Fastai4DEM-ResBlock.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Fastai4DEM-ResBlock.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XbvTrtv2dG-V"
      },
      "source": [
        "FASTAI 4 DEM - SRCNN with ResBlock\n",
        "=========================\n",
        "**This is only a template, no better results could be achieved than with a simple bicubic upsampling.**\n",
        "\n",
        "If you achieve any proper results please get in contact.\n",
        "\n",
        "The implementation is based on  https://github.com/benymd/super_resolution and https://github.com/hiromis/notes/blob/master/refactored_by_topics/CNN_L7_resnet.md"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rayuy-t3eOyV"
      },
      "source": [
        "# Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPtNyKFheTGJ"
      },
      "source": [
        "Download the training and test data of the austrian DTM: https://www.openmaps.online/data/austria_dtm_fastai.zip\n",
        "\n",
        "License: Data by - Geoland.at (2019) - CC BY 4.0 (CC BY 4.0)\n",
        "\n",
        "High resolution images: 300*300px\n",
        "\n",
        "Low resolution image: 100*100px\n",
        "\n",
        "Upsampled to 300*300px with next neighbor or bicubic method\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NyE-lUubmmdb"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mSDFfc5NM8j6"
      },
      "source": [
        "!pip install -Uqq fastbook\n",
        "import fastbook\n",
        "fastbook.setup_book()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NouTg_1FOiaR"
      },
      "source": [
        "!pip install rasterio"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t850JxBPntdx"
      },
      "source": [
        "# Data Loader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TJgqYlIDdFx"
      },
      "source": [
        "# adapt this parameter to the maximum height of your training dataset\n",
        "max_height = 3150\n",
        "# adapt the folder name to the low resolution images \n",
        "LR_PATH_TRAIN = \"/content/gdrive/MyDrive/fastai/data_100/lr_us_cc\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ft-MnjriNMD7"
      },
      "source": [
        "# read geotiffs\n",
        "import rasterio\n",
        "from fastbook import *\n",
        "from fastai.vision import *\n",
        "\n",
        "def raster2tensor(image):  \n",
        "    f = rasterio.open(image)\n",
        "    d1 = f.read(1)\n",
        "    tensorImage = TensorImage([d1])\n",
        "    # adapt \n",
        "    tensorImage = tensorImage / max_height\n",
        "    return tensorImage\n",
        "\n",
        "def raster2tensorHr(image):  \n",
        "    # adapt to replace the folder name to the high resolution images \n",
        "    image = str(image).replace(\"lr_us_cc\", \"hr\")\n",
        "    f = rasterio.open(image)\n",
        "    d1 = f.read(1)\n",
        "    tensorImage = TensorImage([d1])\n",
        "    tensorImage = tensorImage / max_height\n",
        "    return tensorImage "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8eKQyoDNSBn"
      },
      "source": [
        "# valid_pct = percent of the total images that is used for validation \n",
        "db = DataBlock(blocks=(TransformBlock(type_tfms=raster2tensor),\n",
        "                       TransformBlock(type_tfms=raster2tensorHr),\n",
        "                      ),\n",
        "               get_items = get_image_files,\n",
        "               splitter=RandomSplitter(valid_pct=0.1, seed = 42)\n",
        "              )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gA3XMF_0NYcu"
      },
      "source": [
        "dls = db.dataloaders(source=LR_PATH_TRAIN, bs=12, num_workers=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOVPFlmPYved",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "outputId": "30ea0217-43c9-4e5f-c655-7eb9922f06b6"
      },
      "source": [
        "# Batch is ok, but sometimes it is hard to see the diffences\n",
        "dls.show_batch(max_n = 1, cmap='gray', figsize = (100,100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 7200x7200 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7ujWJQ1eQjG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e40e1e07-07ef-4268-afa0-ccd2bc1c7156"
      },
      "source": [
        "# Shows the values, got the right datasets\n",
        "dls.one_batch()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TensorImage([[[[0.3222, 0.3222, 0.3246,  ..., 0.2698, 0.2679, 0.2679],\n",
              "           [0.3222, 0.3222, 0.3246,  ..., 0.2698, 0.2679, 0.2679],\n",
              "           [0.3184, 0.3184, 0.3204,  ..., 0.2700, 0.2681, 0.2681],\n",
              "           ...,\n",
              "           [0.3295, 0.3295, 0.3286,  ..., 0.3415, 0.3417, 0.3417],\n",
              "           [0.3289, 0.3289, 0.3281,  ..., 0.3429, 0.3432, 0.3432],\n",
              "           [0.3289, 0.3289, 0.3281,  ..., 0.3429, 0.3432, 0.3432]]],\n",
              " \n",
              " \n",
              "         [[[0.3957, 0.3957, 0.3939,  ..., 0.3104, 0.3121, 0.3121],\n",
              "           [0.3957, 0.3957, 0.3939,  ..., 0.3104, 0.3121, 0.3121],\n",
              "           [0.3950, 0.3950, 0.3932,  ..., 0.3124, 0.3136, 0.3136],\n",
              "           ...,\n",
              "           [0.1888, 0.1888, 0.1893,  ..., 0.2171, 0.2173, 0.2173],\n",
              "           [0.1879, 0.1879, 0.1883,  ..., 0.2158, 0.2160, 0.2160],\n",
              "           [0.1879, 0.1879, 0.1883,  ..., 0.2158, 0.2160, 0.2160]]],\n",
              " \n",
              " \n",
              "         [[[0.5629, 0.5629, 0.5640,  ..., 0.6342, 0.6338, 0.6338],\n",
              "           [0.5629, 0.5629, 0.5640,  ..., 0.6342, 0.6338, 0.6338],\n",
              "           [0.5641, 0.5641, 0.5652,  ..., 0.6332, 0.6328, 0.6328],\n",
              "           ...,\n",
              "           [0.3121, 0.3121, 0.3133,  ..., 0.4208, 0.4197, 0.4197],\n",
              "           [0.3113, 0.3113, 0.3125,  ..., 0.4204, 0.4192, 0.4192],\n",
              "           [0.3113, 0.3113, 0.3125,  ..., 0.4204, 0.4192, 0.4192]]],\n",
              " \n",
              " \n",
              "         ...,\n",
              " \n",
              " \n",
              "         [[[0.5970, 0.5970, 0.5962,  ..., 0.5958, 0.5953, 0.5953],\n",
              "           [0.5970, 0.5970, 0.5962,  ..., 0.5958, 0.5953, 0.5953],\n",
              "           [0.5992, 0.5992, 0.5986,  ..., 0.5929, 0.5925, 0.5925],\n",
              "           ...,\n",
              "           [0.6986, 0.6986, 0.6978,  ..., 0.6915, 0.6894, 0.6894],\n",
              "           [0.6997, 0.6997, 0.6989,  ..., 0.6908, 0.6887, 0.6887],\n",
              "           [0.6997, 0.6997, 0.6989,  ..., 0.6908, 0.6887, 0.6887]]],\n",
              " \n",
              " \n",
              "         [[[0.6763, 0.6763, 0.6784,  ..., 0.5967, 0.5946, 0.5946],\n",
              "           [0.6763, 0.6763, 0.6784,  ..., 0.5967, 0.5946, 0.5946],\n",
              "           [0.6772, 0.6772, 0.6793,  ..., 0.5972, 0.5949, 0.5949],\n",
              "           ...,\n",
              "           [0.4917, 0.4917, 0.4917,  ..., 0.6507, 0.6544, 0.6544],\n",
              "           [0.4897, 0.4897, 0.4897,  ..., 0.6498, 0.6536, 0.6536],\n",
              "           [0.4897, 0.4897, 0.4897,  ..., 0.6498, 0.6536, 0.6536]]],\n",
              " \n",
              " \n",
              "         [[[0.2996, 0.2996, 0.3003,  ..., 0.4935, 0.4936, 0.4936],\n",
              "           [0.2996, 0.2996, 0.3003,  ..., 0.4935, 0.4936, 0.4936],\n",
              "           [0.2996, 0.2996, 0.3004,  ..., 0.4929, 0.4928, 0.4928],\n",
              "           ...,\n",
              "           [0.2847, 0.2847, 0.2861,  ..., 0.4380, 0.4395, 0.4395],\n",
              "           [0.2850, 0.2850, 0.2864,  ..., 0.4370, 0.4384, 0.4384],\n",
              "           [0.2850, 0.2850, 0.2864,  ..., 0.4370, 0.4384, 0.4384]]]], device='cuda:0'),\n",
              " TensorImage([[[[0.3252, 0.3280, 0.3297,  ..., 0.2695, 0.2674, 0.2652],\n",
              "           [0.3203, 0.3222, 0.3237,  ..., 0.2701, 0.2679, 0.2657],\n",
              "           [0.3165, 0.3185, 0.3199,  ..., 0.2704, 0.2682, 0.2661],\n",
              "           ...,\n",
              "           [0.3303, 0.3294, 0.3285,  ..., 0.3415, 0.3416, 0.3416],\n",
              "           [0.3299, 0.3289, 0.3280,  ..., 0.3432, 0.3432, 0.3430],\n",
              "           [0.3295, 0.3285, 0.3276,  ..., 0.3448, 0.3450, 0.3449]]],\n",
              " \n",
              " \n",
              "         [[[0.3974, 0.3956, 0.3941,  ..., 0.3089, 0.3108, 0.3126],\n",
              "           [0.3976, 0.3957, 0.3938,  ..., 0.3102, 0.3121, 0.3138],\n",
              "           [0.3972, 0.3952, 0.3933,  ..., 0.3114, 0.3134, 0.3151],\n",
              "           ...,\n",
              "           [0.1883, 0.1888, 0.1894,  ..., 0.2174, 0.2174, 0.2174],\n",
              "           [0.1875, 0.1879, 0.1882,  ..., 0.2159, 0.2160, 0.2161],\n",
              "           [0.1866, 0.1868, 0.1871,  ..., 0.2142, 0.2142, 0.2145]]],\n",
              " \n",
              " \n",
              "         [[[0.5608, 0.5616, 0.5626,  ..., 0.6348, 0.6348, 0.6351],\n",
              "           [0.5621, 0.5629, 0.5640,  ..., 0.6341, 0.6338, 0.6338],\n",
              "           [0.5634, 0.5641, 0.5653,  ..., 0.6332, 0.6329, 0.6325],\n",
              "           ...,\n",
              "           [0.3110, 0.3123, 0.3135,  ..., 0.4210, 0.4198, 0.4184],\n",
              "           [0.3100, 0.3113, 0.3122,  ..., 0.4205, 0.4192, 0.4180],\n",
              "           [0.3088, 0.3099, 0.3112,  ..., 0.4203, 0.4189, 0.4176]]],\n",
              " \n",
              " \n",
              "         ...,\n",
              " \n",
              " \n",
              "         [[[0.5957, 0.5944, 0.5946,  ..., 0.5983, 0.5976, 0.5970],\n",
              "           [0.5983, 0.5970, 0.5969,  ..., 0.5952, 0.5953, 0.5951],\n",
              "           [0.6005, 0.5995, 0.5992,  ..., 0.5924, 0.5926, 0.5928],\n",
              "           ...,\n",
              "           [0.6990, 0.6986, 0.6978,  ..., 0.6915, 0.6893, 0.6874],\n",
              "           [0.7003, 0.6997, 0.6990,  ..., 0.6909, 0.6887, 0.6867],\n",
              "           [0.7016, 0.7011, 0.7003,  ..., 0.6902, 0.6881, 0.6860]]],\n",
              " \n",
              " \n",
              "         [[[0.6734, 0.6755, 0.6777,  ..., 0.5969, 0.5947, 0.5920],\n",
              "           [0.6743, 0.6763, 0.6784,  ..., 0.5968, 0.5946, 0.5921],\n",
              "           [0.6750, 0.6770, 0.6792,  ..., 0.5972, 0.5950, 0.5931],\n",
              "           ...,\n",
              "           [0.4919, 0.4922, 0.4921,  ..., 0.6500, 0.6558, 0.6610],\n",
              "           [0.4898, 0.4897, 0.4902,  ..., 0.6495, 0.6536, 0.6602],\n",
              "           [0.4876, 0.4874, 0.4887,  ..., 0.6481, 0.6521, 0.6575]]],\n",
              " \n",
              " \n",
              "         [[[0.2986, 0.2996, 0.3005,  ..., 0.4937, 0.4941, 0.4946],\n",
              "           [0.2986, 0.2996, 0.3004,  ..., 0.4935, 0.4936, 0.4936],\n",
              "           [0.2986, 0.2995, 0.3003,  ..., 0.4933, 0.4931, 0.4928],\n",
              "           ...,\n",
              "           [0.2832, 0.2851, 0.2865,  ..., 0.4380, 0.4395, 0.4409],\n",
              "           [0.2829, 0.2850, 0.2866,  ..., 0.4370, 0.4384, 0.4398],\n",
              "           [0.2828, 0.2847, 0.2865,  ..., 0.4359, 0.4372, 0.4387]]]], device='cuda:0'))"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-wVwsidxnzJi"
      },
      "source": [
        "# Random"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHY9TpwBXfIk"
      },
      "source": [
        "# Taming the random\n",
        "\n",
        "import random\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "from fastai.vision import *\n",
        "\n",
        "\n",
        "def set_seeds():\n",
        "    random.seed(42)\n",
        "    np.random.seed(12345)\n",
        "    torch.manual_seed(1234)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "\n",
        "set_seeds()    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Byih6hDloDvT"
      },
      "source": [
        "# SRCNN Simple with ResBlock"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rlP-xIumYM0"
      },
      "source": [
        "from fastai import *\n",
        "from fastai.vision import *\n",
        "\n",
        "class ResBlock(Module):\n",
        "  def __init__(self, nf):\n",
        "    self.conv1 = ConvLayer(nf, nf)\n",
        "    self.conv2 = ConvLayer(nf, nf)\n",
        "  \n",
        "  def forward(self, x): return x + self.conv2(self.conv1(x))\n",
        "\n",
        "model = nn.Sequential(\n",
        "          nn.Conv2d(in_channels=1, out_channels=64, kernel_size=9, stride=1, padding=9//2),\n",
        "          ResBlock(64),\n",
        "          nn.Conv2d(in_channels=64, out_channels=32, kernel_size=1, stride=1, padding=0//2),\n",
        "          ResBlock(32),\n",
        "          nn.Conv2d(in_channels=32, out_channels=1, kernel_size=3, stride=1, padding=3//2),\n",
        "          ResBlock(1),\n",
        "          nn.ReLU()         \n",
        "        )\n",
        "\n",
        "\n",
        "  \n",
        "\n",
        "learn = Learner(dls, model, loss_func = nn.MSELoss(), metrics=mae)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WDuTDrEu6QBC"
      },
      "source": [
        "# a lots of epochs are needed for proper results \n",
        "epoch = 10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "g8AweIPIGP0B",
        "outputId": "30862077-9b74-4e5f-b9f9-ff3988c431be"
      },
      "source": [
        "# Just to get an idea, the parameters are not used any further\n",
        "# Have a look here for further parameter tuning https://docs.fast.ai/callback.schedule.html#Learner.fit_one_cycle\n",
        "lrs = learn.lr_find(suggest_funcs=(minimum, steep, valley, slide))"
      ],
      "execution_count": null,
      "outputs": [

        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qSNsG/ix6tDsCjJq9kd3HUsnMzuVv38Ty2P9iaFavOiue6cUjvZtdTSIAT956E92b1eavS3cTn5zGS0t3c/ZiJm+P6nA1YfRvG8i0ke3YcPA0T8zPvytMKVXxODKRNASO5nmdaCsryEPAd9cXikhXwBs4aNfoysDTw8IjvZoRffgsUQlnrpZvO3qOCxnZRXZrlVR+/b4eePNMp2f45UAK6Zk5DAq/sVurIDfVq8HixyLx9fZk9AebuHvWBj5en8ADPZuy+NHIfOcR87AI79zbkareHtw7ZxPLdyUx+faWNwzAD+8YzD+Gh7NmfzLDZq4nPrngixKUqqzWrFlT7q0RR3KJwXYRGQtEAG9cVx4EfAo8YIzJzee4iSISJSJRKSkp5ROszciIYGr6ejE7T6tkzf5k62W/LYp/ZVRx3NnsTl7t8SpB1YIQBB/qcOn4cJpV7cWKXUnU8vUisnmdEp2zSZ1qLHoskrrVq5BwKp1ZYzrxyuC2eHsW/CNR38+HaSPbcSrtMp2b1OKxW5rnu9993Rrzv4e7ce5iJkPeW883O46XKDallHtx5IjoMSDvmrTBtrJriMjtwIvALcaYy3nK/YDlwIvGmE35vYExZg4wB6xjJPYLvWi+3p6M696Ed3+OJz45jZvqVWfN/hQ6NamFXwGX/ZbFnc3uvDpgeDY9k35v/8KUJTv4LSWdIR0alOoekYY1q7L86ZvJyMqldrX85xe73m2t67Po0Uha1KuOh6Xg7rsezevy7VO9eHJBDE99to318adoWrcaFzNzuJSZTVaOoU+rALt3Ayqlyp8jE8lWoIWIhGBNIPcC9+XdQUQ6ArOBgcaY5Dzl3sBXwCfGmCUOjLFMxvVoyuxfD/Hh2kM8178lscfPM2VAK4e/b61q3vxtSBhPLogBKFG31vV8vT0pYI7KAhV0I+b1Av19WDixO/9csY+563+7Wu7jZUEQ5m1IIDTIj8f7NGdQeFChiUkp5boclkiMMdkiMglYifXy37nGmFgRmQpEGWOWYe3Kqg4stn0rPWKMGQLcA/QG6ojIBNspJxhjtjsq3tKoW70KIzoHszgqkcZ1rOMK9h4fKcig8EAGtg1k29GzRDYrWbdWefLysPDy4FCe7dcCD4vg4+mBxSJkZueydNsx/vPLQZ76bBtv/rCfh24OYVjHhgXeyKmUck0Ou/y3vJXn5b95/XYqndveXIOnRajl683m/+tbbl01mdm5XMrMwd/Xff/w5uQafog9waxfDrIzMZVq3h4M79SQsd2b0Dqw5FPFqIrH3S7/rV69OmlpaSQkJHDXXXexe/duZ4d0lTte/lsphNStxsC2gWTlmHLv7/f2tLh1EgHr1WB3hAfx9ZM9+eqJHgwMC2JRVCIDp69l1OyNrNmfTEX5sqPKR+o33xB3W1/2tgkl7ra+pH7zjbNDqvA0kdjBY7c0t/1BLP18PpWdiNCxcS3evKc9m//Sl/8b1JqjZy4y4eOt3DVjHd/uPE5OruHYuUss35nEP1bs5cF5W/nvhgTS9X4VZZP6zTck/fVlso8fB2PIPn6cpL++XKZk8sILLzBz5syrr1999VVee+01+vbtS6dOnQgPD+frr78u9Bw5OTlMmTKFLl260K5dO2bPng3AuHHjWLp06dX9xowZU+S5XJF2bdnJ2fRMahXzyidVPJnZuSzdbh1HOZSSTlUvDy7Z1ljx9rAQ6O/DkTMXqVHFk1FdGjG+R9N874FR7q0kXVtxt/W1JpHreDZoQIuffyrV+2/bto3Jkyfzyy+/ABAaGsrKlSvx9/fHz8+PU6dO0b17d+Li4hCRfLu25syZQ3JyMi+99BKXL1+mZ8+eLF68mCNHjvD222+zdOlSUlNT6dChA3FxcXh6Omb42lFdWzohkp1oErE/b08L90Q04u5OwayMPcG6+FO0ql+DDo1q0ibID29PCzFHzvLx+gTmbUhg7vrfuCeiEX8Z1Ab/qu7d5adKJzsp/9VBCyovjo4dO5KcnMzx48dJSUmhVq1aBAYG8uyzz/Lrr79isVg4duwYJ0+eJDAw/16JH374gZ07d7JkifUi1NTUVOLi4ujfvz9PPPEEKSkpfPHFF9x9990OSyKO5H4Rq0rHwyIMCg/K9zLnTo1r0alxLU4MasOHaw/x8YYEft6XzN+Hhd0wD5qq+DyDgvJvkQSV/hJ5gJEjR7JkyRJOnDjBqFGjmD9/PikpKURHR+Pl5UXTpk3JyCh4Vm1jDDNmzGDAgAE3bBs3bhz/+9//WLhwIR9//HGZ4nQWHSNRFUKgvw8v3RXK10/2pG71Kjz6aTRPzI8m+ULRU+ariqPes5MRn2unExIfH+o9O7lM5x01ahQLFy5kyZIljBw5ktTUVOrVq4eXlxerV6/m8OHDhR4/YMAAZs2aRVaWdYnuAwcOkJ6eDsCECROYPn06YO02c0faIlEVSlhDf76e1JM5vx7inZ/i2HzoDNPv7UCvFi4z56dyIP/BgwFIfns62UlJeAYFUe/ZyVfLS6tt27ZcuHCBhg0bEhQUxJgxYxg8eDDh4eFERETQunXrQo9/+OGHSUhIoFOnThhjCAgIuDrIXr9+fdq0acOwYcPKFKMz6WC7qrDiTl7gyQUxxCWn8dStN/HM7S317nk35G73kZTUxYsXCQ8PJyYmxuGLW+l9JEqVUIv6NVj6ZE/u7hTMuz/HM/bDzdrVpVzKqlWraNOmDU899ZSukKiUq/L19mTayPZ0C6nNX7/ezZAZ61n2VE/q1dC15ZXz3X777UWOr7gDbZGoSmFkRCOWPNaDc5cyeeaz7eTkVowuXaVcgSYSVWmENfTn70PD2HjoNO/8FOfscJSqMDSRqEplZEQjRnQOZsbPcayNK9/F0JSqqDSRqEpn6tC23BRQnckLt3PyvA6+K1VWmkhUpePr7cn7YzpxMTOHpz/bxuXsHGeHpNxMnz59uHK7waBBgzh37twN+7z66qtMmzatvENzCr1qS1VKLerX4PXhYTy3aAdDZqxn2sj2hAe77+WX6ncHNp9g49cHSTtzmeq1qxA5tDktuzluupwVK1Y47NzuQlskqtL6Q6dgPhofwblLmQx7fz1vrNynrRM3d2DzCVbP30famcsApJ25zOr5+ziw+USpz5mens6dd95J+/btCQsL4/PPP79me9OmTTl16hQAr7/+Oi1btuTmm29m//79V/c5ePAgAwcOpHPnzvTq1Yt9+/aVOh5XpIlEVWp929Tnh8m3MLxjQ2auPsjgGeuIPnzW2WGpUtr49UGyM3OvKcvOzGXj1wdLfc7vv/+eBg0asGPHDnbv3s3AgQPz3S86OpqFCxeyfft2VqxYwdatW69umzhxIjNmzCA6Oppp06bxxBNPlDoeV+TQRCIiA0Vkv4jEi8gL+Wx/TkT2iMhOEflJRJrk2TZeROJsj/GOjFNVbv6+Xkwb2Z6PH+jChYxs7p61gecX7+BU2mVnh6ZK6EpLpLjlxREeHs6PP/7In//8Z9auXVvgHehr165l+PDh+Pr64ufnx5AhQ6zvnZbGhg0bGDlyJB06dODRRx8lqQzT2rsih42RiIgHMBPoByQCW0VkmTFmT57dtgERxpiLIvI48G9glIjUBl4BIgADRNuO1a+KymFubVWPVc/dwrs/xzF33W+sjD3Bc/1aMjAskJPnL3MiNYOT5zMIrlWVvm3qOztclY/qtavkmzSq165S6nO2bNmSmJgYVqxYwUsvvUTfvn1LdHxubi41a9Zk+/btpY7B1TmyRdIViDfGHDLGZAILgaF5dzDGrDbGXLS93AQE254PAH40xpyxJY8fgfzbk0rZUbUqnvzljjZ890xvOjSqyd++2UPkP39m2Mz1PPa/aF5ZFstD/43isy1HnB2qykfk0OZ4el/7Z83T20Lk0OalPufx48fx9fVl7NixTJkyhZiYmHz36927N0uXLuXSpUtcuHCBb2zL+/r5+RESEsLixYsB69okO3bsKHU8rsiRV201BI7meZ0IdCtk/4eA7wo5tqFdo1OqEDfVq84nD3ZlzYEUjp29RKCfD4H+PgTUqMKfv9jJi1/tonY1b108y8VcuTrLnldt7dq1iylTpmCxWPDy8mLWrFk8//zzN+zXqVMnRo0aRfv27alXrx5dunS5um3+/Pk8/vjjvPbaa2RlZXHvvffSvn37Usfkahw2jbyIjAAGGmMetr2+H+hmjJmUz75jgUnALcaYyyLyPOBjjHnNtv2vwCVjzLTrjpsITARo3Lhx54ow+ZlyfRczs7nvg83sSTrPpw92pVuzOs4OqUKr6NPIlyd3nEb+GNAoz+tgW9k1ROR24EVgiDHmckmONcbMMcZEGGMiAgJ04SJVPny9Pfl4Qhca1arKw59EsTfpvLNDUsqpHJlItgItRCRERLyBe4FleXcQkY7AbKxJJDnPppVAfxGpJSK1gP62MqVcQq1q3nzyUDeqeXsybu4WVsaeoKIsEqdUSTkskRhjsrF2V60E9gKLjDGxIjJVRIbYdnsDqA4sFpHtIrLMduwZ4O9Yk9FWYKqtTCmX0bBmVT55qCv+Vb149NNoRn+wid3HUp0dllLlTpfaVaqMsnNy+WzLEd5eFcfZi5nc3SmYv94Zir+vl7NDqxD27t1L69atEdFlksvCGMO+ffvcboxEqUrB08PC/ZFNWf18Hyb2asbX248x7uMtpF3OdnZoFYKPjw+nT5/WrsMyMMZw+vRpfHwcszKotkiUsrMfYk/w+PwYujStxbwHuuLj5eHskNxaVlYWiYmJZGTolP9l4ePjQ3BwMF5e17aU7dEi0USilAMs3XaMZxdt59ZW9fjP2M54e2rjX7km7dpSykUN69iQ14eF8/O+ZJ5dpGvEq4pN1yNRykHu69aY9MvZvL5iL1nZufx7RDtq+no7Oyyl7E5bJEo50CO9m/HXu0L5eV8yg95Zy5bf9Cp2VfFoIlHKwR66OYQvHu+Bl6eFe+ds5J1VcdrVpSoUTSRKlYP2jWry7VM3M6R9A95edYBxczdzISPL2WEpZReaSJQqJzV8vHh7VAf+fXc7Nh86w71zNpFyQRfPUu5PE4lS5UhEuKdLIz4cH8GhlHRG/GcDh0+nOzsspcpEE4lSTtCnVT0WPNKN1EtZ3D1ro87RpdyaJhKlnKRj41oseSwSbw/h3jmb+OVAirNDUqpUNJEo5UQ31avBF0/0ILhWVR6ct5X5m3VxNuV+NJEo5WRB/lVZ8ngPereoy4tf7eb15XvI1cuDlRvRRKKUC6hexZMPxkUwPrIJH6z9jcf+F01GVo6zw1KqWDSRKOUiPD0s/G1oGK8MDuXHvSd59nOdo0u5B00kSrmYB3qG8OKgNny3+wSvL9/r7HCUKpJO2qiUC3q4VzOOnbvE3PW/0bBWVR66OcTZISlVIIe2SERkoIjsF5F4EXkhn+29RSRGRLJFZMR12/4tIrEisldE3hVdZ1NVMi/dGcqAtvV5bfkevtuV5OxwlCqQwxKJiHgAM4E7gFBgtIiEXrfbEWACsOC6Y3sAPYF2QBjQBbjFUbEq5Yo8LMI793akY6OaTP58O9GHdeZg5Zoc2SLpCsQbYw4ZYzKBhcDQvDsYYxKMMTuB3OuONYAP4A1UAbyAkw6MVSmX5OPlwYfjuxDk78PD/43it1M6nYpyPY5MJA2Bo3leJ9rKimSM2QisBpJsj5XGmBtGHUVkoohEiUhUSoreFawqptrVvJn3QFdEhAc+3sKZ9Exnh6TUNVzyqi0RuQloAwRjTT63iUiv6/czxswxxkQYYyICAgLKO0ylyk3TutX4YFwESakZPPzfrXqPiXIpjkwkx4BGeV4H28qKYziwyRiTZoxJA74DIu0cn1JupXOTWkwf1YFtR8/x7Ofb9e535TIcmUi2Ai1EJEREvIF7gWXFPPYIcIuIeIqIF9aBdr2gXlV6d4QHXb3H5JVlsXrDonIJDkskxphsYBKwEmsSWGSMiRWRqSIyBEBEuohIIjASmC0isbbDlwAHgV3ADmCHMeYbR8WqlDt56OYQHu3djE83Hebx/0VzMTPb2SGpSk6MqRjfaCIiIkxUVJSzw1Cq3Mxb/xtTv91D2wb+fDQ+gnp+Ps4OSbkhEYk2xkSU5RwuOdiulCrahJ4hfDg+goMpaT0Qeh0AABnoSURBVAybuZ69SeedHZKqpDSRKOXGbmtdn8WPRZJjDPfM3qj3mSin0ESilJtr28CfJY/1wNMiTPwkirTLOmaiypcmEqUqgEa1fXnvvk4cTEljyuIdVJSxT+UeNJEoVUH0vKkuf7mjDZe+W862nrewt00ocbf1JfUbveBROZZOI69UBTLyXCzddn6BV5Z1GpXs48dJ+uvLAPgPHuzM0FQFVqwWiYhUExGL7XlLERliu1FQKeVCUqZPv5pErjAZGSS/Pd1JEanKoLhdW78CPiLSEPgBuB+Y56iglFKlk52U/7olBZUrZQ/FTSRijLkI/AF43xgzEmjruLCUUqXhGRRUonKl7KHYiUREIoExwHJbmYdjQlJKlVa9ZycjPtfe4Z7h4cXhYeOcFJGqDIqbSCYDfwG+ss2X1QzreiFKKRfiP3gwQX+fimeDBiCCZ1AQy/tN4KmU+hw4ecHZ4akKqsRzbdkG3asbY1xqPgada0up/CWfz2DQu+vwq+rJskk3U72KXqypflduc22JyAIR8RORasBuYI+ITCnLGyulykc9Px9mjO5Iwql0nl+kNysq+ytu11aorQUyDOsiUyFYr9xSSrmByOZ1+L9Bbfg+9gQzV8c7OxxVwRQ3kXjZ7hsZBiwzxmQB+rVGKTfy0M0hDOvQgDd/PMBPe086OxxVgRQ3kcwGEoBqwK8i0gRwqTESpVThRIR//qEdoUF+TF64nYMpac4OSVUQxUokxph3jTENjTGDjNVh4FYHx6aUsrOq3h7Mvr8zXp4WHvkkivMZWc4OSVUAxR1s9xeRt0QkyvZ4E2vrRCnlZoJr+TLzvk4cPn2RPy3eqYPvqsyK27U1F7gA3GN7nAc+LuogERkoIvtFJF5EXshne28RiRGRbBEZcd22xiLyg4jsFZE9ItK0mLEqpYoQ2bwOfxrQiu9jT/DZlqPODke5ueImkubGmFeMMYdsj78BzQo7QEQ8gJnAHUAoMFpEQq/b7QgwAViQzyk+Ad4wxrQBugLJxYxVKVUMj/RqRq8WdZn6bSzxyXqzoiq94iaSSyJy85UXItITuFTEMV2BeFviyQQWAkPz7mCMSTDG7ARy85bbEo6nMeZH235ptrm+lFJ2YrEIb45sj6+3J099tp2MrBxnh6TcVHETyWPATBFJEJEE4D3g0SKOaQjkbTMn2sqKoyVwTkS+FJFtIvKGrYVzDRGZeGXcJiUlpZinVkpdUc/PhzdGtGNv0nn+/f1+Z4ej3FRxr9raYYxpD7QD2hljOgK3OTAuT6AX8DzQBWs32oR84ppjjIkwxkQEBAQ4MBylKq6+beozoUdT5q7/jdX7tQdZlVyJlto1xpzPM8fWc0XsfgxolOd1sK2sOBKB7bZusWxgKdCpJLEqpYrvhTta0zqwBs8v2sHJ8xnODke5mbKs2S5FbN8KtBCREBHxBu4FlhXz3FuBmiJypZlxG7CndGEqpYri4+XBe/d15FJWDk8t2EZ2Tm7RByllU5ZEUujF57aWxCRgJbAXWGSbgn6qiAwBEJEuIpIIjARmi0is7dgcrN1aP4nILqxJ64MyxKqUKsJN9Wrw+vAwtiSc4a0fDzg7HOVGCp1PWkQukH/CEKBqUSc3xqwAVlxX9nKe51uxdnnld+yPWMdklFLlZHjHYDYfOsP7aw7SJaQ2t7aq5+yQlBsotEVijKlhjPHL51HDGKOLGihVAb06pC2tA2vw3OfbOX6uqKv8lSpb15ZSqgLy8fLg/TGdyMzO5anPtpGl4yWqCJpIlFI3aBZQnX/e3Y7ow2d5Y6XeX6IKp4lEKZWvIe0bMLZ7Y+b8eohVe3T9ElUwTSRKqQK9dGcobRv48cfFO0g8q7MUqfxpIlFKFejKeEluruHJBdvIzNbxEnUjTSRKqUI1qVONf49ox46j5/jXd/ucHY5yQZpIlFJFuiM86Op8XCt2JTk7HOViNJEopYrlL4Na07FxTZ5fvIMDJ3X9EvU7TSRKqWKp4unBf8Z2ploVTyZ+EkXqJV3vXVlpIlFKFVt9Px9mjelE4tlLTF64jdxcXe9daSJRSpVQRNPavDKkLav3p/D2Kp3cURUxaaNSSuVnbLfG7Eo8x4yf42keUJ1hHYu7+KmqiLRFopQqMRFh6tAwuobUZvLn25nxUxzGaDdXZaWJRClVKj5eHnzyYFeGd2zImz8e4NnPt5ORlePssJQTaNeWUqrUfLw8eOue9jQPqMa0Hw5w9OwlZt/fmbrVqzg7NFWOtEWilCoTEWHSbS14f0wnYo+nMmLWBl33vZLRRKKUsotB4UHMf7gbKRcuc98Hm0i5cNnZIaly4tBEIiIDRWS/iMSLyAv5bO8tIjEiki0iI/LZ7iciiSLyniPjVErZR+cmtfn4ga4cP5fBmA83cTpNk0ll4LBEIiIewEzgDiAUGC0iodftdgSYACwo4DR/B351VIxKKfvrGlKbj8ZHcPj0RcZ+tIVzFzOdHZJyMEe2SLoC8caYQ8aYTGAhMDTvDsaYBGPMTuCGualFpDNQH/jBgTEqpRygx011+WBcBAeT0xg3dwtpl7OdHZJyIEcmkobA0TyvE21lRRIRC/Am8HwR+00UkSgRiUpJSSl1oEop++vdMoBZYzsRe/w8T86P0bXfKzBXHWx/AlhhjEksbCdjzBxjTIQxJiIgIKCcQlNKFVffNvX5x/AwfjmQwotf7dKbFisoR95HcgxolOd1sK2sOCKBXiLyBFAd8BaRNGPMDQP2SinXNqpLY46dy+Ddn+JoULMqk29v6eyQlJ05MpFsBVqISAjWBHIvcF9xDjTGjLnyXEQmABGaRJRyX8/e3oLj5y4xfVUcDfyrck+XRkUfpNyGw7q2jDHZwCRgJbAXWGSMiRWRqSIyBEBEuohIIjASmC0isY6KRynlPCLCP/8QTq8WdfnLV7tYvT/Z2SEpO5KK0mcZERFhoqKinB2GUqoQaZezGTV7I4dS0vn80e60C67p7JAqPRGJNsZElOUcrjrYrpSqgKpX8eTjB7pQp7o3D87byuHT6c4OSdmBJhKlVLmqV8OH/z7Ylexcw/i5W/Tu9wpAE4lSqtw1D6jOR+O7kJSawYPztnIxU29YdGeaSJRSTtG5SS1mjO7IrmOpPPppNJezdS0Td6WJRCnlNP3bBvKvu9uxNu4UTy3Ypne/uylNJEopp7onohGvDg7lhz0nmbJ4B7m5FeNK0spEV0hUSjndhJ4hpGfm8MbK/fhW8eT1YWGIiLPDUsWkiUQp5RKevPUm0i9n8/6ag1T18uClO9toMnETmkiUUi5jyoBWXMzM4aN1v+HpIbwwsLUmEzegiUQp5TJEhFcGh5Kdm8vsXw7haRGe799Kk4mL00SilHIpIsLUIWFk5xhmrj6Ip8XCs/10xmBXpolEKeVyLBbhH8PDyc41vPNTHB4W4em+LZwdliqAJhKllEuyWIT/d3c7cnMNb/14AGPgmds1mbgiTSRKKZflYRHeGNkeBN5edYBcY5h8ewsdM3ExmkiUUi7NwyK8MaI9FhHe+SmOXGN4rl9LTSYuRBOJUsrleViEf9/dDg8RZvwcT06uYcoAvZrLVWgiUUq5BYvFusqixSK8v+Yg6ZezeWVwWywWTSbOpolEKeU2LBbh9WFhVK/iwQdrf+PcpSwebRLI1m8OkXbmMtVrVyFyaHNadgt0dqiVikMnbRSRgSKyX0TiReSFfLb3FpEYEckWkRF5yjuIyEYRiRWRnSIyypFxKqXch8Ui/N+gNvxpYCv2bz7Jqk/3knbGujhW2pnLrJ6/jwObTzg5ysrFYYlERDyAmcAdQCgwWkRCr9vtCDABWHBd+UVgnDGmLTAQmC4iurizUgqw3rT4RJ+bGGrxxeO6meezM3PZ+PVB5wRWSTmya6srEG+MOQQgIguBocCeKzsYYxJs2675UTDGHMjz/LiIJAMBwDkHxquUcjO56fmvrHilhaLKhyO7thoCR/O8TrSVlYiIdAW8gRu+YojIRBGJEpGolJSUUgeqlHJP1WtXKVG5cgyXXthKRIKAT4EHjDE3LJ1mjJljjIkwxkQEBASUf4BKKaeKHNocT+9r/4xlC7Tq18hJEVVOjkwkx4C8n2awraxYRMQPWA68aIzZZOfYlFIVQMtugdw6pvXVFoi3nxdramTzx83xJJxKd3J0lYcjx0i2Ai1EJARrArkXuK84B4qIN/AV8IkxZonjQlRKubuW3QKvudw38lgq4+ZuYcR/NjDvga6ENfR3YnSVg8NaJMaYbGASsBLYCywyxsSKyFQRGQIgIl1EJBEYCcwWkVjb4fcAvYEJIrLd9ujgqFiVUhVHWEN/Fj8WSRVPD0bN3sjaOB0/dTQxxjg7BruIiIgwUVFRzg5DKeUiTp7PYPzcLcQnpzFtZHuGdSzxtT6VgohEG2MiynIOlx5sV0qp0qrv58OixyKJaFqLyZ9v54NfDzk7pApLE4lSqsLy8/Fi3gNdGRQeyOsr9vLPFXupKL0wrkTn2lJKVWg+Xh7MGN2JOtVimf3rIU6nZ/KvP4Tj6aHfo+1FE4lSqsLzsAhTh7alTnVvpq+K42x6Ju/d14mq3h7ODq1C0JSslKoURITJt7fktWFh/Lw/mbEfbeZMeqazw6oQNJEopSqVsd2bMPO+Tuw6lsrw99dzKCXN2SG5PU0kSqlKZ1B4EJ890p20jGyGv7+BTYdOOzskt6aJRClVKXVuUouvnuhJQI0q3P/RZpZEJzo7JLeliUQpVWk1ruPLF4/3oGtIbZ5fvIO3fzyglweXgiYSpVSl5l/Veq/JyM7BvPNTHC98sYvsnBsmG1eF0Mt/lVKVnpeHhX+PaEeQvw/v/hxP8oUMZo7phK+3/oksDm2RKKUU1suDn+vfiteHh/HLgRRGz9nEqTRdabE4NJEopVQeY7o1Yfb9Eew/eYHh768nPvmCs0NyeZpIlFLqOv1C67NwYiSXMnMZ/v4G1sefcnZILk0TiVJK5aNDo5osfbIHQf4+jJ+7hYVbjjg7JJeliUQppQoQXMuXJY/3oMdNdXnhy13887u95OTq5cHX00SilFKF8PPxYu74CMZ2b8zsXw4x8ZMoLmRkOTssl6KJRCmliuDpYeG1YeH8fWhb1hxI4e5ZGzhy+qKzw3IZDk0kIjJQRPaLSLyIvJDP9t4iEiMi2SIy4rpt40UkzvYY78g4lVKqOO6PbMonD3bl5PnLDJ25TufosnFYIhERD2AmcAcQCowWkdDrdjsCTAAWXHdsbeAVoBvQFXhFRGo5KlallCqunjfVZemTPalVzZuxH27mvxsSKv20Ko5skXQF4o0xh4wxmcBCYGjeHYwxCcaYncD18xEMAH40xpwxxpwFfgQGOjBWpZQqtpC61Vj6ZE/6tArglWWx/HHxDjKycpwdltM4MpE0BI7meZ1oK7PbsSIyUUSiRCQqJSWl1IEqpVRJ+fl4Mef+CCbf3oIvY44x4j8bOHbukrPDcgq3Hmw3xswxxkQYYyICAgKcHY5SqpKxWKyrLn4wLoLDpy4yeMa6SnnzoiMTyTGgUZ7XwbYyRx+rlFLlql9ofZZO6kmdat7c/9FmZq6OJ7cS3W/iyESyFWghIiEi4g3cCywr5rErgf4iUss2yN7fVqaUUi6peUB1lj7Zk0HhQbyxcj8TP40m9VLluN/EYYnEGJMNTMKaAPYCi4wxsSIyVUSGAIhIFxFJBEYCs0Uk1nbsGeDvWJPRVmCqrUwppVxWtSqezBjdkZfvCmXN/mSGvreO3cdSnR2Ww0lFuWwtIiLCREVFOTsMpZQCYGvCGSYtiOFMeiZ/GtCah24OwWIRZ4d1AxGJNsZElOUcbj3YrpRSrqpL09p890xv+rSqx+sr9jL+4y2cPJ9h1/fIyTV8vzuJRVFHi97ZgTSRKKWUg9Su5s2c+zvzj+HhbE04w8Dpv7J027EyD8Sfz8jiw7WHuOWN1Tz2vxjmbzrs1JsitWtLKaXKQXxyGs8t2s7OxFRa1a/Bc/1b0j+0PiK27q6di+CnqZCaCP7B0PdlaHfP1eNzcg1bE86wfGcSX8Ykkp6ZQ9emtXnw5hD6hdbHo5TdZvbo2tJEopRS5SQ31/DtriSm/3iAQ6fSaRfsz0M3h9A9/SfqrfkTkvX7DY3Gqyrn+k5je63+/BB7kh/3nOBUWiZVPC3cERbIQzc3IzzYv8wxaSLJQxOJUspdZOfk8uW2Y7yzKo5j5y6xzvtpgi033siYmFuXmzPfpZq3B7e2rscdYUH0aRVAtSqedovFHonEftEopZQqFk8PC/dENGJ4x4bsTTpPww/zn0W4oeU0Cx7uRqcmtfDx8ijnKItPB9uVUspJvDwstAuuifgH57td/IPpcVNdl04ioIlEKaWcr+/L4FX12jKvqtZyN6CJRCmlnK3dPTD4XfBvBIj138HvXnPVlivTMRKllHIF7e5xm8RxPW2RKKWUKhNNJEoppcpEE4lSSqky0USilFKqTDSRKKWUKpMKM0WKiKQAh/MU+QOp+TzP+zpveV2gLIstX/8eJdmnuOUF1amg52WpU3HqU9h++ZUXVVbU8/L4jArbrzh1Kuln5syfu4K2aZ1c6+9DQdvsVacmxpiAImIrnDGmQj6AOfk9z/v6un2i7PV+Jd2nuOUF1amQ56WuU3HqU9I6FVVW1PPy+IzKWqeSfmbO/LnTOhVeJ1f5++Cqdcr7qMhdW98U8Dzv6+vL7fV+Jd2nuOUF1amwupZWcc9TkjoVVVYR6lSaz6wsyvJzV9A2rVPx4yiuilinqypM11ZZiUiUKeMMmK6motWpotUHtE7uQutUuIrcIimpOc4OwAEqWp0qWn1A6+QutE6F0BaJUkqpMtEWiVJKqTLRRKKUUqpMNJEopZQqE00kRRCRXiLyHxH5UEQ2ODseexARi4i8LiIzRGS8s+OxBxHpIyJrbZ9VH2fHYy8iUk1EokTkLmfHYg8i0sb2GS0RkcedHY89iMgwEflARD4Xkf7OjqesRKSZiHwkIkuKe0yFTiQiMldEkkVk93XlA0Vkv4jEi8gLhZ3DGLPWGPMY8C3wX0fGWxz2qBMwFAgGsoBER8VaXHaqkwHSAB8qTp0A/gwsckyUJWOn36e9tt+ne4Cejoy3OOxUp6XGmEeAx4BRjoy3KHaqzyFjzEMlet+KfNWWiPTG+sflE2NMmK3MAzgA9MP6B2crMBrwAP553SkeNMYk245bBDxkjLlQTuHnyx51sj3OGmNmi8gSY8yI8oo/P3aq0yljTK6I1AfeMsaMKa/482OnOrUH6mBNjqeMMd+WT/T5s9fvk4gMAR4HPjXGLCiv+PNj578RbwLzjTEx5RT+Dexcn2L/bajQKyQaY34VkabXFXcF4o0xhwBEZCEw1BjzTyDf7gMRaQykOjuJgH3qJCKJQKbtZY7joi0ee31ONmeBKo6IsyTs9Dn1AaoBocAlEVlhjMl1ZNyFsdfnZIxZBiwTkeWAUxOJnT4nAf4FfOfMJAJ2/10qtgqdSArQEDia53Ui0K2IYx4CPnZYRGVX0jp9CcwQkV7Ar44MrAxKVCcR+QMwAKgJvOfY0EqtRHUyxrwIICITsLW4HBpd6ZT0c+oD/AFrsl/h0MhKr6S/T08BtwP+InKTMeY/jgyuFEr6GdUBXgc6ishfbAmnUJUxkZSYMeYVZ8dgT8aYi1iTY4VhjPkSa4KscIwx85wdg70YY9YAa5wchl0ZY94F3nV2HPZijDmNdbyn2Cr0YHsBjgGN8rwOtpW5M62Te9A6uYeKVieH16cyJpKtQAsRCRERb+BeYJmTYyorrZN70Dq5h4pWJ8fXx17z0bviA/gMSOL3y1wfspUPwnoVw0HgRWfHqXXSOrnDQ+vk+g9n1adCX/6rlFLK8Spj15ZSSik70kSilFKqTDSRKKWUKhNNJEoppcpEE4lSSqky0USilFKqTDSRqApNRNLK+f3ssmaNWNdXSRWR7SKyT0SmFeOYYSISao/3V6okNJEoVQIiUuj8dMaYHnZ8u7XGmA5AR+AuESlq/Y5hWGcKVqpcaSJRlY6INBeR70UkWqyrKra2lQ8Wkc0isk1EVtnWNkFEXhWRT0VkPfCp7fVcEVkjIodE5Ok8506z/dvHtn2JrUUx3zbdOCIyyFYWLSLvikih64wYYy4B27HO4oqIPCIiW0Vkh4h8ISK+ItIDGAK8YWvFNC+onkrZmyYSVRnNAZ4yxnQGngfet5WvA7obYzoCC4E/5TkmFLjdGDPa9ro11mnruwKviIhXPu/TEZhsO7YZ0FNEfIDZwB229w8oKlgRqQW04Pcp/780xnQxxrQH9mKdBmMD1vmTphhjOhhjDhZST6XsSqeRV5WKiFQHegCLbQ0E+H0hrGDgcxEJAryB3/IcuszWMrhiuTHmMnBZRJKB+ty4xO8WY0yi7X23A02xrl53yBhz5dyfARMLCLeXiOzAmkSmG2NO2MrDROQ1rGuvVAdWlrCeStmVJhJV2ViAc7axh+vNwLpM7zLbAkyv5tmWft2+l/M8zyH/36Xi7FOYtcaYu0QkBNgkIouMMduBecAwY8wO26JXffI5trB6KmVX2rWlKhVjzHngNxEZCdZlUkWkvW2zP7+v0zDeQSHsB5rlWQ51VFEH2Fov/wL+bCuqASTZutPyrk1/wbatqHoqZVeaSFRF5ysiiXkez2H94/uQrdsoFhhq2/dVrF1B0cApRwRj6x57Avje9j4XgNRiHPofoLctAf0V2AysB/bl2WchMMV2sUBzCq6nUnal08grVc5EpLoxJs12FddMIM4Y87az41KqtLRFolT5e8Q2+B6LtTtttpPjUapMtEWilFKqTLRFopRSqkw0kSillCoTTSRKKaXKRBOJUkqpMtFEopRSqkw0kSillCqT/w/t70MiPFBTWAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pypwGHzVYVf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "46900ba3-b8d1-4d99-b10e-3b0afe1dd219"
      },
      "source": [
        "# load - adapt path and name\n",
        "#learn.load(\"/content/gdrive/MyDrive/fastai/filename.pth\")\n",
        "learn.fit_one_cycle(epoch, cbs=[ShowGraphCallback(), SaveModelCallback])\n",
        "# save - adapt path and name\n",
        "#learn.save(\"/content/gdrive/MyDrive/fastai/filename.pth\") "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>mae</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.147358</td>\n",
              "      <td>0.212433</td>\n",
              "      <td>0.421332</td>\n",
              "      <td>00:12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.100590</td>\n",
              "      <td>0.008125</td>\n",
              "      <td>0.063184</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.070769</td>\n",
              "      <td>0.009910</td>\n",
              "      <td>0.054042</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.054121</td>\n",
              "      <td>0.012788</td>\n",
              "      <td>0.057022</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.043709</td>\n",
              "      <td>0.014626</td>\n",
              "      <td>0.061163</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.037154</td>\n",
              "      <td>0.018592</td>\n",
              "      <td>0.066019</td>\n",
              "      <td>00:05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.031799</td>\n",
              "      <td>0.019479</td>\n",
              "      <td>0.061377</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.027582</td>\n",
              "      <td>0.018604</td>\n",
              "      <td>0.059152</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.024409</td>\n",
              "      <td>0.016423</td>\n",
              "      <td>0.066500</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.021888</td>\n",
              "      <td>0.014140</td>\n",
              "      <td>0.055954</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Better model found at epoch 0 with valid_loss value: 0.2124331295490265.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Better model found at epoch 1 with valid_loss value: 0.008125454187393188.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RYVfaGsuGo4H"
      },
      "source": [
        "# Stats with independent test images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "id": "cA6UP2O-IiRj",
        "outputId": "248c8700-5c56-47ec-a0d5-1c0797137510"
      },
      "source": [
        "import os\n",
        "import sys\n",
        "import numpy as np\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from skimage.metrics import peak_signal_noise_ratio\n",
        "from skimage.metrics import structural_similarity\n",
        "import rasterio\n",
        "from rasterio.plot import reshape_as_image, reshape_as_raster, show\n",
        "from rasterio import Affine\n",
        "from rasterio.enums import Resampling\n",
        "from numpy import savetxt\n",
        "\n",
        "# adapt this parameter to the maximum height of your training dataset for PSNR processing\n",
        "max_height = 3150\n",
        "\n",
        "# adapt paths\n",
        "path_lr = '/content/gdrive/MyDrive/fastai/data_test_100/lr_us_cc'\n",
        "path_hr = '/content/gdrive/MyDrive/fastai/data_test_100/hr'\n",
        "\n",
        "meanAbsoluteError = []\n",
        "meanAbsoluteErrorCubic = []\n",
        "\n",
        "countMAE = 0\n",
        "countRMSE = 0\n",
        "countPSNR = 0\n",
        "countSSI = 0\n",
        "countMAESlice = 0\n",
        "countRMSESlice = 0\n",
        "countPSNRSlice = 0\n",
        "countSSISlice = 0\n",
        "totalMAE_lr = 0\n",
        "totalMAE_sr = 0\n",
        "totalRMSE_lr = 0\n",
        "totalRMSE_sr = 0\n",
        "totalMAX_lr = 0\n",
        "totalMAX_sr = 0\n",
        "fileCounter = 0 \n",
        "\n",
        "# read low resolution images\n",
        "for file in os.listdir(path_lr):  \n",
        "    current = os.path.join(path_lr, file)\n",
        "    fileCounter += 1;\n",
        "    x, y = [], []\n",
        "    with rasterio.open(current) as src:\n",
        "        arrayLR = src.read(\n",
        "            out_shape=(\n",
        "                src.count,\n",
        "                int(src.height),\n",
        "                int(src.width)\n",
        "            )\n",
        "        )        \n",
        "    max_val_lr = np.amax(arrayLR)\n",
        "    min_val_lr = np.amin(arrayLR)\n",
        "    arrayLR = reshape_as_image(arrayLR)\n",
        "    nsamples, nx, ny = arrayLR.shape\n",
        "    arrayLR = arrayLR.reshape((nsamples,nx*ny))\n",
        "   \n",
        "    f = rasterio.open(current)\n",
        "    d1 = f.read(1)\n",
        "    tensorImage = TensorImage([d1])\n",
        "    tensorImage = (tensorImage - min_val_lr) / (max_val_lr - min_val_lr)\n",
        "    arraySR = learn.predict(tensorImage)\n",
        "    arraySR = arraySR[0].numpy()\n",
        "\n",
        "    # recalculate real world values for sr image\n",
        "    arraySR = arraySR * (max_val_lr - min_val_lr) + min_val_lr\n",
        "    arraySR = np.squeeze(arraySR, axis=0)\n",
        "   \n",
        "    # maximal value of sr image    \n",
        "    max_val_sr = np.amax(arraySR)\n",
        "\n",
        "    # load hr image to compare images    \n",
        "    currentHr = os.path.join(path_hr, file)\n",
        "    with rasterio.open(currentHr) as src:\n",
        "        arrayHR = src.read(\n",
        "            out_shape=(\n",
        "                src.count,\n",
        "                int(src.height),\n",
        "                int(src.width)\n",
        "            )\n",
        "        )\n",
        " \n",
        "    arrayHR = reshape_as_image(arrayHR)\n",
        "    nsamples, nx, ny = arrayHR.shape\n",
        "    arrayHR = arrayHR.reshape((nsamples,nx*ny))\n",
        "\n",
        "    # slice 15 pixels from border because border effects\n",
        "    slice_val = 15\n",
        "    HrSlice = arrayHR[slice_val:-slice_val, slice_val:-slice_val]\n",
        "    SrSlice = arraySR[slice_val:-slice_val, slice_val:-slice_val]\n",
        "    LrSlice = arrayLR[slice_val:-slice_val, slice_val:-slice_val]\n",
        "    \n",
        "    # calculate the metrics for sr image\n",
        "    MAE_sr = mean_absolute_error(arrayHR, arraySR)\n",
        "    RMSE_sr = mean_squared_error(arrayHR, arraySR, squared=False)\n",
        "    SSI_sr = structural_similarity(arrayHR, arraySR)\n",
        "    MAE_sr_slice = mean_absolute_error(HrSlice, SrSlice)\n",
        "    RMSE_sr_slice = mean_squared_error(HrSlice, SrSlice, squared=False)\n",
        "    SSI_sr_slice = structural_similarity(HrSlice, SrSlice)\n",
        "    MAX_sr = np.amax(abs(arrayHR - arraySR))\n",
        "    MAX_sr_slice = np.amax(abs(HrSlice - SrSlice))\n",
        "    # adapt data_range to the maximal overall value  of the dataset\n",
        "    PSNR_sr = peak_signal_noise_ratio(arrayHR, arraySR, data_range=max_height)\n",
        "    PSNR_sr_slice = peak_signal_noise_ratio(HrSlice, SrSlice, data_range=max_height)\n",
        "    \n",
        "    # calculate the metrics for lr image\n",
        "    MAE_lr = mean_absolute_error(arrayHR, arrayLR)\n",
        "    RMSE_lr = mean_squared_error(arrayHR, arrayLR, squared=False)\n",
        "    SSI_lr = structural_similarity(arrayHR, arrayLR)\n",
        "    MAE_lr_slice = mean_absolute_error(HrSlice, LrSlice)\n",
        "    RMSE_lr_slice = mean_squared_error(HrSlice, LrSlice, squared=False)\n",
        "    SSI_lr_slice = structural_similarity(HrSlice, LrSlice)\n",
        "    MAX_lr = np.amax(abs(arrayHR - arrayLR))\n",
        "    MAX_lr_slice = np.amax(abs(HrSlice - LrSlice))\n",
        "    # adapt data_range to the maximal overall value  of the dataset\n",
        "    PSNR_lr = peak_signal_noise_ratio(arrayHR, arrayLR, data_range=max_height)\n",
        "    PSNR_lr_slice = peak_signal_noise_ratio(HrSlice, LrSlice, data_range=max_height)\n",
        "    \n",
        "    # MAE count improved images\n",
        "    if MAE_sr < MAE_lr:\n",
        "        countMAE += 1\n",
        "    if MAE_sr_slice < MAE_lr_slice:\n",
        "        countMAESlice += 1    \n",
        "    # RMSE count improved images\n",
        "    if RMSE_sr < RMSE_lr:\n",
        "        countRMSE += 1\n",
        "    if RMSE_sr_slice < RMSE_lr_slice:\n",
        "        countRMSESlice += 1 \n",
        "    # PSNR count imporved images, higher values are better\n",
        "    if PSNR_sr > PSNR_lr:\n",
        "        countPSNR += 1\n",
        "    if PSNR_sr_slice > PSNR_lr_slice:\n",
        "        countPSNRSlice += 1    \n",
        "    # SSI count imporved images, higher values are better\n",
        "    if SSI_sr > SSI_lr:\n",
        "        countSSI += 1\n",
        "    if SSI_sr_slice > SSI_lr_slice:\n",
        "        countSSISlice += 1\n",
        "\n",
        "    totalMAE_lr = totalMAE_lr + MAE_lr_slice\n",
        "    totalMAE_sr = totalMAE_sr + MAE_sr_slice\n",
        "    totalRMSE_lr = totalRMSE_lr + RMSE_lr_slice\n",
        "    totalRMSE_sr = totalRMSE_sr + RMSE_sr_slice        \n",
        "    totalMAX_lr = totalMAX_lr + MAX_lr_slice\n",
        "    totalMAX_sr = totalMAX_sr + MAX_sr_slice \n",
        "\n",
        "    # print metrics for every tile if desired\n",
        "    #print(file ,' SR MaxError: ', MAX_sr_slice  ,' LR MaxError: ', MAX_lr_slice, ' Difference MaxError LR - SR: ', MAX_lr_slice - MAX_sr_slice)\n",
        "    #print(file ,' SR MAE: ', MAE_sr_slice ,' LR MAE: ', MAE_lr_slice, ' Difference LR - SR: ', MAE_lr_slice - MAE_sr_slice)\n",
        "    #print(file ,' SR RMSE: ', RMSE_sr_slice ,' LR RMSE: ', RMSE_lr_slice, ' Difference LR - SR: ', RMSE_lr_slice - RMSE_sr_slice)\n",
        "    #print(file ,' SR PSNR: ', PSNR_sr_slice ,' LR PSNR: ', PSNR_lr_slice, ' Difference LR - SR: ', PSNR_lr_slice - PSNR_sr_slice)\n",
        "\n",
        "print('Mean MAE LR', totalMAE_lr/fileCounter);\n",
        "print('Mean MAE SR', totalMAE_sr/fileCounter);\n",
        "print('Difference LR - SR MAE ', totalMAE_lr/fileCounter - totalMAE_sr/fileCounter)\n",
        "print('Mean RMSE LR', totalRMSE_lr/fileCounter);\n",
        "print('Mean RMSE SR', totalRMSE_sr/fileCounter);\n",
        "print('Difference SR - LR RMSE ', totalRMSE_lr/fileCounter - totalRMSE_sr/fileCounter)\n",
        "print('MAX LR', totalMAX_lr/fileCounter);\n",
        "print('MAX SR', totalMAX_sr/fileCounter);\n",
        "print('Difference LR - SR MAX ', totalMAX_lr/fileCounter - totalMAX_sr/fileCounter)"
      ],
      "execution_count": null,
      "outputs": [


        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean MAE LR 2.63192657828331\n",
            "Mean MAE SR 69.54290273666382\n",
            "Difference LR - SR MAE  -66.9109761583805\n",
            "Mean RMSE LR 3.3373901045322416\n",
            "Mean RMSE SR 88.37143955230712\n",
            "Difference SR - LR RMSE  -85.03404944777488\n",
            "MAX LR 29.542468872070312\n",
            "MAX SR 313.6396166992188\n",
            "Difference LR - SR MAX  -284.0971478271485\n"
          ]
        }
      ]
    }
  ]
}