4nMJaRETE5xTWIiIiPqewFhER8TmFtYiIiM8prEVERHxOYS0iIuJzCmsRERGfU1iLiIj4nMJaRETE5xTWIiIiPqewFhER8TmFtYiIiM8prEVERHxOYS0iIuJzCmsRERGfU1iLiIj4nMJaRETE5xTWIiIiPqewFhER8TmFtYiIiM8prEVERHxOYS0iIuJzCmsRERGfU1iLiIj4nMJaRETE5xTWIiIiPnfKsDazRma2zMxWmdkXZvZrr72dmX1qZpvN7G9m1sBrb+jd3uyNT6rmdRAREanVKrJnfRi4xjnXFUgF+ptZD+APwLPOuYuAPcBIr/9IYI/X/qzXT0RERE7TKcPaBRzwbkZ6fw64Bnjba58C3OgND/Ju443vY2ZWVQWLiIjUNRX6zNrM6pvZSmAnMB/4EtjrnCv2uuQDCd5wApAH4I3fB0SHmOdoM8s2s+zCwsIzWgkREZHarEJh7Zw75pxLBRKBy4AOZ7pg59xk51y6cy69ZcuWZzo7ERGRWqtSZ4M75/YCi4CeQHMzi/BGJQLbvOFtQBsAb3wUsLsqihUREamLKnI2eEsza+4NnwP0BdYRCO2hXrcMYJY3PNu7jTd+oXPOVWHNIiIidUrEqbsQD0wxs/oEwn2ac+49M1sLvGVmvwU+A17y+r8EvGpmm4FvgNuqoW4REZE645Rh7ZxbDXQL0b6FwOfXx7cfAm6ukupEREREVzATERHxO4W1iEiQvLw8rr76ajp27EhKSgoTJ04sHfenP/2JDh06kJKSwqOPPgrAkSNHuPPOO+ncuTNdu3Zl8eLFYapcarOKfGYtIlJnRERE8Mwzz5CWlsb+/fvp3r07ffv2paCggFmzZrFq1SoaNmzIzp07AfjrX/8KwJo1a9i5cyfXXXcdy5cvp1497QtJ1dGzSUQkSHx8PGlpaQA0a9aM5ORktm3bxqRJk8jMzKRhw4YAxMbGArB27Vquueaa0rbmzZuTnZ0dnuKl1lJYi4iUIzc3l88++4zLL7+cjRs38vHHH3P55Zdz1VVXsXz5cgC6du3K7NmzKS4uZuvWraxYsYK8vLwwVy61jQ6Di4iEcODAAW666SYmTJjAueeeS3FxMd988w1Lly5l+fLl3HLLLWzZsoW77rqLdevWkZ6ezvnnn88VV1xB/fr1w12+1DIKaxGR4xw9epSbbrqJYcOGMWTIEAASExMZMmQIZsZll11GvXr12LVrFy1btuTZZ58tnfaKK67g4osvDlfpUkvpMLiISBDnHCNHjiQ5OZkHH3ywtP3GG29k0aJFAGzcuJEjR44QExNDUVERBw8eBGD+/PlERETQsWPHsNQutZf2rEVEPJ2ndObgxoNsfXUrDRMb8pd3/wJA3NA4mqQ0YdsH2xj/8ngswmj1L63oMrULRwqPkPtMLmZGxHkRJNyVQOcpncvMd03GmnCsjtQiCmsRkSBNLm5Cp1c6hRzX5p42J7Q1aNmAi7N02Fuqlw6Di4iI+JzCWkRExOcU1iIiIj6nsBYREfE5hbWIiIjPKaxFRER8TmEtIiLicwprERERn1NYi2/l5eVx9dVX07FjR1JSUpg4cSIATzzxBF26dCE1NZVrr72W7du3A7B+/Xp69uxJw4YN+c///M9wli4iUqUU1uJbERERPPPMM6xdu5alS5fywgsvsHbtWh555BFWr17NypUrueGGG3jyyScBaNGiBc899xwPP/xwmCsXEalaCmvxrfj4eNLS0gBo1qwZycnJbNu2jXPPPbe0z8GDBzEzAGJjY7n00kuJjIwMS70iItVF1waXs0Jubi6fffYZl19+OQCPP/44U6dOJSoqqvSXkEREaivtWYvvHThwgJtuuokJEyaU7lX/7ne/Iy8vj2HDhvH888+HuUIRkeqlsBZfO3r0KDfddBPDhg1jyJAhJ4wfNmwY77zzThgqExGpOQpr8S3nHCNHjiQ5OZkHH3ywtH3Tpk2lw7NmzaJDhw7hKE/CqLxvCkyfPp2UlBTq1atHdnZ2af+jR4+SkZFB586dSU5O5qmnngpX6SKnRZ9Ziy8lZc7hUP4XFLz+KpEtk3jxzdkAnNfrDg6sns/Rb/LB6hFxbkta9BtDUuYcjh3Yw44pD/D9kSKwemT+OovWoyZRr2HjMvPOzbo+HKskVajkmwJpaWns37+f7t2707dvXzp16sS7777LPffcU6b/9OnTOXz4MGvWrKGoqIiOHTty++23k5SUFJ4VkGqTl5fHHXfcQUFBAWbG6NGjuf/++5k+fTrjxo1j3bp1LFu2jPT09NJpVq9ezT333MO3335LvXr1WL58OY0aNQrjWpxIYS2+1SgxhfMfe++E9nMuvDRk//pNzyNxzJTqLkt8ID4+nvj4eKDsNwX69u0bsr+ZcfDgQYqLi/nuu+9o0KBBmW8VSO1R2TdyxcXFDB8+nFdffZWuXbuye/duX36jRIfBReSsdvw3BUIZOnQoTZo0IT4+nrZt2/Lwww/TokWLGqxSakp5X/lMTk7mkksuOaH/vHnz6NKlC127dgUgOjqa+vXr12jNFaGwFpGzVqhvCoSybNky6tevz/bt29m6dSvPPPMMW7ZsqcFKJRwq8kZu48aNmBn9+vUjLS2N8ePH12CFFafD4CJyVjrVNwWCvfHGG/Tv35/IyEhiY2O58soryc7O5oILLqihaqWmVfSNXHFxMUuWLGH58uU0btyYPn360L17d/r06VOD1Z6a9qxF5KxT3jcFytO2bVsWLlwIBK56t3TpUn2LoBarzBu5xMREevXqRUxMDI0bN2bAgAHk5OTUUKUVpz1rETm7jIvin/9XzKuvFtE5th6pb/0JgN/3acjhYviP9w9RWOS4vvdlpLaqxz+GN2HMEcedy74jJXYqzsGdqZF0effH8O5x827XtubXR6pUZd/I9evXj/Hjx1NUVESDBg348MMPGTt2bA1UWjkKaxE56/yobQTuV6EPbQ5OPvFM3qYNjOk3Nw7RW2qbC39xIVtf3UrDxIb85d2/ABA3NA5X7Nj+2naO7T9Gjz49OKftOSQ9nATA3kv30qJ9CzBo1qUZmbsyyZySWWa+azLW1PSqlKGwFhGRWqPJxU3o9EqnkOPO7R76DV7zK5rT/Irm1VjVmdNn1iIiIj6nsBYREfG5U4a1mbUxs0VmttbMvjCz+732FmY238w2ef/P89rNzJ4zs81mttrM0qp7JURERGqziuxZFwMPOec6Aj2AMWbWEcgEFjjn2gMLvNsA1wHtvb/RwKQqr1pERKQOOWVYO+d2OOdyvOH9wDogARgElFyIeQpwozc8CJjqApYCzc0svqoLFxERqSsq9Zm1mSUB3YBPgTjn3A5v1NdAnDecAOQFTZbvtR0/r9Fmlm1m2YWFhZWtW0REpM6ocFibWVPgHeAB59y3weOccw5wlVmwc26ycy7dOZfesmXLykwqIiJSp1QorM0skkBQv+6cK7nmT0HJ4W3v/06vfRvQJmjyRK9NRERETkNFzgY34CVgnXPuj0GjZgMZ3nAGMCuo/Q7vrPAewL6gw+UiIiJSSRW5gtmVwL8Ca8xspdf2/4AsYJqZjQS+Am7xxs0FBgCbgSLgzqosWEREpK45ZVg755YAVs7oE35DzPv8eswZ1iUiIiIeXcFMRETE5xTWIiIiPqewFhER8TmFtYiIiM8prEVERHxOYS0iIuJzCmsRERGfU1iLiIj4nMJaRETE5xTWIiIiPqewFhER8TmFtYiIiM8prEVERHyu1ob1XXfdRWxsLJ06dTph3DPPPIOZsWvXLgBmzZpFly5dSE1NJT09nSVLltR0uSIiIuWqtWE9YsQI/v73v5/QnpeXx7x582jbtm1pW58+fVi1ahUrV67k5ZdfZtSoUTVZqohInVOZHSrnHD/72c+46KKL6NKlCzk5OTVdbtjV2rDu1asXLVq0OKF97NixjB8/HrMffqK7adOmpbcPHjxYZpyIiFS9yuxQvf/++2zatIlNmzYxefJk7r333pos1RdqbViHMmvWLBISEujatesJ42bMmEGHDh24/vrrefnll8NQnYhI3VGZHapZs2Zxxx13YGb06NGDvXv3smPHjposN+zqTFgXFRXx+9//nieffDLk+MGDB7N+/XpmzpzJE088UcPViYhIeTtU27Zto02bNqW3ExMT2bZtW02XF1YR4S6gpnz55Zds3bq19EmQn59PWloay5Yto1WrVqX9evXqxZYtW9i1axcxMTHhKldEpE4p2aGaN29euEvxpToT1p07d2bnzp2lt5OSksjOziYmJobNmzdz4YUXYmbk5ORw+PBhoqOjw1itiEjdcrIdqoSEBPLy8kr75ufnk5CQEK5Sw6JWhnVS5hwKZ4/n8P+t4dh33xLRLIaoHw2jWddrS/vk7/mObk/Oo37jKPYtfZuDny+E+vWxiAacd/X9tPv53JDzzs26vqZWQ0SkzjjZDtXAgQN5/vnnue222/j000+JiooiPj4+jNXWvFoZ1gAtBz560vGJ9/5wEllUj6FE9Rha3SWJiAjAuChuf6eIxbnH2FXkSDy3Hr/u3ZCRaQ1+6LN3P4xvB43rMcA55u46xEXR9Wkcafz3oHNgXFToebdrG7r9LFdrw1pERPzrzZsan3R87gPNSofNjBeuP6e6S/K1OnM2uIiIyNlKYS0iIuJzCmsRERGfU1iLiIj4nMJaRETE5xTWIiIiPqewFqlCoX7275tvvqFv3760b9+evn37smfPntJxixcvJjU1lZSUFK666qpwlCwiZwGFtUgVCvWzf1lZWfTp04dNmzbRp08fsrKyANi7dy/33Xcfs2fP5osvvmD69OnhKFlEzgIKa5EqFOpn/2bNmkVGRgYAGRkZzJw5E4A33niDIUOGlP5ub2xsbI3WKiJnD4W1SDUrKCgovY5xq1atKCgoAGDjxo3s2bOH3r170717d6ZOnRrOMkXEx3S5UZEaZGaYGQDFxcWsWLGCBQsW8N1339GzZ0969OjBxRdfHOYqRcRvtGctUs3i4uLYsWMHADt27Cg93J2YmEi/fv1o0qQJMTEx9OrVi1WrVoWzVBHxKYW1SDUbOHAgU6ZMAWDKlCkMGjQIgEGDBrFkyRKKi4spKiri008/JTk5OZyliohPnTKszexlM9tpZp8HtbUws/lmtsn7f57Xbmb2nJltNrPVZpZWncWL+M3tt99Oz5492bBhA4mJibz00ktkZmYyf/582rdvzwcffEBmZiYAycnJ9O/fny5dunDZZZcxatSoMl/5EhEpUZHPrF8BngeCz37JBBY457LMLNO7/RhwHdDe+7scmOT9F6n1Ok/pDP0hpn8MMcQAMIEJTHhvAtwBjWhEAQVc9T9B36eOhfqP1QfgJV7ipSkvhZz3mow11V2+iPjYKfesnXMfAd8c1zwImOINTwFuDGqf6gKWAs3NLL6KahUREamTTvcz6zjn3A5v+GsgzhtOAPKC+uV7bSIiInKazvgEM+ecA1xlpzOz0WaWbWbZhYWFZ1qGiIhIrXW6YV1Qcnjb+7/Ta98GtAnql+i1ncA5N9k5l+6cS2/ZsuVpliEiIlL7ne5FUWYDGUCW939WUPu/m9lbBE4s2xd0uFxOYcOGDdx6662lt7ds2cKTTz7JJ598woYNG4DA9aSbN2/OypUrw1SliIjUtFOGtZm9CfQGYswsH/gVgZCeZmYjga+AW7zuc4EBwGagCLizGmqutS655JLSED527BgJCQkMHjyYBx54oLTPQw89RFRUVHgKFBGRsDhlWDvnbi9nVJ8QfR0w5kyLEliwYAEXXngh559/fmmbc45p06axcOHCMFYmIiI1TVcw86m33nqL228v+z7p448/Ji4ujvbt24epKhERCQeFtQ8dOXKE2bNnc/PNN5dpf/PNN08IcKm8DRs2kJqaWvp37rnnMmHCBL755hv69u1L+/bt6du3L3v27Al3qSIigMLal95//33S0tKIi4srbSsuLubdd98tcwKanJ6ScwNWrlzJihUraNy4MYMHDyYrK4s+ffqwadMm+vTpQ1ZWVrhLFREBFNa+FGoP+oMPPqBDhw4kJiaGqaraKfjcgFmzZpGRkQFARkYGM2fODG9xIiIehbXPHDx4kPnz5zNkyJAy7aE+w5YzF3y/FhQUEB8fuDpuq1atKCgoCGdpIiKlTvd71lLFOk/pXDrc+o+t+dHMH5XtcDWsYAUvTHmh0vPWj0CEVnJuwFNPPXXCODPDzMJQlYjIibRnLXXW8ecGxMXFsWNH4Bo+O3bsIDY2NpzliYiUUlhLnXX8uQEDBw5kypTAj8lNmTKFQYMGhas0EZEydBhc6p5xURw84pg/+wB/SZoL4x4AINO+55bJ3/HS049zfpQx7ebGMO4vFZ9vu7bVU6+I1HkKa6mTmjQwdj/arExbdON6LLijSZgqEhEpnw6Di4iI+JzCWkRExOcU1qfp2LFjdOvWjRtuuAGAESNG0K5du9JLWOonLEVEpKroM+vTNHHiRJKTk/n2229L255++mmGDh0axqpERKQ20p71acjPz2fOnDmMGjUq3KWIiEgdoLA+DQ888ADjx4+nXr2yd9/jjz9Oly5dGDt2LIcPHw5TdSIiUtsorCvpvffeIzY2lu7du5dpf+qpp1i/fj3Lly/nm2++4Q9/+EOYKhQRkdpGYV1J//znP5k9ezZJSUncdtttLFy4kOHDhxMfH4+Z0bBhQ+68806WLVsW7lJFRKSWUFhX0lNPPUV+fj65ubm89dZbXHPNNbz22mul15R2zjFz5kw6deoU5kpFRKS20NnglTUu6ofh3GLYeATGRTFsykEKixzOQWqr+vz5hkYwblLF56tLVYqISDkU1megd1IEvZMCd+HCDF2mUkREqocOg4uIiPicwlpERMTnFNYiIiI+p7AWERHxOYW1iIiIzymsRUREfE5hLSIi4nMKaxEREZ9TWIuIiPicwlpERMTnFNYiIiI+p7AWERHxOYW1iIiIzymsRUREfE5hLSIi4nPVEtZm1t/MNpjZZjPLrI5liIiI1BVVHtZmVh94AbgO6AjcbmYdq3o5IiIidUV17FlfBmx2zm1xzh0B3gIGVcNyRERE6oTqCOsEIC/odr7XJiIiIqchIlwLNrPRwGjv5gEz2xCuWirDKtc9BthVsa6fV7qWirIRlay6ltNjeHY7jXsi7I+hHr+yqu81CGfRY3h+ZTpXR1hvA9oE3U702spwzk0GJlfD8n3DzLKdc+nhrkNOnx7Ds58ew7ObHr+A6jgMvhxob2btzKwBcBswuxqWIyIiUidU+Z61c67YzP4d+AdQH3jZOfdFVS9HRESkrqiWz6ydc3OBudUx77NMrT7MX0foMTz76TE8u+nxA8w5F+4aRERE5CR0uVERERGf811Ym1mcmb1hZlvMbIWZfWJmg6txeePM7OEqmtcrZja0ppZb3vLMrLeZvXcm8w6a1wgze74q5hVuZpZoZrPMbJOZfWlmE82sgZmlmtmAoH5V9pyQkzOzY2a20sw+N7PpZtbYaz9wBvMcYWatK9vPzP7rbLvaopk9bmZfmNlq7368vIrme8D7n2Rmn3vDld4WmFmumcWEaA/7a8zMnjSzn5zmtLlmFmNmzc3svqquLRRfhbWZGTAT+Mg5d4FzrjuBs8kTQ/QN23fEa4oF+OoxOlt5z613gZnOufbAxUBT4HdAKjCg/Kkrvaz6VTWvOuA751yqc64TcAT4tyqY5wjglGF9fD/n3Cjn3NoqWH6NMLOewA1AmnOuC/ATyl6Qqtao6u29mdV3zv3SOffBGc6qOVDpsD6dbYTfguAa4Ihz7s8lDc65r5xzf4LSd3azzWwhsMDMmprZAjPLMbM1ZjbI69fEzOaY2SrvHfutXnuWma313oX+5/ELN7O7zWy5N907Qe/yXzGz58zsf709/qFeu5nZ896PlnwAxIZaKTP7WdBy3woa1dHMFnvz/JnXN8mb31QC3+5vE7yXYWZDzeyVoHn8xMyyzWyjmd0QYtmXeUcnPvPqvyTovnzXzP7u7WmOD5rmTm9+y4ArT/J4nU2uAQ455/4bwDl3DBgLjALGA7d6eya3ev1PeGwAzGy4mS3z+v6l5EVnZgfM7BkzWwX0rNE1qz0+Bi4KbjjJazzJzNaZ2V+9Pct5ZnaO99pMB173HqNzzOyX3uv6czOb7L1uQ/VbbGbp3vxv95b3uZn9IaieA2b2O28bsdTM4mrs3jlRPLDLOXcYwDm3yzm33asz18ye8tYt28zSzOwfFjii9G9en5D37Sm0LmebEfL+CmaBowAbzWwJcElQ+4XePFeY2cdm1sFrf8XM/mxmnxJ4jQbPK8nrm+P9XeG11zOzF81svZnNN7O59sP2OtfM/mBmOcDNFnRk0swu9baPq7zXdzM77kiCmb1nZr2PW60s4ELvfn7ajjuqaYF8GFHO8q+1wLY5xwJHlZqe9J53zvnmD/gZ8OxJxo8gcPnSFt7tCOBcbzgG2Ezg4jg3AX8Nmi4KiAY28MNJdc29/+OAh73h6KBpfgv8hzf8CjCdwJubjgSufQ4wBJhP4CtqrYG9wNAQdW8HGoZY7v8CDb3adwORQBLwPdAjaPoDQcNDgVeC6vq7V1d7775pBPQG3vP6nAtEeMM/Ad4Jui+3ePdNI+ArAheziQf+D2gJNAD+CTwf7udGdT23gM+8cc8HtZX32CQD/wNEev1eBO7whh1wS7jX82z7K3lue6/lWcC9IdpDvcaTgGIg1Rs3DRjuDS8G0oOW0SJo+FXgp+X0W0wgwFsHvQYigIXAjUGPc8n044FfhPG+awqsBDZ6z8WrgsblBt2XzwKrgWbeOhWc7L497v5PAj73hsvbZpzs/sr15t0dWAM0JrBN2swP290FQHtv+HJgoTf8CvAeUD/EujcGGnnD7YFsb3gogW8i1QNaAXvwtsleLY8GzeMVr38Db70u9drP9dZjBGW3C+8BvY9br9L7x2vvjbft9W4/D4w4fvnetB8BTbzbjwG/PNnj7etDyWb2AvAjAnvbl3rN851z35R0AX5vZr0IBFwCEEfgSfGM9w7vPefcxxY4jHIIeMl75xPqM91OZvZbAoc2mhL4rniJmc6574G1Qe+mewFvusBe2nYL7PGHsprAO/iZBA7zl5jjAu+KD5vZTq92gK+cc0tPeuf8YJpX1yYz2wJ0OG58FDDFzNoT2NBEBo1b4JzbB2Bmawlc/i4GWOycK/Ta/0bgkHFdE+qx6UNgo7PczADOAXZ6/Y8B74Sj0LPcOWa20hv+GHjpuPHlvcYBtjrnSqZdQWDDGcrVZvYogQ18C+ALAm+6ynMpZV8DrxN4rc8kcKi+ZNuxAuh70rWrRs65A2bWHfgxcDXwNzPLdM694nUpuRjVGqCpc24/sN/MDptZc+Agoe/br0+y2FDbjGjKv79K/BiY4Zwr8vrM9v43Ba4ApnuvKQi8SS4x3du+Hi8SeN7MUgm89kq2UT/ypvke+NrMFh033d9CzOsSYIdzbjmAc+5br7by74XTV7L8HgR2/P7pLacB8MnJJvRbWH9BYK8YAOfcGAucnJAd1Odg0PAwAu/mujvnjppZLoF3WxvNLI3A55C/NbMFzrknzewyAhvcocC/Ezg0GuwVAu8IV3mHLnoHjTscNFzZR/F6Ak/enwKPm1nnEPM8xg+PR/A6QiBkSzQ6ybhQt38DLHLODTazJAJ7DyXKW35ttJbA417KzM4F2hLYQzteqPvGgCnOuZ+H6H+onI2KnNx3zrnUk4wP+Rr3xh3/GJ1z/MRm1ojAXme6cy7PzMZx4muoMo46b1cIH7xmvOfcYmCxma0BMghsx+CH++d7yt5X3xOo+2T3bXmqeptRD9h7kufA8dvCEmOBAqCrN49DFVxeefMLpZiyHxVX5HlzqmlKlm8Edjxvr2gxfvvMeiHQyMzuDWprfJL+UcBO74l2Nd6F0S1whmeRc+414GkgzXsHF+UCF2wZS+BBPl4zYIeZRRJ4Ip/KRwQ+66xvZvEE3t2WYYETxNo45xYRONQRRWCvvTIKzCzZm9fxZ8bf7H1OcyFwAYFD/cGi+OHa7CMqsKxPgavMLNq7H26uZK1+tQBobGZ3QOkJHs8Q2LAVEHjsKzKPoWYW682jhZlV6mL8UmkhX+OnsJ8fHs+SjeUubxswtJx+wZYReA3EeM+T24EPT6v6amRml3hHzEqkEjg0XVGnc9+GUpH76yPgRgucG9CMwI5LyV7sVjO7GUrPAwq1bQ5V+w5vD/pfCXwUCYGP7W7ytolxlN3hKs8GIN7MLvVqaOYdic0FUr15tSHw88/HO/459BWB810aekcv+pSzzKXAlWZ2kbfMJmZ20iOYvtqTcs45M7sReNY7bFVI4J3IY+VM8jrwP947ymxgvdfeGXjazL4HjgL3ErhDZ3nvtA14MMT8niAQVoXe/1NtwGcQ2DtfS+Azm1CHMeoDr5lZlLfc55xzeyt5iCWTwKG3QgLrGRz2/0fgxXIu8G/OuUPHzXs8gcPgvwDmnGpBzrkd3t7HJwQ+g19ZmUL9yntuDQZeNLMnCLxRnQv8P6AJkOkdjn3qJPNY692P87w3TkeBMVRuAymVU95r/GReAf5sZt8RONnvrwRO1vyawG8XlNcPKH0NZAKLCLxm5zjnZp35qlS5psCfvFAoJvA58OiTTlHW6dy3J6jI/eWcy/E+UltF4KOj4MdhGDDJe21FAm95/U7mReAd78333/lhj/UdAgG5lsCZ8TnAvlPUf8QCJ5b+yczOAb4jcH7PP4Gt3rzWefM6ftrdZvZPC3y97X3n3CNmNo3A820rgXNiQi2z0Dt6+6aZlRz2/wWB8w9C0hXMRESk1jCzpt7n+dEEdmSudM6d7HP4s4Kv9qxFRETO0Hve0YYGwG9qQ1CD9qxFRER8z28nmImIiMhxFNYiIiI+p7AWERHxOYW1iIiIzymsRUREfE5hLSIi4nP/H6rQr3HovzKzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution of data\n",
    "train_df_class = train_df.groupby(['merged_label']).size().to_frame('train').reset_index()\n",
    "val_df_class = val_df.groupby(['merged_label']).size().to_frame('val').reset_index()\n",
    "test_df_class = test_df.groupby(['merged_label']).size().to_frame('test').reset_index()\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "x_axis = np.arange(len(train_df_class['merged_label']))\n",
    "index = [[train_df_class,'train',-0.2],[val_df_class,'val',0],[test_df_class,'test',0.2]]\n",
    "for i in index:\n",
    "  df = i[0]\n",
    "  bars = plt.bar(x_axis + i[2], df[i[1]] , width=0.2, label=i[1])\n",
    "  for bar in bars:\n",
    "      yval = bar.get_height()\n",
    "      plt.text(bar.get_x(), yval + .005, yval)\n",
    "plt.xticks(x_axis,train_df_class['merged_label'])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6231163b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ForestDataset(Dataset):\n",
    "    \"\"\"Forest dataset.\"\"\"\n",
    "\n",
    "    def __init__(self, csv_file_to_df, root_dir='../../dataset/ForestNetDataset ', transform=None, types=\"classifier\"):\n",
    "        self.csv = csv_file_to_df\n",
    "        self.root_dir = root_dir\n",
    "        self.transform = transform\n",
    "        self.label_to_int = {'Grassland shrubland':0, 'Other':1, 'Plantation':2, 'Smallholder agriculture':3}\n",
    "        self.types = types\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.csv)\n",
    "    \n",
    "    def poly_from_utm(self, polygon):\n",
    "        poly_pts = []\n",
    "\n",
    "        poly = unary_union(polygon)\n",
    "        for i in np.array(poly.exterior.coords):\n",
    "\n",
    "            poly_pts.append(tuple(i))\n",
    "\n",
    "        new_poly = Polygon(poly_pts)\n",
    "        return new_poly\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        if torch.is_tensor(idx):\n",
    "            idx = idx.tolist()\n",
    "\n",
    "        label = self.csv.iloc[idx, 0]\n",
    "        merged_label = self.csv.iloc[idx, 1]\n",
    "        lat = self.csv.iloc[idx, 2]\n",
    "        long = self.csv.iloc[idx, 3]\n",
    "        year = self.csv.iloc[idx, 4]\n",
    "        folder = self.csv.iloc[idx, 5]\n",
    "        \n",
    "        ## Load the image and auxiliary\n",
    "        image = io.imread(f'{self.root_dir}/{folder}/images/visible/composite.png')\n",
    "        slope = np.load(f'{self.root_dir}/{folder}/auxiliary/slope.npy')\n",
    "          \n",
    "        ## Get the segmentation map\n",
    "        with open(f'{self.root_dir}/{folder}/forest_loss_region.pkl', 'rb') as f:\n",
    "            data = pickle.load(f)\n",
    "      \n",
    "        nx, ny = 332, 332\n",
    "        xy_array = np.empty((0,2))\n",
    "        if data.geom_type == 'Polygon':\n",
    "            data = [data]\n",
    "        elif data.geom_type == 'Multipolygon':\n",
    "            data = list(data)\n",
    "              \n",
    "        poly_shp = []\n",
    "        for poly_verts in data:\n",
    "            poly_shp.append(self.poly_from_utm(poly_verts))\n",
    "\n",
    "        mask = rasterize(shapes=poly_shp, out_shape=(332,332))\n",
    "        seg = np.array(mask)\n",
    "        \n",
    "        if self.transform:\n",
    "            augmented = self.transform(image=image)\n",
    "            image = augmented[\"image\"]\n",
    "          \n",
    "        image = torch.from_numpy(image).type(torch.float)\n",
    "        image = image.permute(2, 0, 1)\n",
    "        seg = torch.from_numpy(seg).type(torch.uint8)\n",
    "        slope = torch.from_numpy(slope).type(torch.float)\n",
    "\n",
    "        merged_label = self.label_to_int[merged_label]\n",
    "\n",
    "        image = image[:, 86:246, 86:246]\n",
    "        seg = seg[86:246, 86:246]\n",
    "        slope = slope[86:246, 86:246]\n",
    "        if self.types == \"classifier\":\n",
    "            return image, slope, merged_label\n",
    "        else:\n",
    "            return image, merged_label\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f19b2a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = A.Compose(\n",
    "    [\n",
    "        A.HorizontalFlip(p=0.5),\n",
    "        A.Rotate(270),\n",
    "        A.ElasticTransform(\n",
    "            p=0.4, alpha=120, sigma=120 * 0.05, alpha_affine=120 * 0.03\n",
    "        ),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "124267ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialization several arguments for training\n",
    "BATCH_SIZE = 16\n",
    "DEVICE = device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
    "LR = 0.001\n",
    "EPOCH = 20\n",
    "nbClass = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e0f7737",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Dataset and Create DataLoader For Classification\n",
    "train_dataset = ForestDataset(csv_file_to_df=train_df, transform=transform)\n",
    "val_dataset = ForestDataset(csv_file_to_df=val_df)\n",
    "test_dataset = ForestDataset(csv_file_to_df=test_df)  \n",
    "trainloader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE,shuffle=True)\n",
    "valloader = torch.utils.data.DataLoader(val_dataset, batch_size=BATCH_SIZE,shuffle=True)\n",
    "testloader = torch.utils.data.DataLoader(test_dataset, batch_size=BATCH_SIZE,shuffle=False)\n",
    "dataloaders = {'train': trainloader, 'val':valloader}\n",
    "\n",
    "# Load Dataset and Create DataLoader For Representation Learning\n",
    "train_dataset_re = ForestDataset( csv_file_to_df=train_df, transform=transform, types=\"projection\")\n",
    "val_dataset_re = ForestDataset( csv_file_to_df=val_df, types=\"projection\")\n",
    "test_dataset_re = ForestDataset( csv_file_to_df=test_df, types=\"projection\")\n",
    "trainloader_re = torch.utils.data.DataLoader(train_dataset_re, batch_size=BATCH_SIZE,shuffle=True)\n",
    "valloader_re = torch.utils.data.DataLoader(val_dataset_re, batch_size=BATCH_SIZE,shuffle=False)\n",
    "testloader_re = torch.utils.data.DataLoader(test_dataset_re, batch_size=BATCH_SIZE,shuffle=False)\n",
    "dataloaders_re = {'train': trainloader_re, 'val':valloader_re}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e66778c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "images shape on PyTroch :  torch.Size([16, 3, 160, 160])\n",
      "slope shape on PyTroch :  torch.Size([16, 160, 160])\n",
      "labels shape on PyTroch :  torch.Size([16])\n"
     ]
    }
   ],
   "source": [
    "dataiter = iter(trainloader)\n",
    "images, slope, labels = dataiter.next()\n",
    "\n",
    "print('images shape on PyTroch : ', images.size())\n",
    "print('slope shape on PyTroch : ', slope.size())\n",
    "print('labels shape on PyTroch : ', labels.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5e5ec26e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_curves(acc_dict, loss_dict):\n",
    "    fig = plt.figure(figsize=(22,8))\n",
    "    train_score,eval_score = acc_dict.values()\n",
    "    train_score=list(map(float,train_score))\n",
    "    eval_score=list(map(float,eval_score))\n",
    "    ax = plt.figure().gca()\n",
    "    plt.plot(train_score)\n",
    "    plt.plot(eval_score)\n",
    "\n",
    "    ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "\n",
    "    plt.legend(['train', 'val'])\n",
    "    plt.xlabel('number of epochs')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.title('Learning curves')\n",
    "    plt.show()\n",
    "    \n",
    "    train_loss, eval_loss = loss_dict.values()\n",
    "    train_loss=list(map(float,train_loss))\n",
    "    eval_loss=list(map(float,eval_loss))\n",
    "    ax = plt.figure().gca()\n",
    "    plt.plot(train_loss)\n",
    "    plt.plot(eval_loss)\n",
    "\n",
    "    ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "\n",
    "    plt.legend(['train', 'val'])\n",
    "    plt.xlabel('number of epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.title('Loss')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5c5dcb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_sizes = {'train':len(train_df), 'val':len(val_df)}\n",
    "acc_dict = {'train':[], 'val':[]}\n",
    "loss_dict= {'train':[], 'val':[]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1ceb485",
   "metadata": {},
   "source": [
    "## Multimodal SuperCon using Custom Resnet as Backbone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ffefcece",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model_SCL(model, criterion, optimizer, scheduler, num_epochs, dataloaders):\n",
    "    start = time()\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_loss = np.inf\n",
    "    history = defaultdict(list)\n",
    "    scaler = amp.GradScaler()\n",
    "\n",
    "    for step, epoch in enumerate(range(1,num_epochs+1)):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs))\n",
    "        print('-' * 10)\n",
    "        for phase in ['train','val']:\n",
    "            if(phase == 'train'):\n",
    "                model.train()\n",
    "            else:\n",
    "                model.eval()\n",
    "            \n",
    "            running_loss = 0.0\n",
    "            \n",
    "            for inputs,labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    with amp.autocast(enabled=True):\n",
    "                        outputs = model(inputs)\n",
    "                        loss = criterion(outputs, labels)\n",
    "                        loss = loss / 4\n",
    "                    \n",
    "                    if phase == 'train':\n",
    "                        scaler.scale(loss).backward()\n",
    "\n",
    "                    if phase == 'train' and (step + 1) % 4 == 0:\n",
    "                        scaler.step(optimizer)\n",
    "                        scaler.update()\n",
    "                        optimizer.zero_grad()\n",
    "                        scheduler.step()\n",
    "\n",
    "                running_loss += loss.item()*inputs.size(0)\n",
    "            \n",
    "            epoch_loss = running_loss/dataset_sizes[phase]            \n",
    "            history[phase + ' loss'].append(epoch_loss)\n",
    "\n",
    "            print('{} Loss: {:.4f}'.format(\n",
    "                phase, epoch_loss))\n",
    "            \n",
    "            # deep copy the model\n",
    "            if phase=='val' and epoch_loss <= best_loss:\n",
    "                best_loss = epoch_loss\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "    end = time()\n",
    "    time_elapsed = end - start\n",
    "    print('Training complete in {:.0f}h {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 3600, (time_elapsed % 3600) // 60, (time_elapsed % 3600) % 60))\n",
    "    print(\"Best Loss \",best_loss)\n",
    "\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model, history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "57defbe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "----------\n",
      "train Loss: 0.8429\n",
      "val Loss: 0.6841\n",
      "Epoch 2/20\n",
      "----------\n",
      "train Loss: 0.8343\n",
      "val Loss: 0.6437\n",
      "Epoch 3/20\n",
      "----------\n",
      "train Loss: 0.8373\n",
      "val Loss: 0.6491\n",
      "Epoch 4/20\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:129: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
      "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.9341\n",
      "val Loss: 0.6783\n",
      "Epoch 5/20\n",
      "----------\n",
      "train Loss: 0.9256\n",
      "val Loss: 0.7011\n",
      "Epoch 6/20\n",
      "----------\n",
      "train Loss: 0.9229\n",
      "val Loss: 0.6432\n",
      "Epoch 7/20\n",
      "----------\n",
      "train Loss: 0.9260\n",
      "val Loss: 0.7235\n",
      "Epoch 8/20\n",
      "----------\n",
      "train Loss: 0.9349\n",
      "val Loss: 0.6742\n",
      "Epoch 9/20\n",
      "----------\n",
      "train Loss: 0.9216\n",
      "val Loss: 0.6703\n",
      "Epoch 10/20\n",
      "----------\n",
      "train Loss: 0.9507\n",
      "val Loss: 0.6540\n",
      "Epoch 11/20\n",
      "----------\n",
      "train Loss: 0.9401\n",
      "val Loss: 0.6977\n",
      "Epoch 12/20\n",
      "----------\n",
      "train Loss: 0.9144\n",
      "val Loss: 0.6758\n",
      "Epoch 13/20\n",
      "----------\n",
      "train Loss: 0.9041\n",
      "val Loss: 0.6878\n",
      "Epoch 14/20\n",
      "----------\n",
      "train Loss: 0.9218\n",
      "val Loss: 0.6601\n",
      "Epoch 15/20\n",
      "----------\n",
      "train Loss: 0.9107\n",
      "val Loss: 0.6482\n",
      "Epoch 16/20\n",
      "----------\n",
      "train Loss: 0.9031\n",
      "val Loss: 0.6726\n",
      "Epoch 17/20\n",
      "----------\n",
      "train Loss: 0.8943\n",
      "val Loss: 0.6832\n",
      "Epoch 18/20\n",
      "----------\n",
      "train Loss: 0.8802\n",
      "val Loss: 0.6565\n",
      "Epoch 19/20\n",
      "----------\n",
      "train Loss: 0.9269\n",
      "val Loss: 0.7033\n",
      "Epoch 20/20\n",
      "----------\n",
      "train Loss: 0.9367\n",
      "val Loss: 0.7320\n",
      "Training complete in 0h 10m 41s\n",
      "Best Loss  0.6432287328843083\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Training Projection Head\"\"\"\n",
    "\n",
    "pretrained_resnet_18 = timm.create_model('resnet18', pretrained=True)\n",
    "num_ftrs = pretrained_resnet_18.get_classifier().in_features\n",
    "pretrained_resnet_18.fc = nn.Linear(num_ftrs, 4)\n",
    "pretrained_resnet_18 = pretrained_resnet_18.to(device)\n",
    "\n",
    "criterion = SupervisedContrastiveLoss()\n",
    "optimizer = torch.optim.Adam(pretrained_resnet_18.parameters(), lr=0.001)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n",
    "model, history = train_model_SCL(pretrained_resnet_18, criterion, optimizer, scheduler, 20, dataloaders_re)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d702ad2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1584x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "plt.rcParams[\"font.size\"] = \"15\"\n",
    "fig = plt.figure(figsize=(22,8))\n",
    "epochs = list(range(20))\n",
    "\n",
    "plt.plot(epochs, history['train loss'], label='train loss')\n",
    "plt.plot(epochs, history['val loss'], label='valid loss')\n",
    "plt.ylabel('Loss', fontsize=20)\n",
    "plt.xlabel('Epoch', fontsize=20)\n",
    "plt.legend()\n",
    "plt.title('Loss Curve');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "edaf81b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_sizes = {'train':len(train_df), 'val':len(val_df)}\n",
    "    \n",
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch+1, num_epochs))\n",
    "        print('-' * 10)\n",
    "\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                model.train() \n",
    "            else:\n",
    "                model.eval()  \n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            for images, slope, labels in dataloaders[phase]:\n",
    "                images = images.to(device)\n",
    "                slope = slope.to(device)\n",
    "                labels = labels.type(torch.LongTensor).to(device)\n",
    "\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(images, slope).to(device)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    preds.to(device)\n",
    "                    loss = criterion(outputs.to(device), labels.to(device))\n",
    "\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                running_loss += loss.item() * images.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "                \n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "            acc_dict[phase].append(epoch_acc)\n",
    "            loss_dict[phase].append(epoch_loss)\n",
    "\n",
    "            if phase == 'val' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "    \n",
    "    show_curves(acc_dict, loss_dict)\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "76fe7b89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "----------\n",
      "train Loss: 0.6323 Acc: 0.4604\n",
      "val Loss: 0.4801 Acc: 0.5011\n",
      "Epoch 2/15\n",
      "----------\n",
      "train Loss: 0.5519 Acc: 0.4635\n",
      "val Loss: 0.4281 Acc: 0.5370\n",
      "Epoch 3/15\n",
      "----------\n",
      "train Loss: 0.4536 Acc: 0.5161\n",
      "val Loss: 0.4523 Acc: 0.5011\n",
      "Epoch 4/15\n",
      "----------\n",
      "train Loss: 0.3994 Acc: 0.5415\n",
      "val Loss: 0.5192 Acc: 0.5095\n",
      "Epoch 5/15\n",
      "----------\n",
      "train Loss: 0.3266 Acc: 0.6244\n",
      "val Loss: 0.4732 Acc: 0.5285\n",
      "Epoch 6/15\n",
      "----------\n",
      "train Loss: 0.2711 Acc: 0.6417\n",
      "val Loss: 0.6692 Acc: 0.3214\n",
      "Epoch 7/15\n",
      "----------\n",
      "train Loss: 0.2439 Acc: 0.6528\n",
      "val Loss: 0.4607 Acc: 0.5032\n",
      "Epoch 8/15\n",
      "----------\n",
      "train Loss: 0.1616 Acc: 0.7314\n",
      "val Loss: 0.5730 Acc: 0.4841\n",
      "Epoch 9/15\n",
      "----------\n",
      "train Loss: 0.1525 Acc: 0.7364\n",
      "val Loss: 0.5597 Acc: 0.5201\n",
      "Epoch 10/15\n",
      "----------\n",
      "train Loss: 0.1529 Acc: 0.7358\n",
      "val Loss: 0.5996 Acc: 0.5159\n",
      "Epoch 11/15\n",
      "----------\n",
      "train Loss: 0.1478 Acc: 0.7401\n",
      "val Loss: 0.6986 Acc: 0.5116\n",
      "Epoch 12/15\n",
      "----------\n",
      "train Loss: 0.1294 Acc: 0.7593\n",
      "val Loss: 0.7504 Acc: 0.5180\n",
      "Epoch 13/15\n",
      "----------\n",
      "train Loss: 0.0957 Acc: 0.7797\n",
      "val Loss: 0.6071 Acc: 0.5603\n",
      "Epoch 14/15\n",
      "----------\n",
      "train Loss: 0.0845 Acc: 0.8007\n",
      "val Loss: 0.5874 Acc: 0.4926\n",
      "Epoch 15/15\n",
      "----------\n",
      "train Loss: 0.0911 Acc: 0.7970\n",
      "val Loss: 0.6391 Acc: 0.5581\n",
      "Best val Acc: 0.560254\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1584x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"Training Resnet Classifier\"\"\"\n",
    "\n",
    "resnet_classifier = resnet18(pretrained=False) \n",
    "\n",
    "num_ftrs = resnet_classifier.fc.in_features\n",
    "resnet_classifier.fc = nn.Linear(\n",
    "    in_features = num_ftrs, \n",
    "    out_features = nbClass\n",
    ")\n",
    "\n",
    "resnet_classifier = resnet_classifier.to(device)\n",
    "criterion = FocalLoss()\n",
    "optimizer = torch.optim.Adam(resnet_classifier.parameters(), lr=1e-3, weight_decay=1e-5)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n",
    "acc_dict = {'train':[], 'val':[]}\n",
    "loss_dict= {'train':[], 'val':[]}\n",
    "\n",
    "resnet_classifier = train_model(resnet_classifier, criterion, optimizer, scheduler, num_epochs=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "13e9b0ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(model): \n",
    "    running_accuracy = 0 \n",
    "    total = 0 \n",
    " \n",
    "    with torch.no_grad(): \n",
    "        for images, slope, labels in testloader: \n",
    "            images = images.to(device)\n",
    "            slope = slope.to(device)\n",
    "            labels = labels.type(torch.LongTensor).to(device)\n",
    "            outputs = model(images, slope).to(device)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "            preds.to(device)\n",
    "\n",
    "            total += labels.size(0) \n",
    "            running_accuracy += (preds == labels).sum()\n",
    " \n",
    "        print('Accuracy of the model based on the test set is: %d %%' % (100 * running_accuracy / total))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "05f145b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model based on the test set is: 50 %\n"
     ]
    }
   ],
   "source": [
    "test(resnet_classifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87b03b1d",
   "metadata": {},
   "source": [
    "## Multimodal SuperCon using Custom Efficientnet as Backbone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "70b9a599",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "----------\n",
      "train Loss: 0.7364\n",
      "val Loss: 0.6441\n",
      "Epoch 2/20\n",
      "----------\n",
      "train Loss: 0.7033\n",
      "val Loss: 0.6431\n",
      "Epoch 3/20\n",
      "----------\n",
      "train Loss: 0.7250\n",
      "val Loss: 0.6363\n",
      "Epoch 4/20\n",
      "----------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:129: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
      "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train Loss: 0.5815\n",
      "val Loss: 0.5408\n",
      "Epoch 5/20\n",
      "----------\n",
      "train Loss: 0.5746\n",
      "val Loss: 0.5437\n",
      "Epoch 6/20\n",
      "----------\n",
      "train Loss: 0.5727\n",
      "val Loss: 0.5417\n",
      "Epoch 7/20\n",
      "----------\n",
      "train Loss: 0.5757\n",
      "val Loss: 0.5425\n",
      "Epoch 8/20\n",
      "----------\n",
      "train Loss: 0.5708\n",
      "val Loss: 0.5430\n",
      "Epoch 9/20\n",
      "----------\n",
      "train Loss: 0.5717\n",
      "val Loss: 0.5412\n",
      "Epoch 10/20\n",
      "----------\n",
      "train Loss: 0.5750\n",
      "val Loss: 0.5417\n",
      "Epoch 11/20\n",
      "----------\n",
      "train Loss: 0.5640\n",
      "val Loss: 0.5421\n",
      "Epoch 12/20\n",
      "----------\n",
      "train Loss: 0.5699\n",
      "val Loss: 0.5409\n",
      "Epoch 13/20\n",
      "----------\n",
      "train Loss: 0.5695\n",
      "val Loss: 0.5418\n",
      "Epoch 14/20\n",
      "----------\n",
      "train Loss: 0.5747\n",
      "val Loss: 0.5413\n",
      "Epoch 15/20\n",
      "----------\n",
      "train Loss: 0.5782\n",
      "val Loss: 0.5407\n",
      "Epoch 16/20\n",
      "----------\n",
      "train Loss: 0.5585\n",
      "val Loss: 0.5407\n",
      "Epoch 17/20\n",
      "----------\n",
      "train Loss: 0.5633\n",
      "val Loss: 0.5422\n",
      "Epoch 18/20\n",
      "----------\n",
      "train Loss: 0.5711\n",
      "val Loss: 0.5398\n",
      "Epoch 19/20\n",
      "----------\n",
      "train Loss: 0.5722\n",
      "val Loss: 0.5412\n",
      "Epoch 20/20\n",
      "----------\n",
      "train Loss: 0.5665\n",
      "val Loss: 0.5422\n",
      "Training complete in 0h 12m 50s\n",
      "Best Loss  0.5398450154834772\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Training Projection Head\"\"\"\n",
    "from collections import defaultdict\n",
    "from time import time\n",
    "\n",
    "pretrained_eff_b1 = timm.create_model('efficientnet_b2', pretrained=True)\n",
    "num_ftrs = pretrained_eff_b1.get_classifier().in_features\n",
    "pretrained_eff_b1.fc = nn.Linear(num_ftrs, 4)\n",
    "pretrained_eff_b1 = pretrained_eff_b1.to(device)\n",
    "\n",
    "criterion = SupervisedContrastiveLoss()\n",
    "optimizer = torch.optim.Adam(pretrained_eff_b1.parameters(), lr=0.001)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n",
    "\n",
    "\n",
    "model, history = train_model_SCL(pretrained_eff_b1, criterion, optimizer, scheduler, 20, dataloaders_re)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3dd960e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j/Pn8+fOYNGkSpk6dakwbP358vcvQUNQc2kQWRUhNjo9lq/BHDvUNEkKsIzw83CQAAkB5eTn++9//IiIiAp6ennB3d8d//vMf3Lp1C1qtttbrDRkyxOQ4NDQUd+/ebVCZzp07hyFDhhgDIAD07t0bgYGBOHnyJAAgLCwMSUlJ+O9//4tz586Z1fDCwsKwdetWrFmzBpcuXQLLsmhKFASbSJS3EIN8TGt+y5txFZkDd8rR7accdPspB79nljfb7yWENA8PDw+ztHfeeQfr1q3Dc889h+3bt+PIkSN44403ABgCZG2cnZ1Njvl8fp3nVJebm2uxXJ6enrh//z4A4JlnnsHbb7+NXbt2ISYmBiEhIXj//feNwfCNN97ArFmzsGHDBjz66KPo1q2bsYbaFKg5tAktDHfC/7If1P6Sc9Q4nqPCo95N2yyamq/GjCMFUFV8wfr3CTlSJ3nRElKEoOF9cy2Vpf/Pu3fvxgsvvGDShHngwIFmK5OXlxfy8/PN0vPy8hAeHg4A4HA4iIuLQ1xcHDIzM7F9+3b85z//ga+vL2bOnAmRSITFixdj8eLFuH79OjZu3IhFixYhJCQEsbGxVi8z1QSb0ABvIQZXqw029ZqicpUezx4pNAZAALhVqoNc3bRNCoQQ6xMIBA0aoalUKiEQPBidrtPpsHPnzqYomkW9e/fGoUOHUFLyYET8+fPncfv2bfTr188sv7+/P+bPn4/27dsjLS3N7PkOHTrg/fffh1AotPi8NVBNsIktinDCsSq1weM5aiRnqzDQx/q1QT3L4qXk+7hVaj6K6k6pFi5CgYWzCCEtVUhICPbt24c9e/bAz88P3t7e8PHxqTF/dHQ0NmzYgPbt28PFxQUbNmx4qGkODysuLg7ffPMNJk6ciNdee804OrRr167GwS/z5s2Di4sLevfuDalUiuTkZFy/fh3Lli0DAEybNg3h4eHo0aMHRCIREhMTodVqERUV1SRlpppgE+vnJUS0r3nfYFN09q65WIr9dyy34WeWNc3wYkJI05k1axaio6PxyiuvIDo6Gps2bao1/4oVK9C/f38sWLAAr7zyCrp06VLnqFBrcnd3x6+//gqhUIhZs2bhjTfeQP/+/bFr1y5jDTUyMhJ//PEH4uLi8OSTT2LPnj1Ys2YNRo8eDQDo27cv9u7di9mzZ2PatGlITU3F5s2bERER0SRlZuRyObWTWVF6ejpCQkJM0k7lqvD4PtN28sTh7mYDZxojOVuFsQfyoa/hr/lRP2fM7uJotd9na5buM7G+1nyfi4qKzAZ7tFTl5eW0qW4jPezfm2qCzaCvlxBDmrA2mKPQ4fljhTUGQADItNBESggh9o6CYDOpPm/wRK7aZOTow9LoWfzraCHylA+WRWIATAw2XReQmkMJIcQcBcFmEukpQKxf9dpgSaNrg/85V4wTuaZr6r3R0wkzOjmYpFEQJIQQcxQEm9HCarXBk3lqk5GjDbXnlhJrL5WapEX7CrEw3AkBjlyT9CwKgoQQYoaCYDPq7SHAUCvVBm8Ua/Fy8n2TNF8JB+sHu4DLYeArMQ2CdxU6aGvrNCSEEDtEQbCZVa8NnspT48jdhtUGlVoWM44UoljzIKjxGGBTtCvcRYbgJ+Ix8BA9+PPqWSBbQbVBQgipioJgM+vlIcDj/o0bKbrgpByXCk037/1PpDP6eJpe179akyj1CxJCiCkKgjbwf+GmtcEz9zQ4XM/a4JZrZfg+XWGSNq6dGC91dTDL6+9A/YKEEFIbCoI28IiHAI8HmE6MrU9t8EKBGgtOyk3SOkp5WBsls7iYrl+1IEhzBQkhxBQFQRtZFO5kcnz2nga/Z9VcG6xcGLu8ShyT8BhsHuIKqcDyn7F6TZCaQwmxT8uXL0f79u2Nx8nJyZDJZPj7779rPW/JkiUICwurNc+cOXPw2GOPWaOYNkFB0EbC3QUYUc/aIMuyiDt+HxklpkHs4wEydHXh1/g7AhxN10e/Q0GQEAKgZ8+eSEpKQnBwsK2LYnMUBG3o/6rVBs/na5CUaV4bXHepFHtvmy6MPbOzA57qIKn1+tQnSAixRCqVIjIyEmKxuO7MbRwFQRsKdxdgZGC12mCqaW0wJUeFZedM9yAMd+Pjv33qXijWvE9Q24jSEkKa0w8//AAPDw/I5XKT9CtXrkAmk+Ho0aMADJvmjhs3Dh07dkRAQABiY2Nx+PDhWq9tqTlULpdj1qxZ8PPzQ+fOnbFy5cqHLvuFCxcwZswY+Pj4ICgoCLNnz0ZeXp5JntWrVyMiIgJeXl4ICQnBxIkTkZubCwDQaDRYsmQJunfvDk9PT4SGhmLatGlQq9WWfl2jUBC0sYXVaoN/5mtwINNQ68tV6DDzaCF0VVpIZQIG30W7QsSre5d4TzEH/Cp/YbmaRYlGX/MJhJAWY9SoUWAYBnv27DFJ37lzJzw9PTFw4EAAwK1btzB8+HB89dVX2Lx5M/r06YNJkybh5MmTDfp9cXFxSEpKwn//+1988sknOHz48ENtyJufn4/Ro0dDqVRi/fr1WLFiBVJSUjBu3DhjENu2bRtWr16NuLg4/PLLL1i1ahXat28PhcIw8n316tXYvn073nrrLSQkJGD58uWQSqXQ6azfmkWb6tpYDzcBRgeKsKdKc2f8nyWI8RPh+WOFyFWaBq2vBrkiyKl+fzYOw8DPgYubVfoSs8p0CJXRdx9ivxyffcwmv7f0u6MNyi+TyRATE4OEhAQ888wzxvSEhASMHTsWXK6hpeeFF14wPqfX6zFw4ECkpaVhy5YtFndzt+TKlSvYu3cvNm7ciAkTJgAABg4ciO7du8PJyamOs02tW7cOAPDLL79AKjVMB+vQoQNiY2ORmJiISZMm4fz584iOjsasWbOM51VuugsYdqOfNGkSpk6dakwbP358g8pRX/Rp2AL8X7VVZFILNBh/IB/Hc0yr/v/u4Wg2taIu1C9ISOs1YcIEHDt2DIWFhQAMzYz//POPSUDIysrCSy+9hC5dusDNzQ3u7u44fPgwrl+/Xu/fc/78eQDAyJEjjWmOjo6Ijo5ucJnPnTuHIUOGGAMgAPTu3RuBgYHG2mlYWJix1nnu3DmzGl5YWBi2bt2KNWvW4NKlS02yCXklCoItQJgrH08EmQa36gFwkI8Qb1ULlvVBcwUJab1GjBgBPp+PxMREAIZaoJ+fH/r37w/AUPN7+umncfr0aSxatAi//vorjhw5gqFDh6K8vLy2S5vIy8uDk5OT2ca+7u7uDS5zbm4uPDw8zNI9PT1x/75hveNnnnkGb7/9Nnbt2oWYmBiEhITg/fffNwbDN954A7NmzcKGDRvw6KOPolu3bvjiiy8aXJb6sHkQTEtLM3aghoaG4oMPPqiz3Xf58uWQyWQW/61evdok7969ezFgwAB4eXmhb9++D9XG3RyqryJTlY+Eg28qFsZuqAAHmiZBSGvl6OiIYcOGGT+3KptCKxfHuHHjBi5cuIAVK1ZgxowZePTRRxEREQGlUtmg3+Pp6YmSkhKzwJmfn9/gMnt5eVk8Ly8vDy4uLgAADoeDuLg4nD59GpcuXcKrr76K1atX47vvvgMAiEQiLF68GBcvXsS5c+cwfvx4LFq0CL///nuDy1MXm/YJyuVyjBs3Dp07d8bWrVuRkZGBJUuWgGVZLFmypMbzZsyYgdjYWJO0vXv34pNPPjFJP3HiBGbMmIHnn38e8fHxSEpKwvPPPw+ZTIYhQ4Y02et6GN1d+RjbToTdN03fhFwG+PYxV3iIuTWcWTuz9UNphCixcw3tm7O1CRMmYObMmfjtt99w8+ZNTJw40fhcZbATCATGtNu3b+PUqVPo1q1bvX/HI488AgDYt2+fsU+wtLQUR44caXCfYO/evfHNN9+gpKTEeO758+dx+/Zti32U/v7+mD9/Pn744QekpaWZPd+hQwe8//772LBhA9LS0sw++xvLpkFw48aNUCqV2LJlC6RSKaKjo1FSUoL4+HjMnTvXpE25Kj8/P/j5+ZmkffTRR+jUqRN69OhhkjZgwACsWLECADBo0CCkpaVhxYoVLS4IAobaYPUg+G5vKfp5CWs4o27UJ0hI6zZs2DBIJBLMnz8fQUFB6NWrl/G5Tp06wc/PD0uWLMHixYtRWlqK5cuXw8fHp0G/o0uXLhgxYgRef/11lJSUwMvLC2vXroVEUvtcZEvi4uLwzTffYOLEiXjttddQVlaGd999F127djUOfpk3bx5cXFzQu3dvSKVSJCcn4/r161i2bBkAYNq0aQgPD0ePHj0gEomQmJgIrVaLqKioBpenLjZtDk1KSkJMTIxJsJswYQKUSiVSUlLqfZ3CwkIcOXLE5BuSSqVCcnKy2YiiCRMm4PTp0ygqKmr8C7Cyri58vN7D0Xj8TIgEcd0cazmjbmZ9ghQECWlVxGIxRowYgZycHGMtrZJQKMSWLVvA4/Hw7LPP4oMPPsD8+fMfKlh88cUXGDJkCBYtWoRXX30VgwcPNvt99eHu7o5ff/0VQqEQs2bNwhtvvIH+/ftj165dxhprZGQk/vjjD8TFxeHJJ5/Enj17sGbNGowePRoA0LdvX+zduxezZ8/GtGnTkJqais2bNyMiIqLB5akLI5fLbbbTaseOHfH8889j0aJFJum+vr5YuHAh5s6dW6/rbNq0CfPmzcO5c+fQoUMHAIa+xn79+uHXX381zqcBDNXyIUOG4PDhw8YmAGtKT09HSEjIQ5/PsizO5Wug1bONqgFWKlbrEfhDtvFYwAFyZviCY2HB7daksfeZ1E9rvs9FRUVwdq57UYmWoLy83GxQCmmYh/1727xP0FKhZTKZ2SoJtfnll1/Qs2dPYwCsvDYAs+vLZDKT51sahmHQ20NQd8Z6kgo4cBYwKFIbvuuo9cA9pR5ekofrYySEkLak1U+Wz8nJQUpKCt59912rXTM9Pd2m51ubJ0+EIvWDlu8TaTfRzan1rxzT0u5zW9Va77NIJIJQ2PjWlObSkCkNxFxxcbHZ0myVamvNsGkQlMlkKC4uNkuXy+XGGltdEhISwLKsWd9f5fnVr19ZA6zt+o1p/mmJzUftb+YjXfFgYW7G1Rch7Vr3wrkt8T63Ra35PhcVFbWaJkZqDm08qVSKgICABp9n04ExISEhZt8yMzMzoVAo6v0fb+fOnejXrx/8/f1N0oODg8Hn882uf+3aNXA4HJOm07bOv9qWSjQ4hhBCDGwaBIcOHYpDhw6hpKTEmJaQkACxWFyv0U23bt3CmTNnMGnSJLPnhEIhBg4ciF27dpmkJyQkoE+fPq2mw9wazDbXpbmChBACwMZBcObMmRAKhZg+fTqOHj2KTZs2IT4+HnFxcSbTJiIiIvDKK6+Ynb9z507weDyMGzfO4vUXLFiA48ePY+HChUhOTsbSpUtx8OBBvPnmm031klokmitICCGW2TQIymQy7N69GzqdDlOmTMHy5csxZ84csykTWq3W4lJqv/zyCwYPHgw3NzeL1+/fvz++++47HDt2DBMnTsRvv/2GDRs2tMiJ8k2J5goSe9WUCy+TlqMxf2ebzhNsi1riQILbpVr02J5rPPYUc3BtSsNWlGhpWuJ9bota833WarUoKSmBTCYzrrXZUtHAmIfHsizkcjmcnJzA4zV8rGernyJB6uYj4YLDAPqKrzt5Sj1UOhZCbsv+YCCkMXg8HpycnCyOQG9piouLa1wmktTtYQMgQEHQLvA5DHzEXGQpHjSD3i3TIVhKf37StvF4vFYxCC4vL++hhveTxrP5VkqkeVTvF6QtlQghhIKg3aAtlQghxBwFQTthNleQaoKEEEJB0F7QXEFCCDFHQdBO0FxBQggxR0HQTpj3CVIQJIQQCoJ2IsBCTZBW0yCE2DsKgnbCRciBhPdgcnyZljVutEsIIfaKgqCdYBiG5goSQkg1FATtCG2pRAghpigI2hGaK0gIIaYoCNqR6iNEaa4gIcTeURC0IzRXkBBCTFEQtCNm0yRoriAhxM5RELQj/g6mWydRTZAQYu8oCNoR32o1wWyFDlo9zRUkhNgvCoJ2RMxj4C568CfXsUCOgmqDhBD7RUHQztA0CUIIeYCCoJ2hIEgIIQ9QELQzNFeQEEIeoCBoZ8zmCtI0CUKIHaMgaGcCqk2ToEW0CSH2jIKgnTHbXJeCICHEjlEQtDPVB8ZkldFOEoQQ+0VB0M54ijngV/mr31exKNXobVcgQgixIQqCdobDMPCV0AhRQggBKAjaJeoXJIQQAwqCdsi8X5CCICHEPlEQtEPVg+AdmitICLFTFATtEG2pRAghBhQE7ZBZn2ApTZMghNgnCoJ2iPoECSHEgIKgHaq+fmiWQgc9S5vrEkLsDwVBOyQVcCAVMMZjlQ7IL6cJ84QQ+0NB0E6Z7StII0QJIXaIgqCdCqDNdQkhhIKgvfKjaRKEEEJB0F6ZL51G0yQIIfaHgqCdoj5BQghpAUEwLS0NY8aMgY+PD0JDQ/HBBx9Ap6vfB3JiYiKio6Ph7e2N4OBgTJw4EWVlZcbn58yZA5lMZvbv2rVrTfVyWg2aK0gIIQCv7iym5HI5cnJyEBwcDKFQaEz//vvvsXfvXjg4OGDOnDno1atXva41btw4dO7cGVu3bkVGRgaWLFkClmWxZMmSWs/dvHkzFixYgLlz5+K9996DXC7H//73P2i1ps16nTp1wmeffWaSFhgY2IBX3DZVnytIfYKEEHvU4CD43nvv4eeff0Z6erox7auvvsKiRYvAVky43rt3L44cOYLQ0NBar7Vx40YolUps2bIFUqkU0dHRKCkpQXx8PObOnQupVGrxvIKCArz11ltYsWIFnn32WWP6E088YZZXIpEgMjKyoS+zzfN14IIBUDlFPleph0rHQshlajuNEELalAY3h548eRKDBg2CWCw2pn366afw9fXFvn37sGnTJgAwq31ZkpSUhJiYGJNgN2HCBCiVSqSkpNR4XkJCAgDg6aefbmjxSQU+h4GPxPTPf5dqg4QQO9PgIJidnY2goCDjcVpaGrKysvDCCy+gf//+GDt2LIYPH44//vijzmulp6cjJCTEJC0gIAASicSkplnd2bNn0bFjR2zZsgVdu3aFu7s7YmJicOrUKbO8V69eRUBAADw9PTF8+HAcP368Aa+2baPdJAgh9q7BQbC8vBwikch4fOrUKTAMg8GDBxvTgoODkZ2dXee15HI5nJ2dzdJlMhnkcnmN5+Xl5eGff/7BypUrsWzZMvz444+QSCSYNGkS8vLyjPl69OiB999/H9u2bcP69euh0+kwfvx4nDt3rp6vtm2jfkFCiL1rcJ+gj4+PyejKQ4cOwcnJCWFhYcY0uVxuEiitjWVZlJaWYtOmTYiNjQUA9OnTB2FhYfj666+Ng2rmzJljct7QoUPRr18/rFq1Clu3bq3x+rXVQuujsec3FwcNHwDfePzXrVz0ZlvPfMHWcp9bO7rPzYPuc9Op3uJYVYOD4MCBA7Ft2zZ8/fXXEIlE+O233zBmzBhwOA8qlRkZGfDz86vzWjKZDMXFxWbpcrkcMpms1vMYhsGjjz5qTJNKpQgPD8fVq1drPE8ikWDYsGHYv39/reWq7YbVxVITb0sVpikFsoqMx0qRDCEhLjYsUf21pvvcmtF9bh50n22nwUFw/vz5SExMxMKFC8GyLBwdHbFw4ULj88XFxTh58iSmTZtW57VCQkLMvv1kZmZCoVDU+obo1KkTWJY1jkatxLKsSTC2hGFo9GMlmitICLF3De4TbNeuHU6ePIn4+Hh8+OGH+OOPP0wC1o0bN/Dcc89h6tSpdV5r6NChOHToEEpKSoxpCQkJEIvFiIqKqvG84cOHAwCSk5ONaUVFRUhNTUX37t1rPE+pVOLAgQMIDw+vs2z2gPoECSH2jpHL5TbbTVUul6Nv377o0qUL5s2bh5s3b2Lx4sWYM2eOyWT5iIgIREVFYd26dca0qVOn4ty5c3jnnXfg5uaGNWvW4OrVqzh37hxkMhmKioowZcoUPPnkk2jfvj0KCgrw+eef48KFCzhw4AAiIiKa5DVpPlwAJwEfrMQBrNgBkDiCFTuAlVT92fAIsQNYB0dAIAJsUEMtKNehw7Yc47Ejj8GdZ3xaRW2Zmo+aB93n5kH32XYa3Bxak8LCQqSkpEAikeCxxx4Dl8ut8xyZTIbdu3djwYIFmDJlCpydnTFnzhwsWrTIJJ9WqzVbSu3rr7/G0qVLsXjxYiiVSvTt2xeJiYnGvkShUAg3NzesXLkS9+7dg0gkQmRkJPbu3dtkARAAnDL+Bk9ZVnfGKlgOBxA7VgmcDtC7+0DbPxa6ro8AdTTxPixXIQdiLgOlzvA9qFTLokjNQiZs+UGQEEKsocE1wW+++QZbt27Fjh074OJiGESRmpqKiRMn4v79+wAMNbfExEQ4ODhYv8QtGcvC4V8xYFjr7dKu9/KHJvoJaAYOBxzNp5M0VuTOXKQXPRgRenysJ7q78ms5o2Wgb87Ng+5z86D7bDsNrmLs3LkTDMMYAyAAvP3225DL5Zg2bRqGDh2K8+fPY+PGjVYtaKugUlo1AAIAJzcTwh+/gMO8SRB+vRycfy4DrPVasM37BVvPFAlCCGmsBjeH3rhxA8OGDTMeFxQUICUlBTNmzMAnn3wCAIiJicGOHTvw6quvWq2grQJPgPRn/g1/VxkYZRkYRRmgKK342fAIRZnxGMpSMIoyMBp1nZdmNBrwUw6An3IAusAO0AwZC23/WEAkaVSRaUslQog9a3AQLCwshIeHh/H45MmTAIDRo0cb0/r371/rZPQ2i8dDabtQ6BrarKHVGIKjohSMshRMUSF4Jw6Bd+YYGJ15zYx7+zq4m1aD/fFLaKKGQTtkDPT+7R+qyGZBkEaIEkLsSIODoIuLCwoKCozHKSkp4HA46NOnjzGNYRioVCrrlNAe8PiAVAZWKjPu6qALHwD1tFfA+98+8I/8Ck5+jtlpTLkCgkO7IDi0C7qQ7obaYeRggC+o96+uvsM8zRUkhNiTBvcJdurUCfv370dhYSHkcjl27tyJRx55xGQniNu3b8PT09OqBbVHrNQFmtHToPhoK5Svfwht+ACwjOU/GTf9EkRffQCH+ZMh+OkrMHl36/U7qCZICLFnDa4JvvTSS5g2bRq6du0KHo8HhUKBd9991yTP2bNn8cgjj1itkHaPw4GuZ1/oevYFU5AL/tE94B3bC05RoVlWpqQIgn3bINi3DdqwSGhix0PXs3+N8xCrB8E71CdICLEjDQ6CI0eOxMcff2zcN3Dy5Ml46qmnjM8nJyejtLQUMTExViskeYB184J64vNQj30W3PPHwT+8G7wrf1rMy7t4BryLZ6Ae+TTUT71oMY9fte2UshU66PQsuByaK0gIaftsumJMW2SL+T7M3VvgH/kV/OP7DaNOq2H5fJR9uc/Q92hBx23ZyC9/MLXj8pPeZlMnWhqaV9U86D43D7rPttM0S5GQZsX6BkE97RWUfbID5c//H3TBoSbPMxoNONl3ajzfbK5gKc0VJITYh4cOgmfOnMGrr76KwYMHIzw8HIMGDcLcuXMt7u5OmolQBO2gEVAu+xLasEiTpziZN2o8jQbHEELs1UOtHfr+++9j9erVZlsZXbx4Ed9//z3mzZuHpUuXWqWA5OHoAzoCF88Yjzl3bgD9LeelIEgIsVcNrgnu2rULq1atgr+/P9auXYvU1FTk5OQgNTUVa9euhb+/Pz755BMkJCQ0RXlJPen9g02OOVkZNeatPleQgiAhxF40OAh+9dVX8PT0xJEjRzB9+nQEBQVBKBQiKCgI06dPx5EjR+Du7o7169c3RXlJPZkFwYY0h9I0CUKInWhwELx8+TLGjBkDNzc3i8+7ublh7NixuHTpUqMLRx6e3ifQsEVTBU5+LlDDFk/+1aZJUE2QEGIvGhwEtVotJJLaF22WSCTQammEoU0JhGC9/E2SOJmWm0TNm0Ppb0cIsQ8NDoLt2rXDgQMHoNdb3jJIr9cjKSkJ7dq1a2zZSCPpqi2qXVMQ9BJzwK/yTrivYlGmse6WUIQQ0hI1OAhOnjwZV69exdNPP43r16+bPJeRkYEZM2YgLS0NkydPtlohycPRB1QPgpb7BTkMAx8JLaRNCLE/DZ4iERcXh99//x0HDx7E77//Dh8fH3h5eSE3NxfZ2dnQ6/Xo168f4uLimqK8pAH0fqaDY7h1DI65XWVATGaZDp1kLX+HeUIIaYwG1wQFAgF27dqFt99+G0FBQcjKysL58+eRlZWFoKAgvP3220hMTIRAUP/tfEjT0AdUHyGaUeOu9DRNghBijx5qsjyfz8frr7+O119/HaWlpSguLoZUKoWjoyMAoLy8HEql0mR7JdL8WA9fsAIRGHU5AIApLQZTVAhWZj6yN4AmzBNC7FCj1w51dHSEr6+vMQACwOuvv47g4OBaziLNgsOB3q+dadIdy02i1XeToLmChBB70GQLaFdfUo3YRn1XjqGl0wgh9oh2kWjj9NWnSdRQEzTrE6SdJAghdoCCYBtnPjimhiBYrSaYpdBRbZ4Q0uZREGzjqk+T4GTdBPTmTZ1SAQdS/oPd5FU6mGy0SwghbREFwTaOdXYF6+RsPGY0ajB52RbzUr8gIcTeUBBs6xjGwvJp9esXvEMjRAkhbVy95gm6uro2dTlIE9L7BwNX/jQec+7cgK73ILN8ht0kVMZjWjqNENLW1SsIPswACYZh6s5EmkX1EaLcrAxoLOTzo+ZQQoidqVcQvH//flOXgzSh+m6wS1sqEULsDfUJ2oHqI0SZnCxArTLLRzvME0LsDQVBeyCWQO/ubTxkWD042bfNspnNFaTmUEJIG0dB0E7UZ+UYXwcuqvbk5ij1UOlowjwhpO2iIGgn6rPBLp/DwFti+pbIVlBtkBDSdlEQtBNmK8fUc/k0mitICGnLKAjaCYsb7FrgX21LJeoXJIS0ZRQE7YTeOwAs90GA49zPB8pKzPLRXEFCiD2hIGgveHzofQJMkiwNjqEtlQgh9oSCoB0xGyFqYYNdWkSbEGJPKAjaEbPl0yzVBGmuICHEjlAQtCP12WDX0k4StLkuIaStsnkQTEtLw5gxY+Dj44PQ0FB88MEH0OnqV/tITExEdHQ0vL29ERwcjIkTJ6KsrMwkz969ezFgwAB4eXmhb9++2LlzZ1O8jFbBfIPdDKBagHMTciCqEgdLtSyK1BQECSFtk02DoFwux7hx48AwDLZu3Yo333wTn332GZYvX17nuZs3b8bs2bMRGxuL7du3Y+3atWjfvj202gcDOU6cOIEZM2bg0Ucfxfbt2zFs2DA8//zzOHz4cFO+rBaLdfcGK5IYjxlFGZjCeyZ5GIYxmyZB/YKEkLaqXrtINJWNGzdCqVRiy5YtkEqliI6ORklJCeLj4zF37lxIpVKL5xUUFOCtt97CihUr8OyzzxrTn3jiCZN8H330EQYMGIAVK1YAAAYNGoS0tDSsWLECQ4YMaboX1lIxDPT+weD+c9mYxMm8AZ2bp0k2f0cu/il+8GUiq0yH7q78ZismIYQ0F5vWBJOSkhATE2MS7CZMmAClUomUlJQaz0tISAAAPP300zXmUalUSE5Oxvjx403SJ0yYgNOnT6OoqKiRpW+d6rNyjPlcQZomQQhpm2waBNPT0xESEmKSFhAQAIlEgvT09BrPO3v2LDp27IgtW7aga9eucHd3R0xMDE6dOmXMk5GRAY1GY3b9zp07Q6/X4/r169Z9Ma2E+Rqi9ZgmQUunEULaKJv3CTo7O5uly2QyyOXyGs/Ly8vDP//8g5UrV2LZsmX48ccfIZFIMGnSJOTl5RmvDcDs+jKZzOR5e1OfDXZpriAhxF7YtE/wYbEsi9LSUmzatAmxsbEAgD59+iAsLAxff/01lixZ0qjr11YLbY7zmxJXzaJHlWMm6xbS064AVZZUY4o4AETG43/yS5GeXtB8haynlnyf2xK6z82D7nPTqd4iWJVNg6BMJkNxcbFZulwuN9bYajqPYRg8+uijxjSpVIrw8HBcvXrVmAeA2fUra4C1Xb+2G1YXS028LY3e2RWcokIAAEenRSepBKxv0IMMRRrgUp7xMF8vQEhIYHMXs1at4T63BXSfmwfdZ9uxaXNoSEiI2befzMxMKBSKWt8QnTp1AsuyZpO4WZYFh2N4ScHBweDz+WbXv3btGjgcDjp06GClV9H61LVyTPWBMXfLdNDpaa4gIaTtsWkQHDp0KA4dOoSSkge7GSQkJEAsFiMqKqrG84YPHw4ASE5ONqYVFRUhNTUV3bt3BwAIhUIMHDgQu3btMjk3ISEBffr0sdgXaS/q2mBXwuPATfjgraFjDbvME0JIW2PTIDhz5kwIhUJMnz4dR48exaZNmxAfH4+4uDiTaRMRERF45ZVXTI5HjhyJV199FVu3bsWBAwfw9NNPg8/nY/bs2cZ8CxYswPHjx7Fw4UIkJydj6dKlOHjwIN58881mfZ0tTX2mSVRfPi2LpkkQQtogmwZBmUyG3bt3Q6fTYcqUKVi+fDnmzJmDRYsWmeTTarVmS6l9/fXXGDVqFBYvXoxnn30WfD4fiYmJJn19/fv3x3fffYdjx45h4sSJ+O2337Bhwwb7nChfRX022DWbK0jTJAghbZDNR4eGhobi119/rTXPxYsXzdIcHR2xevVqrF69utZzR48ejdGjRzeqjG2N3rcdWIYBU9GnytzLBlRKQCg25qFpEoQQe2DzBbSJDQhFYD19jYcMy4KTedMkS0C1IHiHgiAhpA2iIGin6tpg17xPkIIgIaTtoSBop8yCYB3TJKhPkBDSFlEQtFO6OpZPo+2UCCH2gIKgnTJbQ7Rac6iXmAMe8+C4UKWHQktzBQkhbQsFQTvFevmB5T/YI5BTdB8olhuPuRwGvg7UL0gIadsoCNorLg9633amSZnUL0gIsS8UBO1YXSvH0DQJQkhbR0HQjtW1wS5NkyCEtHUUBO1YXRvs0ghRQkhbR0HQjpkHwQxA/2AEKPUJEkLaOgqCdox18QArcTQeM6pyMAW5xuPq64emFqix6WoZyjQ0VYIQ0jZQELRnDFPryjHV+wSL1Czm/SFHl59zsPCUHOlFmmYpJiGENBUKgnZOV8sGu84CDnq586ufgmI1iy//LkPkzjyMO5CPPbeU0NLO84SQVsjmWykR26prmsSWIW5486Qc++6Uw1KcO3pXhaN3VfB34OK5zg6Y0UkCTzHXPCMhbUCZRo/UAg3O31PjapEWwU48vNLdEUIuU/fJpEWiIGjn6tpg19eBi+9j3HC7VItNV8uw+ZoC+eXmfYKZZTq8f74YH6YWY2w7MWaFOqCvpwAMQx8OpHXS6lmkybU4n6/G2XtqnLunxhW51uzL4MlcFbbFuoHHofd6a0RB0M6Z1QRz7gBaDcAzbQYNdORhaS9n/F+4FLtvKrHhShlO31ObXU+jB3bcUGLHDSW6ufAwu4sjJrcXw4FPLe+k5WJZFpllOpy7p8G5fEPASy3QQKGtu5k/KUuFJWeKEN9X1vQFJVZHQdDeOThB7+oBTuE9AACj04Fz9zb0gR0sZhdyGTzZQYInO0jwV4Ea36SVYft1JZQ68w+Ly/e1mPeHHEvPFOHpjhLM6uKAEGfzPsbmoNaxSC/S4u/7Gly+r8Hf9zW4KtfCkc9gsK8Qw/xF6O8lbPXNWnqWxZ1SHZwFHMiE9MWjJnKVHn/mq3EuX4Oz99Q4n69GnvLhRz1/+XcZQpx5eD7Use7MpEWhIEig929vDIKAYUeJmoJgVT3dBFgbJcB7vZ2x9R8FvkkrxfVi87mExRoWX10pw1dXyhDpwUcHKQ/+jjz4O3Dh78CFX8U/qaDxH9osyyJboTcGu8uFhsdrRVrUNLPj8n0tPr9cBkfeg4A41F9ktoB4S6PVGwL7XwUa/FVgqLlcLNCgVMtCwAHmhjlhQU+nVh/YrUGrZ3EyT419t5X4PVOFa0Xah75WRykPj3jwcTxbhbuKB2+qN08Wob0TD9F+ImsUmTQTCoLEME3iwinjMefODaB//c+XCTl4uZsjXurqgGN3VVifVob9NQykOXNPgzP3LE+tkAoY+Eu48Hc0BEV/B54xQAY4cuEj4Zp8oJdp9EiTa3H5vgaXCjXGwHdf9XAjVUu1LPbeLsfe2+UAgO6ufDzuL8RQfxEiPQTg2rDPR1PRP5War8aFAg3+KtDgYqHGYg0cANR6YOVfJdh7S4nPHnXBIx6CZi6x7ZVq9DiUpcK+20oczCx/qPeFh4iDXh4C9HLno5eHAI+4C4w17L8K1BixL9/YZKpjgWePFiJplAc6y2zT4kEajoIgqXP5tPriMAyi/USI9hPhTsVAmu9qGEhjSbGaxd9qLf6W1/wt3UvMgZ8DF/dKRcg8no2mnJhxqdAQXFddKIWLkEGMn6GGGOsnhJuo6WqJ5VoWV+SGQJear8ZfhYYarfohWuuuyLWI3XsPc7s7YmG4FCJe264V5ih02H+nHPtuK3EsWwVVAxY5kvAY9HTjo5e7AL08DEEvwIFb4+Cunm4CrB/kgmcOFxrfh8VqFk/9XoBDoz2a9D3SUulZFsVqFvdVepRo9Ah05LX4ZnlGLpfTBC8rSk9PR0hIiK2L0SCcW+mQLJ1tPNa7e0Gx6ierXFulY5F4U4kNaWU4lWc+kKY5+Ug46ObCRzcXPrq68hEq4+FWiQ4HM8uRlFmO3Hr2CTEAenvwjc2mPd34Fj8o9SyLUo3hX4lGb3ws0bAoURseqz5XpNbjilyLtPsa1GM8Ro04DCzWwjs587DuURn6eArrfa2W/n5mWUMNed9tQ+A7l1+/BRw4DBAq46GXuwC9PQR4xEOALjLeQ43w/PRiCd4+W2yS1t9LgF2Pu9e7Kbol3WeWZaHUsbivYiFX6XFfrcd9lR7yyn9qveG5yvQqzxepWZMvplwGGOwjxPhgMZ4IErfIgEhB0Mpa0pu53jRqOLwwHEyVdUNLv9wLiB2s+mtulmiRXqRFZqkOWWU63CnTIqtMZ/z3MDUdSyQ8Bl1kPHRzrQh4Lnx0c+HBtZZv5nqWxYUCDZIyy5GUqcKZe+p61zK9xRx0kvFRqqkMbHqUqFmUNiaS1ZO7iINwNz7C3QTo4cZHTzc+pAIO3jpdhG3/KMzyMwBe7uaIxY84QcKr+wOpJb6fK/v3fqsIfBkl9avuuQgZDPMXYWSgGEP8hHCy0ohllmUxN0WOLemm93tKBzG+GOhSr2lCLeE+X5Vr8OmlUiTeUqJYbf33Lp8DRPsKMT5YgpGBIjhbYQyANVAQtLKW8GZ+GJJFz4Jz95bxWLFkHfQh3Zvt9+tZFvnlemSW6pBZERQfPBqCZY5CbxKYGADtpdyKIMc3Br12TlxwGjk/saBch0NZKhzMLMfvmeWQN8GHQkP5SDjo6SZAz4pgF+4mgI+EU+OHbFJmOealyJGlMA8S7Z24+PRRF0R5114rbMz7uVSjx55b5fg9qxwlaj14HAYCDgM+B+BzGfAZw6MxreJRwGHAq3jkcxjwuYbndHoW/8tW4WCmCoWq+n1jaufExchAQ+Dr5ylosrl8ah2LCQfzcTzHtLVjaS8pXu/hVOf5tvzcOJ2nwicXS7Gvoi+8OQi5QIyfCBOCxRgeIIKjDadQURC0stYaBIXrloF/5qjxuPy516GNHmO7Almg1rHIVuhwV6FDXlYmYsOCm2X+oVbP4uw9NZIyy3EwU4WLhU2/ZmqAIxc9XfkIdzcEvR6ufHhJGt7HVKTWY+mZInx3zbxWCACzuzjgnV7SGj+EGvp+1uhZHMoqx/brSuy7XV7jwJ2m1MudjxGBYowMFKGLjNdsCzbcV+kRuyfPbIT0d9GuGNtOXOu5zf25oWdZHMwsx5qLpTiRa91uCic+A2cBBxwGuF2PnWfEXAbDAoSYECzBUH9hvVoorImCoJW11iDI370Zwp0bjcfq2PFQT3/NhiWqnS3v890yHX7PKseBO+U4eleFslqaPR14DBz5DJz4nIpHBo58DpwEhjTjMd+Qz8+Bix6u/Fqbbh/G0bvleDVFjjsWPpSCHLlYG+WCwb7mtcL63GeWZXEqT43tN5RIyFDWu5ZmLQKOod9pZKAYwwNF8HmILwvW8k+RBrF77pm0HIi5DPaNdEeEe80jdJvr/azWsdhxQ4FPL5XiSi0D0AQcwEXIgYuQA1nFnFOZgDEcCzlwqUirfN5FyEAm5MBZwAG/Sm37yn0NEm4a3hfp9ZiW4sBjMCJQhPHtxIjxEzXLQC4KglbWWoMg91wyxGvfNh7rQntCuWiNDUtUu5Zyn1U6Fn/mq6HQsoagJmDgyGPgJODAkcfYdFpFdSUaPd47W4z1aWUWn/9XZwne7e1sMl+ztvucJtdg+3UFtt9Q1usbvzXJBAyGBYgwysr9e9Zw7K4KEw/mmwxu8hZzcOgJT7M9Ois19fu5VKPHd9cU+PxSqcXm8UrhbnzMC3PCE0Eiq753WZbFpftaJGQosDNDiZv16MeV8g0BcUKwBNG+QgiaaL4rBUEraykfzg3F5GbC4c1njMesgxRln+0GWujan631PrcEx3NUePX4fYsDSvwduFgTJUNMxYTv6vf5bpkOv9xQ4Ocbyno1C/s7cDGpvRh9PAXQ6g3NpRo9oNaz0FY8anQsNKyhlmJMq8in0bNQG89jEeDAw+MBIvT3arr+PWvYfK0Mc1PkJmlhrnz8NtLdYtNzU72f7yl1+OrvMmxIK621X3uIrxCvhTlikI+wyZuPWZZFaoEGOzMMNcTMsroD4vGxnuju2jRzL2meIAEAsB6+YAUiMGpD5zhTVgymqBCszM3GJSPW9qi3EMfHeuL988X48u8yk8FGmWU6TDxYgGkhEnwQ6QzAsMRY4i0ltl9X4HhO3aNmZQIG49qJMbmDBP29BI0epNQazejkgPQiLT69VGpMu1iowQv/u4/vh7g2+T3JKNZi3eVS/JBehvIaYgyHAca3E2NumCN6ujXfYgoMwyDCXYAIdwHe6y3FmXtq7MxQYvdNJbIV5k3pnZx56ObSdKGKgiAx4HCg92sHbkbag6Q7N6CjINgmOfA5WN5XhrHtxHjluBz/FJv21/yQrsDhrHJ0Fglw4kR2nZPORVxgeIAYk9uLMdRf1GRNV63Jsl5S/FOkxW93Hoy63He7HO+eLca7FV8wrC01X401F0ux+5bS4lxRwPC3mh7igLjujmjnZNsQwDAM+ngK0cdTiP/2ccbJXDUSMpTYdVOJexWLbIwPFjdp7ZSCIDHS+webBsHMG9CFRdqwRKSp9fMSInmsJ5b/WYx1l0tNPjizFXpkK2r+iOAwwCAfISa3N0yEtsbar20Jl8Ng/WAXDN+Xj0tVmo7XXCpFR2cepneyzjzcgnIdkjJV+PG6AkfvqmrMJxMwmN3FES92dYB7C1zNhsMwGOAtxABvIeL7OuN4jhoJGQpMDK59ZG1jURAkRnr/mneZJ22XmMfgvUhnjGknxivH7yOtllGDgGHwxOQOEkwMFsPbhiMxWwNHPgc/xrgiZs89kxWJ5v8hRzsnHgb61H/1nkosy+JCoQYH75TjYGY5zt7T1NpE7e/ARVw3R0zvJLHpfLyG4HIMi9lbGrFsbRQEiVFdG+yStq23hwDHxnjio9QSfHyxBFWn+LVz4mJyewme7CC22XZYrZW/Iw/bYtww8rd7xv45LQtMP1yAQ6M90cG57o/hUo0eR++qjEv8Weo7q66rCw+vhTlhQrDYZNoCMUVBkBiZ1QSzbgJ6HcChb/v2QshlsKSXFGPaifDdNQXUJXLMCPdFbw/L66OS+nnEQ4AvB7riuaOFxjR5xWLbSaM9LJ5zo1iLAxW1vZQcVb2XFYzyFuC17k4Y6t/0Iz3bAgqCxIiVuoB1cgZTUgQAYDRqMHl3wXoH2LhkpLn1cBNgVX8B0tPvIcTT/rZhagrjgsVYUizF++cfLLb9T7EWMw4XYEV7wxSRP3JVOHDHsH5t9cFKtQlz5eNxfxFGB4kQXsukfGKOgiB5gGGg828P3pU/jUmczAzoKAgSYhX/7uGI9CINfrquNKYl56gxo1iE7NPZ9V503aFiA+jHW8kG0C0ZBUFiQu/fHqgaBO/cgK73IBuWiJC2g2EYrI1ywa0SHU5W2VosXcEB6piB2d6Ji2EBIgzzFyHKW1jvbZpI7SgIEhPVN9jlZt5A0y8XTYj9EHIZfB/jiphf7+FWLcvN8TnAAC8hhgWI8Li/EB1pQFKToCBITJjtMp9FI0QJsTZ3ERc/DXXDsL33TPbu8xJzMNTfUNt7zFdIcy+bAQVBYkLvZxoEmZwsQK0CBE0/X4cQexIq4yN5jCe+u1aG8qJCPNnDDz3c+Ha5zJwt0dcMYkosgd7d23jIsHqTzXYJIdYT5MTD0l7O+FeAFuHu9rnOqq3ZPAimpaVhzJgx8PHxQWhoKD744APodLUvVHjr1i3IZDKzfzNnzjTJN2fOHIv5rl271pQvqdWjlWMIIfbCps2hcrkc48aNQ+fOnbF161ZkZGRgyZIlYFkWS5YsqfP8//znP+jXr5/x2M3NfLHnTp064bPPPjNJCwwMbHzh2zB9QHsg9Q/jMa0cQwhpq2waBDdu3AilUoktW7ZAKpUiOjoaJSUliI+Px9y5cyGVSms9PyQkBJGRtS/wLJFI6sxDTJkNjqGaICGkjbJpc2hSUhJiYmJMgt2ECROgVCqRkpJiw5LZN7MgeIdqgoSQtsmmQdDSbsoBAQGQSCRIT0+v8/y4uDi4urqic+fOeOutt6BUKs3yXL16FQEBAfD09MTw4cNx/Phxq5W/rdJ7B4LlPmgk4MjzgdLiWs4ghJDWyeZ9gs7O5ptLymQyyOXyGs8TCoWYPXs2oqOj4eTkhOPHj2PNmjXIyMjAtm3bjPl69OiB3r17o3PnzigoKMC6deswfvx47N+/H7169WqKl9Q28HjQ+wSCW6UZlJOZAX1oTxsWihBCrK9VzhP09vbGRx99ZDweOHAgPD098e9//xsXL15EWFgYAMPo0KqGDh2Kfv36YdWqVdi6dWuN169PLbQ2jT2/JQhydodrlSBY8Ocp5HMlNiyRubZwn1sDus/Ng+5z06ne4liVTYOgTCZDcbF5M5tcLodMJmvQtcaOHYt///vfSE1NNQbB6iQSCYYNG4b9+/fXeq3ablhdLDXxtkb8Lj2By6eNx96qUri0oNfVVu5zS0f3uXnQfbYdm/YJhoSEmH37yczMhEKhaPAbonLfrLr2z6L9terHfINdGiFKCGl7bBoEhw4dikOHDqGkpMSYlpCQALFYjKioqAZda/fu3QCA8PDwGvMolUocOHCg1jzEoPryaZysDICt3zYvhBDSWti0OXTmzJn46quvMH36dMybNw83b95EfHw84uLiTKZNREREICoqCuvWrQMALF++HKWlpejbty+kUilSUlLw6aef4oknnkD37t0BAEVFRZgyZQqefPJJtG/fHgUFBfj888+Rk5OD7777ziavtzVh3b3BiiRgyhUAAEZRBqYwD6ybV8MvVloMTmYGOHdvAiwL1s0TrLs39G7egLhl9TMSQuyLzfsEd+/ejQULFmDKlClwdnbGnDlzsGjRIpN8Wq3WZCm1Tp064dNPP8WWLVugVCrh7++PV199FW+88YYxj1AohJubG1auXIl79+5BJBIhMjISe/fuRURERLO9xlaLYaD3Dwb3n8vGJE5mBnS1BUGVEpzMm+BkZRiCXmaG4Wd5QY2nsA5O0Lt5gXX3gt7N2xAc3b3AunlB7+4FODoD1mzC1mqAcgUYpQJMuRJQlwMaDRitBtCqDT9r1IZ8Gg0YrRrQqKvk0QAadbU8GrBCEVgXd7CuHtC7eIB19TAcO7sC3FY5/owQu8DI5XJq47KittTBLfx2FfhHfzUeq558AZpRUwGtBpzsOw+CXGXAu3fX6mVgBaKKAOlVJUB6425BIXxdnIFyJZhyBRhl2YOfyxUVgU5peDSmKQ2BrBmxDAeszLUiKFYNkB7QVwRN1sUd4Avqvphebwi8apUhCFc+atSApjLNcGwI2FpApwWj0xp/hk4LpsrP0FY8XyVP1fylKjUcvHzAOjiBdZCCdXAEHKQVxw/+QewAcBrZu6LXGf5Gysq/X1mVnyv+hgwDli8A+AKwAqFhd5PKn/kCw3MmaUKAy7XuF6kmUOfnhqocTFEhmOL7YOSFYIoKwSk2PBr+3QdTIgcrdoTe2w+spx/03v7Qexn+wcnKXyYfhl4HKCv+rooyQFkGRlla8bPC+DOjrHhOUQpGWQZt117QjH+uyYpFX1FJjaqvHMM/tBu84wfByb0Dpo5Fzq2FUZeDuXvLbCeL9jXkb2kYVg/mfj5wPx/AlRrz6Z1khmAoENYY3Jo7gAOADACu/llnPpbhABJHsA6OFcGyIjhWPrJsxYde1S8pZYafKz8YVeVN8hpYhgMIDMHREECFFQFUAJZfEUgrnxOIan5OKKpIqxJgjYGfNXkw/GxyYCGtgl4PafpV8LKuGoMapzLgVQa5cvOFQGrCvWW+QQArcYDe0w96L3+wXoZHQ5D0e7jWFrUKTEkRmBK54V+x3PS4xHCMslJDcKv8uz8E1tm1STf2piBIamS2fFpB7kNdh+XyoPcNMlyPxwdTkAtOfg6YgjxDrYOAUyIHSuS2LsZDY1g9UFYMpqwYgPVbBBqDYfWAqtxQm7J1YWrQoYmvzyjKwL15DdybNQTIihoj6+UHvacfoNebBTRDoKv410RfWCxSlDXp5SkIkhrpAhpW32IZxtAM4x8MvX8wdP7tofcPBuvpB/AsvNX0ejDygoqgmAumIKfiMRdMfkWgVFv3PxvL4QAiCdiKfxBWNJ3x+IYmSR7f8K2fxwf4fLA8AcDnV0uvzF/xM5dv+LZbeA+c+/fA3M9/8HNJkVXLT4i1MYoycDOugptx1dZFsYhRUhAktuLoDG1oOHhpqWZP6V09jcFO71fx6BvUsB3oORxDn5irB/Qh3c2fZ1mgtMgYGKsGyrKSYkjcPCoCmtgQ0MQSk0dWJAGqP8cXNG/fiFplCPSVQbHQECSNPxfeMzR3sfp6XY7l8w3NeVX7vviVfWCGvjJj3xiXC3D5YHm8ip95hiDO5RrWhuXyDF9OuDzLxzwucm7dhI/UEUxpCRhFCZjSYjCKUqC0uOK4Ir0BzXW1vj6RGKzIARAbHlmxxPA3FjuAFYkBwLQ/VK2qGKikMjQZm6WpwOjrd29bMpbLA+vsAtbZFay04rHyn9QFemdXsE7OYErk4ORkgpOXBU5uFpiKnxm1ytYvAQAM/y8lDmDFDoDYEazYcAyxY5V0B7ASx4qfJdA7uzZpmWhgjJW1pYExAIDSYggO/gIoSyuaNNsbgp2Dk02L1abus05rCISF9wyDX6oM9HjQj1VRC23s4JMGqvd91mqqBMdSQ7AsKwFTVgKUlRiCsEhi+NATO1T7WWx4FIqb5vXptBUB0hAcoVYZgoJGbXhUqwx9r6qKR7XqQf6K5yvzGYOtWmUYWVz107Pyu5XxSxZT7bhKnmrPlbEMxD7+VQKdK1iZK/QVAQ8OTg//5Y1lDV+8cjPB5BqCo/HnhwyQLJcL1tEZrJMMrFQG1qni58p/UsMxHKQP/s5iCcDhPtxraEJUEyS1c5RCPeFfti5F28blgXX1BOvqaeuSPDweH6zUBZC6oMV9q+byADHPUKus0NLKeL0pv9QxDFhXD+hcPYAu1aaH6fVg5PmGWmNFcOTcywbL41cJcFWCXMUxJI62H21qJRQECSHEXnE4YF09oXP1NA+QdsKmy6YRQgghtkRBkBBCiN2iIEgIIcRuURAkhBBitygIEkIIsVsUBAkhhNgtmixPCCHEblFNkBBCiN2iIEgIIcRuURAkhBBitygIEkIIsVsUBK0gLS0NY8aMgY+PD0JDQ/HBBx9A10w7r9uLH374ATKZzOzfxo0bbV20Vu3GjRuYN28eBgwYAFdXV4waNcosD8uyWLVqFbp16wZvb2+MGDECFy5csEFpW6/63OewsDCz93enTp1sUFr7QgtoN5JcLse4cePQuXNnbN26FRkZGViyZAlYlsWSJUtsXbw2JzExEWKx2Hjcrl072xWmDbhy5QqSkpLQu3dvaLVai3k+/vhjfPTRR3jvvffQqVMnfPbZZxg3bhxOnDgBLy+vZi5x61Sf+wwAkydPxgsvvGA85vP5zVE8u0ZBsJE2btwIpVKJLVu2QCqVIjo6GiUlJYiPj8fcuXMhlUptXcQ25ZFHHoGjo6Oti9FmjBgxwlgrmTFjBgoKCkyeLy8vxyeffIL58+cbP5wjIyPRo0cPrF+/nr7o1VNd97mSl5cXIiMjm7Nodo+aQxspKSkJMTExJsFuwoQJUCqVSElJsWHJCKkbp45NbE+dOoXi4mKMHz/emObg4IDhw4cjKSmpqYvXZtR1n4nt0F+mkSztvB0QEACJRIL09HQblartioiIgJubG3r37o1vv/3W1sVp89LT08HlctGhQweT9M6dO9P7uwls2bIFHh4eCAwMxIwZM3D79m1bF6nNo+bQRpLL5XB2djZLl8lkkMvlzV+gNsrb2xuLFy9Gr169oNPp8Msvv2D+/PlQKBSIi4uzdfHaLLlcDgcHB3C5XJN0mUwGhUIBtVoNgUBgo9K1LSNHjkRkZCR8fX1x7do1fPjhhxg5ciRSUlIsfsYQ66AgSFqFmJgYxMTEGI+HDh0KlUqFlStXYs6cOdTcRFq9Dz/80PjzgAED0KdPHwwcOBA//PADXn75ZRuWrG2jT45GkslkKC4uNkuXy+WQyWTNXyA7MnbsWNy/f5+ajJqQTCZDWVmZ2ZQfuVwOiURCtcAm1LVrV4SEhOCvv/6ydVHaNAqCjRQSEmLWN5KZmQmFQmHWV0isi2EYWxehzQsJCYFOp8ONGzdM0q9du0bv72bAMAy9z5sYBcFGGjp0KA4dOoSSkhJjWkJCAsRiMaKiomxYsrZv9+7dcHNzQ2BgoK2L0mb17dsXUqkUu3btMqYpFArs378fQ4cOtV3B7MDff/+Na9euITw83NZFadOoT7CRZs6cia+++grTp0/HvHnzcPPmTcTHxyMuLo7mCFrR9OnT0atXL3Tr1g06nQ47d+7Ezp078eGHH1J/YCMoFArjVIfs7GyUlJRg9+7dAAxf8CQSCebNm4ePPvrIuILJZ599Br1ebzKpm9SurvucnJyMn3/+GY8//ji8vb2Rnp6OlStXwt/fH1OnTrVl0ds82k/QCtLS0rBgwQKcOXMGzs7OmD59OhYtWmQ2oo48vPfeew+JiYnIysoCy7Lo3Lkz5syZgylTpti6aK3arVu30LNnT4vP/fXXXwgKCjIum7Zx40YUFhYiIiIC8fHxNZ5HzNV1n0tKSvDWW2/h8uXLKCoqgqurK2JiYrB06VL4+Pg0c2ntCwVBQgghdovakQghhNgtCoKEEELsFgVBQgghdouCICGEELtFQZAQQojdoiBICCHEblEQJIQ02vLlyyGTyZCcnGzrohDSILRiDCEtQH0WW//1118xcODApi8MIXaEgiAhLcj//d//1fgcrZFKiPVRECSkBVm0aJGti0CIXaE+QUJaoap9cFu3bsXAgQPh7e2Njh07Ii4uDrm5uRbPu379Ol588UV06dIFHh4eCA0NxYsvvojr169bzK/T6bBx40Y8/vjjCAwMhLe3NyIiIvDqq6/WeM7u3bsxZMgQ+Pj4oF27dpg5cybu3r1rtddOiDVRTZCQVuzzzz/HkSNHMH78eMTGxuLkyZP44YcfcPz4cRw6dAju7u7GvOfPn8e4ceNQUlKCESNGIDQ0FNeuXcPPP/+M3377Dbt27cIjjzxizK9Wq/HUU0/hyJEj8Pf3x6RJk+Dk5ITbt29jz5496NevHzp06GBSnm+++Qa//fYbRowYgaioKJw9exY7d+7EpUuXkJycDKFQ2Gz3hpD6oCBISAuyfPlyi+kikQjz5883S//999/x+++/m+xQsGjRInzxxRdYtmwZ1q1bBwBgWRYvvfQSiouL8fXXX+PJJ5805t+5cydmzpyJF198EadOnTJuTRUfH48jR45g+PDh+O6770wCmEqlMtlDs9KhQ4dw+PBhdOvWzZg2a9Ys7NixA/v27cP48eMbeEcIaVoUBAlpQT788EOL6VKp1GIQfOqpp8y26Fm4cCF++OEH7NixA6tWrYJQKMSpU6dw7do19OnTxyQAAsCECROwfv16nDhxAidOnEBUVBR0Oh02bNgAsViMjz/+2KwGJxQKLdbqXnzxRZMACAAzZszAjh07cO7cOQqCpMWhPkFCWhC5XG7x3+3bty3mj4qKMktzdnZGWFgYysvLcfXqVQCGPesAYNCgQRavUzn14sKFCwCAa9euobi4GN26dWvQfnaWdkH39/c3vjZCWhoKgoS0Yp6enhbTvby8AADFxcUmj5Xp1Xl7ewMAioqKTB4buqGrs7OzWVrl5tI6na5B1yKkOVAQJKQVy8vLs5heOTpUKpWaPNY0ajQnJ8ckX2Uwy87Otl5hCWmBKAgS0oqlpKSYpRUVFeHixYsQiUTo3LkzAKBHjx415gdgXO6ssn+xU6dOcHZ2xuXLlykQkjaNgiAhrdhPP/1k7O+rFB8fj+LiYkycONE4eKVfv34ICQnBiRMnsHv3bpP8u3fvxokTJ9CxY0f0798fgKEJc9asWVAqlZg/fz5UKpXJOWq1Gvn5+U34yghpHjQ6lJAWpKYpEgAwatQoY42uUmxsLIYPH45x48bB29sbJ0+exIkTJxAYGIhly5YZ8zEMgy+++ALjx4/Hv/71L4wcORKdOnVCeno69u7dCycnJ3z55ZfG6RGAYQm3s2fPYv/+/ejVqxeGDx8OR0dHZGVl4fDhw3jvvfcwbdo0q98DQpoTBUFCWpCapkgAhrVDqwfBl19+GaNHj8YXX3yBhIQEODg4YOrUqVi6dCk8PDxM8vbu3RuHDx/GRx99hGPHjmH//v1wc3PDpEmTsGDBAoSEhJjkFwgE+OWXX7Bx40b8+OOP2LZtG1iWhbe3N0aPHm2sNRLSmjFyuZy1dSEIIQ2zfPlyfPjhh7SzBCGNRH2ChBBC7BYFQUIIIXaLgiAhhBC7RX2ChBBC7BbVBAkhhNgtCoKEEELsFgVBQgghdouCICGEELtFQZAQQojdoiBICCHEbv0/nk1SqMZGg0YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = list(range(20))\n",
    "plt.plot(epochs, history['train loss'], label='train loss')\n",
    "plt.plot(epochs, history['val loss'], label='valid loss')\n",
    "plt.ylabel('Loss', fontsize=20)\n",
    "plt.xlabel('Epoch', fontsize=20)\n",
    "plt.legend()\n",
    "plt.title('Loss Curve');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3c2b846f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet-b2-8bb594d6.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f5ed45d0e6645f5b6247aadb793fe5b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/35.1M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded pretrained weights for efficientnet-b2\n",
      "Epoch 1/15\n",
      "----------\n",
      "train Loss: 0.4237 Acc: 0.5897\n",
      "val Loss: 0.2846 Acc: 0.7188\n",
      "Epoch 2/15\n",
      "----------\n",
      "train Loss: 0.2443 Acc: 0.6838\n",
      "val Loss: 0.3637 Acc: 0.7590\n",
      "Epoch 3/15\n",
      "----------\n",
      "train Loss: 0.2120 Acc: 0.7067\n",
      "val Loss: 0.2547 Acc: 0.7674\n",
      "Epoch 4/15\n",
      "----------\n",
      "train Loss: 0.2260 Acc: 0.6931\n",
      "val Loss: 0.1450 Acc: 0.7484\n",
      "Epoch 5/15\n",
      "----------\n",
      "train Loss: 0.1789 Acc: 0.7228\n",
      "val Loss: 0.3193 Acc: 0.6808\n",
      "Epoch 6/15\n",
      "----------\n",
      "train Loss: 0.2132 Acc: 0.7061\n",
      "val Loss: 0.2328 Acc: 0.6829\n",
      "Epoch 7/15\n",
      "----------\n",
      "train Loss: 0.1736 Acc: 0.7407\n",
      "val Loss: 0.2797 Acc: 0.6934\n",
      "Epoch 8/15\n",
      "----------\n",
      "train Loss: 0.1382 Acc: 0.7611\n",
      "val Loss: 0.2876 Acc: 0.7463\n",
      "Epoch 9/15\n",
      "----------\n",
      "train Loss: 0.1195 Acc: 0.7679\n",
      "val Loss: 0.1529 Acc: 0.7400\n",
      "Epoch 10/15\n",
      "----------\n",
      "train Loss: 0.0751 Acc: 0.8317\n",
      "val Loss: 0.1455 Acc: 0.7759\n",
      "Epoch 11/15\n",
      "----------\n",
      "train Loss: 0.0848 Acc: 0.8267\n",
      "val Loss: 0.2042 Acc: 0.7273\n",
      "Epoch 12/15\n",
      "----------\n",
      "train Loss: 0.0776 Acc: 0.8428\n",
      "val Loss: 0.2246 Acc: 0.7590\n",
      "Epoch 13/15\n",
      "----------\n",
      "train Loss: 0.0694 Acc: 0.8323\n",
      "val Loss: 0.1849 Acc: 0.8013\n",
      "Epoch 14/15\n",
      "----------\n",
      "train Loss: 0.0577 Acc: 0.8595\n",
      "val Loss: 0.2612 Acc: 0.7801\n",
      "Epoch 15/15\n",
      "----------\n",
      "train Loss: 0.0528 Acc: 0.8577\n",
      "val Loss: 0.2070 Acc: 0.7801\n",
      "Best val Acc: 0.801268\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1584x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"EfficientNet B2 Classifier\"\"\"\n",
    "\n",
    "effnet_b2_classifier = effnetb2(pretrained=False)\n",
    "effnet_b2_classifier = effnet_b2_classifier.to(device)\n",
    "criterion = FocalLoss()\n",
    "optimizer = torch.optim.Adam(effnet_b2_classifier.parameters(), lr=1e-3, weight_decay=1e-5)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n",
    "acc_dict = {'train':[], 'val':[]}\n",
    "loss_dict= {'train':[], 'val':[]}\n",
    "\n",
    "effnet_b2_classifier = train_model(effnet_b2_classifier, criterion, optimizer, scheduler, num_epochs=15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "883cb230",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model based on the test set is: 72 %\n"
     ]
    }
   ],
   "source": [
    "test(effnet_b2_classifier)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}