Fastai4DEM_EPSC.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Fastai4DEM-EPSC.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "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 - EPSCN\n",
        "=========================\n",
        "EPSCN: Real-Time Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolution Neural\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.\n",
        "\n",
        "Paper: https://arxiv.org/abs/1609.05158\n"
      ]
    },
    {
      "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",
        "\n",
        "**Upsampling is done by the algorithm here.**\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\""
      ],
      "execution_count": 6,
      "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\", \"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": 7,
      "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": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gA3XMF_0NYcu"
      },
      "source": [
        "dls = db.dataloaders(source=LR_PATH_TRAIN, bs=12, num_workers=2)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOVPFlmPYved",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "outputId": "ca3d8160-5678-47df-cce9-9e52f3f0b7ef"
      },
      "source": [
        "dls.show_batch(max_n = 1, cmap='gray', figsize = (100,100))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "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": "4ebb80c5-5233-43b0-e626-e9fae5d33999"
      },
      "source": [
        "# Shows the values\n",
        "dls.one_batch()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TensorImage([[[[0.3222, 0.3294, 0.3343,  ..., 0.2781, 0.2735, 0.2679],\n",
              "           [0.3107, 0.3149, 0.3223,  ..., 0.2784, 0.2743, 0.2685],\n",
              "           [0.3058, 0.3083, 0.3112,  ..., 0.2786, 0.2741, 0.2687],\n",
              "           ...,\n",
              "           [0.3327, 0.3292, 0.3266,  ..., 0.3340, 0.3349, 0.3358],\n",
              "           [0.3307, 0.3278, 0.3257,  ..., 0.3386, 0.3386, 0.3387],\n",
              "           [0.3289, 0.3264, 0.3248,  ..., 0.3408, 0.3424, 0.3432]]],\n",
              " \n",
              " \n",
              "         [[[0.6185, 0.6143, 0.6095,  ..., 0.4551, 0.4496, 0.4446],\n",
              "           [0.6190, 0.6157, 0.6109,  ..., 0.4558, 0.4480, 0.4417],\n",
              "           [0.6200, 0.6170, 0.6116,  ..., 0.4518, 0.4463, 0.4426],\n",
              "           ...,\n",
              "           [0.5789, 0.5941, 0.5881,  ..., 0.7521, 0.7588, 0.7557],\n",
              "           [0.5759, 0.5900, 0.5858,  ..., 0.7597, 0.7640, 0.7596],\n",
              "           [0.5699, 0.5779, 0.5716,  ..., 0.7668, 0.7631, 0.7590]]],\n",
              " \n",
              " \n",
              "         [[[0.1770, 0.1778, 0.1787,  ..., 0.2116, 0.2117, 0.2116],\n",
              "           [0.1766, 0.1774, 0.1781,  ..., 0.2124, 0.2124, 0.2121],\n",
              "           [0.1768, 0.1775, 0.1782,  ..., 0.2130, 0.2130, 0.2130],\n",
              "           ...,\n",
              "           [0.2266, 0.2265, 0.2263,  ..., 0.2043, 0.2035, 0.2033],\n",
              "           [0.2245, 0.2244, 0.2240,  ..., 0.2019, 0.2019, 0.2007],\n",
              "           [0.2207, 0.2202, 0.2205,  ..., 0.2002, 0.1997, 0.1988]]],\n",
              " \n",
              " \n",
              "         ...,\n",
              " \n",
              " \n",
              "         [[[0.7002, 0.7049, 0.7101,  ..., 0.5492, 0.5467, 0.5436],\n",
              "           [0.6957, 0.7010, 0.7045,  ..., 0.5525, 0.5496, 0.5471],\n",
              "           [0.6905, 0.6950, 0.6958,  ..., 0.5568, 0.5537, 0.5509],\n",
              "           ...,\n",
              "           [0.4335, 0.4311, 0.4304,  ..., 0.3457, 0.3453, 0.3456],\n",
              "           [0.4389, 0.4359, 0.4329,  ..., 0.3456, 0.3456, 0.3457],\n",
              "           [0.4443, 0.4403, 0.4366,  ..., 0.3456, 0.3456, 0.3455]]],\n",
              " \n",
              " \n",
              "         [[[0.4398, 0.4425, 0.4449,  ..., 0.2389, 0.2389, 0.2388],\n",
              "           [0.4388, 0.4408, 0.4434,  ..., 0.2392, 0.2392, 0.2389],\n",
              "           [0.4390, 0.4395, 0.4409,  ..., 0.2393, 0.2392, 0.2390],\n",
              "           ...,\n",
              "           [0.4863, 0.4846, 0.4831,  ..., 0.4309, 0.4392, 0.4459],\n",
              "           [0.4858, 0.4841, 0.4820,  ..., 0.4280, 0.4369, 0.4445],\n",
              "           [0.4845, 0.4809, 0.4802,  ..., 0.4252, 0.4341, 0.4417]]],\n",
              " \n",
              " \n",
              "         [[[0.3231, 0.3260, 0.3322,  ..., 0.4439, 0.4416, 0.4387],\n",
              "           [0.3231, 0.3257, 0.3302,  ..., 0.4482, 0.4454, 0.4429],\n",
              "           [0.3278, 0.3321, 0.3354,  ..., 0.4542, 0.4511, 0.4486],\n",
              "           ...,\n",
              "           [0.4868, 0.4837, 0.4814,  ..., 0.5878, 0.5876, 0.5864],\n",
              "           [0.4818, 0.4774, 0.4765,  ..., 0.5917, 0.5894, 0.5845],\n",
              "           [0.4799, 0.4733, 0.4685,  ..., 0.5905, 0.5856, 0.5799]]]], 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",
              "         [[[0.6194, 0.6180, 0.6167,  ..., 0.4466, 0.4448, 0.4428],\n",
              "           [0.6200, 0.6185, 0.6174,  ..., 0.4466, 0.4446, 0.4424],\n",
              "           [0.6202, 0.6189, 0.6177,  ..., 0.4460, 0.4441, 0.4416],\n",
              "           ...,\n",
              "           [0.5614, 0.5727, 0.5830,  ..., 0.7612, 0.7598, 0.7585],\n",
              "           [0.5615, 0.5699, 0.5796,  ..., 0.7605, 0.7590, 0.7576],\n",
              "           [0.5620, 0.5695, 0.5757,  ..., 0.7595, 0.7579, 0.7562]]],\n",
              " \n",
              " \n",
              "         [[[0.1767, 0.1769, 0.1772,  ..., 0.2115, 0.2115, 0.2115],\n",
              "           [0.1767, 0.1770, 0.1772,  ..., 0.2116, 0.2116, 0.2117],\n",
              "           [0.1766, 0.1769, 0.1772,  ..., 0.2118, 0.2118, 0.2119],\n",
              "           ...,\n",
              "           [0.2218, 0.2218, 0.2218,  ..., 0.1991, 0.1993, 0.1993],\n",
              "           [0.2209, 0.2207, 0.2205,  ..., 0.1985, 0.1988, 0.1987],\n",
              "           [0.2201, 0.2198, 0.2194,  ..., 0.1979, 0.1982, 0.1983]]],\n",
              " \n",
              " \n",
              "         ...,\n",
              " \n",
              " \n",
              "         [[[0.6998, 0.7017, 0.7032,  ..., 0.5447, 0.5428, 0.5411],\n",
              "           [0.6985, 0.7002, 0.7016,  ..., 0.5450, 0.5436, 0.5425],\n",
              "           [0.6970, 0.6986, 0.7001,  ..., 0.5451, 0.5441, 0.5434],\n",
              "           ...,\n",
              "           [0.4437, 0.4423, 0.4407,  ..., 0.3456, 0.3456, 0.3456],\n",
              "           [0.4458, 0.4443, 0.4430,  ..., 0.3456, 0.3455, 0.3455],\n",
              "           [0.4477, 0.4461, 0.4451,  ..., 0.3456, 0.3455, 0.3455]]],\n",
              " \n",
              " \n",
              "         [[[0.4396, 0.4402, 0.4412,  ..., 0.2388, 0.2388, 0.2388],\n",
              "           [0.4390, 0.4398, 0.4407,  ..., 0.2389, 0.2388, 0.2388],\n",
              "           [0.4386, 0.4393, 0.4401,  ..., 0.2389, 0.2389, 0.2388],\n",
              "           ...,\n",
              "           [0.4863, 0.4850, 0.4839,  ..., 0.4402, 0.4427, 0.4449],\n",
              "           [0.4860, 0.4845, 0.4831,  ..., 0.4391, 0.4417, 0.4440],\n",
              "           [0.4851, 0.4836, 0.4821,  ..., 0.4380, 0.4406, 0.4430]]],\n",
              " \n",
              " \n",
              "         [[[0.3232, 0.3244, 0.3256,  ..., 0.4380, 0.4368, 0.4357],\n",
              "           [0.3222, 0.3231, 0.3239,  ..., 0.4397, 0.4387, 0.4372],\n",
              "           [0.3204, 0.3213, 0.3222,  ..., 0.4410, 0.4398, 0.4386],\n",
              "           ...,\n",
              "           [0.4823, 0.4800, 0.4776,  ..., 0.5829, 0.5811, 0.5795],\n",
              "           [0.4825, 0.4799, 0.4774,  ..., 0.5817, 0.5799, 0.5784],\n",
              "           [0.4825, 0.4801, 0.4777,  ..., 0.5809, 0.5792, 0.5777]]]], device='cuda:0'))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "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": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Byih6hDloDvT"
      },
      "source": [
        "# EPSCN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WU4Vj5biUkQx"
      },
      "source": [
        "class EPSCN(nn.Module):\n",
        "  \n",
        "    def __init__(self, upscale=2):\n",
        "        super(EPSCN, self).__init__()\n",
        "        self.upscale = upscale\n",
        "        self.conv1 = nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2)\n",
        "        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)\n",
        "        self.conv3 = nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1)\n",
        "        self.conv4 = nn.Conv2d(32, upscale ** 2, kernel_size=3, stride=1, padding=1)\n",
        "        self.pixel_shuffle = nn.PixelShuffle(self.upscale)\n",
        "\n",
        "    def _initialize_weights(self):\n",
        "        weights_with_relu = [\n",
        "            self.conv1.weight,\n",
        "            self.conv2.weight,\n",
        "            self.conv3.weight\n",
        "        ]\n",
        "\n",
        "        for w in weights_with_relu:\n",
        "            nn.init.orthogonal_(w, nn.init.calculate_gain('relu'))\n",
        "\n",
        "        nn.init.orthogonal_(self.conv4.weight)\n",
        "\n",
        "    def forward(self, x):\n",
        "        h = torch.tanh(self.conv1(x))\n",
        "        h = torch.tanh(self.conv2(h))\n",
        "        h = torch.tanh(self.conv3(h))\n",
        "        return self.pixel_shuffle(self.conv4(h))"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4B_K2dLbXNry",
        "outputId": "23365c97-edcc-4323-d490-5229c4187df9"
      },
      "source": [
        "test_input=torch.ones(1, 1, 100, 100)\n",
        "g = EPSCN(3)\n",
        "test_input = test_input.cuda()\n",
        "g = g.cuda()\n",
        "out = g(test_input)\n",
        "print(out.size())"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 1, 300, 300])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4Z-nVJyU05t"
      },
      "source": [
        "model = EPSCN(upscale=3)\n",
        "loss_func = MSELossFlat()\n",
        "metrics = [mse]\n",
        "learn = Learner(dls, model, loss_func=loss_func, metrics=metrics)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WDuTDrEu6QBC"
      },
      "source": [
        "# a lots of epochs are needed for proper results \n",
        "epoch = 10"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "g8AweIPIGP0B",
        "outputId": "19f4cd1d-5760-4c14-97da-7c0234776757"
      },
      "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": 17,
      "outputs": [
        
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pypwGHzVYVf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 777
        },
        "outputId": "0e93a49f-174f-4a38-cc6d-ccf0a444593b"
      },
      "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": 18,
      "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>mse</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.190330</td>\n",
              "      <td>0.085678</td>\n",
              "      <td>0.085678</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.121213</td>\n",
              "      <td>0.039285</td>\n",
              "      <td>0.039285</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.082795</td>\n",
              "      <td>0.010401</td>\n",
              "      <td>0.010401</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.059903</td>\n",
              "      <td>0.004569</td>\n",
              "      <td>0.004569</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.045635</td>\n",
              "      <td>0.003266</td>\n",
              "      <td>0.003266</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.035974</td>\n",
              "      <td>0.002506</td>\n",
              "      <td>0.002506</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.029119</td>\n",
              "      <td>0.002224</td>\n",
              "      <td>0.002224</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.024066</td>\n",
              "      <td>0.002200</td>\n",
              "      <td>0.002200</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.020240</td>\n",
              "      <td>0.002185</td>\n",
              "      <td>0.002185</td>\n",
              "      <td>00:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.017234</td>\n",
              "      <td>0.002173</td>\n",
              "      <td>0.002173</td>\n",
              "      <td>00:01</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: 0.08567774295806885.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+vfWw3XGUUuqMaQF0wJVj+jC0VwT/++FuvX6wUsrraQF0gJ/DunLYgWK9frBSyvtpAXTQlMEJTBoQxxMr91Kl1w9WSnkxLYAOEhEemDmU4uoGFq3JtjuOUkqdNi2A03BW32guG5XMok+zdYgIpZTX0gI4TfdfOoTG5hYe+2C33VGUUuq0aAGcprT4MG45L403NuTx9cFSu+MopVSHaQGcgR9dnE5iRBAPL9uuJ4cppbyOFsAZCA/y58FZQ9mcV87SDXl2x1FKqQ7RAjhDV43pw7j+Mfzhg12U1zbaHUcppdzmVgGIyAwR2S0iWSLyQDvTLxCRjSLSJCJz2kxrFpFNztuyzgruKUSEX88eQUlNA0+s3Gt3HKWUctspC0BE/ICngJnAcOA6ERneZraDwPeB19p5i1pjzBjnbfYZ5vVII/tEcd2Efrz8ZQ57j1baHUcppdzizhrABCDLGJNtjGkAFgNXus5gjMkxxmwBfHaAnPunDyE8yJ+H392OMbpDWCnl+dwpgD5ArsvzPOdr7goWkUwRWSsiV7U3g4jc7pwns7CwsANv7TliwwL58bR0Ps8q5ot9xXbHUUqpU+qOncD9jTEZwPXAX0RkYNsZjDELjTEZxpiMhISEbojUNa47px/JUcE8/tEeXQtQSnk8dwrgENDX5XmK8zW3GGMOOe+zgdXA2A7k8ypB/n788KJBZB4o5fMsXQtQSnk2dwpgPZAuImkiEgjMB9w6mkdEYkQkyPk4HpgM7DjdsN7g2owUekcF8/hKXQtQSnm2UxaAMaYJuBtYAewElhhjtovIIyIyG0BExotIHjAXeE5EtjsXHwZkishmYBXwqDGmRxdA61rAhgOlfKbXD1ZKeTDxtP+lZmRkmMzMTLtjnJH6pmYuemw1vaKCefOucxERuyMppXo4Edng3N/qNj0TuAu0rgVsPFjGp3t1LUAp5Zm0ALrItRl96R0VzF90X4BSykNpAXSRQH8HCy621gLW6FqAUsoDaQF0obnjrLWAP324W4eLVkp5HC2ALhTo7+D+S4ewJa+cNzbknnoBpZTqRloAXezqsX0YnxrDHz7YTVlNg91xlFLqGC2ALmYNFz2SspoG/vzRHrvjKKXUMVoA3WB470i+N7E//7f2ANvzy+2Oo5RSgBZAt/nJ9CHEhAby0Dt6/WCllGfQAugmUSEB/GLGUDYcKOWtr90eS08ppbqMFkA3mjMuhTF9o3l0+U4q6vT6wUope2kBdCOHQ/jNlSMprm7g9+/vtDuOUsrHaQF0s1EpUdxxwUBeX5fLmxvy7I6jlPJhWgA2uH/6YCYOiOWXb29lR36F3XGUUj5KC8AG/n4O/nrd2USFBHDXqxsor9X9AUqp7qcFYJOEiCCevuFsDpXWcv8bm/XQUKVUt9MCsFFGaiwPzhrGRzuO8tyabLvjKKV8jBaAzW6ZnMplo5N5bMUuPtNho5VS3UgLwGYiwh+uGc2gxHB++OoG9hVW2R1JKeUjtAA8QHiQPy/cNB5/Pwe3/n09pdU6aqhSqutpAXiIvrGhLPzeOPLL6rjr1Q00NLXYHUkp1cNpAXiQjNRY/jBnFGuzS3jonW16LWGlVJfytzuAOt7VY1PIKqjiqVX7GJQYzg/OH2B3JKVUD6UF4IF+eskQsgur+e37O+kbG8qlI3rZHUkp1QPpJiAP5HAIf752DKNTorl38dd8fbDU7khKqR5IC8BDhQT68cJNGSREBPGDlzM5UFxtdySlVA+jBeDB4sOD+PvNE2g2hu+/tJ4SPTxUKdWJtAA83MCEcJ6/MYNDZbXc9komdY3NdkdSSvUQWgBeICM1lr/MG8PGg6Xcu/hrPUdAKdUptAC8xKxRyTx0+XBWbD/KrS+vp7q+ye5ISikvpwXgRW6enMYfrxnNF/uKuX7RWoqr6u2OpJTyYloAXuba8X157rvj2HWkkjnPfkluSY3dkZRSXkoLwAtNG57Eqz84h5LqBq555gt2HtbLSiqlOs6tAhCRGSKyW0SyROSBdqZfICIbRaRJROa0mXaTiOx13m7qrOC+LiM1ljfunIRDhOsWrdVrCyulOuyUBSAifsBTwExgOHCdiAxvM9tB4PvAa22WjQV+BZwDTAB+JSIxZx5bAQxOimDJHZMIDfDjhufX6pqAUqpD3FkDmABkGWOyjTENwGLgStcZjDE5xpgtQNvjEy8FPjLGlBhjSoGPgBmdkFs59YsL5fXbJxIc4McNz3/F7iOVdkdSSnkJdwqgD5Dr8jzP+Zo73FpWRG4XkUwRySwsLHTzrVWr/nFhvH7bRAL8hOsXrWXPUS0BpdSpecROYGPMQmNMhjEmIyEhwe44Xik13ioBP4eWgFLKPe4UwCGgr8vzFOdr7jiTZVUHDUgI5/XbJ+IQYe6zX7LhQIndkZRSHsydAlgPpItImogEAvOBZW6+/wpguojEOHf+Tne+prrIwIRw3rzrXGLDArnh+a9YueOo3ZGUUh7qlAVgjGkC7sb6xb0TWGKM2S4ij4jIbAARGS8iecBc4DkR2e5ctgT4DVaJrAcecb6mulDf2FCW3jmJwUkR3PF/G/jn+oN2R1JKeSDxtOvOZmRkmMzMTLtj9AjV9U3c9epG1uwp5P7pg1lw0SBExO5YSqkuICIbjDEZHVnGI3YCq64RFuTP8zdmcPXYPvzvh3t44M2tOpKoUuoYvSZwDxfo7+BPc8+iT3QIf1uVxYGSap65YRwxYYF2R1NK2UzXAHyAwyHcf+kQHp93FhsPlHHV05+TVVBldyyllM20AHzI1WNTeP32iVTXN3H105/z6V496U4pX6YF0J6GGmistTtFlxjXP4Z/LZhM76gQbnpxHU+vzqKlxbMOBFBKdQ8tgLaaG+HlK+Bfd0FLz9xhmhITyps/PJeZo5L54we7ue2VTMpq9ILzSvkaLYC2/AJg2BWw/W345Dd2p+ky4UH+/O26sTxy5QjW7C3ksic/Y1Numd2xlFLdSAugPZPvhbNvgs/+DBv/YXeaLiMi3DgplaV3ngvA3Ge/4MXP9usmIaV8hBZAe0Tgsj/BgIvgvfsge7XdibrUWX2j+fc953Hh4AQeeW8HN720jiPldXbHUkp1MS2AE/ELgGtfhrh0+OeNULDL7kRdKjo0kEU3ZvDbq0eSmVPK9Mf/wzubdNw+pXoyLYCTCY6CG5aAfxC8NheqCuxO1KVEhBvO6c/ye89nUGI49y7exN2vbaS0WncQK9UTaQGcSnQ/uH4xVBXC6/OtQ0R7uNT4MJbcMYmfXTqED7Yd4ZLH17B862G7YymlOpkWgDv6jINrFsGhjfD2HT328FBX/n4OFlw0iHfunkyvqCDuenUjd/wjk4IK3TegVE+hBeCuYVfA9N/AzmXw8cN2p+k2I3pH8a8fTuaBmUNZvbuQaX/+D0vW5+Jpo8gqpTpOC6AjJt0NGbfA50/Ahr/bnabb+Ps5uPPCgSy/93yGJkfy8ze3cP2ir8gu1PGElPJmWgAdIQIzH4NB0+C9n8C+T+xO1K0GJISz+LaJ/PbqkWzLL2fGXz7liZV7qW9qtjuaUuo0aAF0lJ8/zHkJEobCkpvg6A67E3Urh8M6Uujjn17IpSN78fjKPcx64lPWZhfbHU0p1UFaAKcjONI6PDQgFF67Fip977q7iRHB/PW6sbx083jqm1qYv3AtC17bSG5Jzz9KSqmeQgvgdEWlWIeH1hT7zOGh7bloSCIf/fhC7pmazsc7jzL1T//hd+/vpLy20e5oSqlT0AI4E73HwjXPQ/7X8NZtPnF4aHtCAv34ySWDWX3/Rcwe05tFn2Yz5bFV/P3z/XoJSqU8mBbAmRp6GVz6O9j1Hqx8yO40tuoVFcz/zj2Ld+8+j2HJkTz87g6m/dkaUkIHmFPK82gBdIaJd8H42+CLv8L6F+xOY7uRfaJ49Qfn8PebxxMW5M+9izdx2V8/Y9XuAj1/QCkPogXQGURgxqOQPh3e/xnsXWl3ItuJCFOGJPLvH53HE/PHUFXfyM0vrWfewrV8sa9Ii0ApDyCe9g8xIyPDZGZm2h3j9NRXwoszoTQHbvkAeo20O5HHaGhq4fV1B3lqVRYFlfVMSIvlvqnpTBoYh4jYHU8pryciG4wxGR1aRgugk5UfguengvjBbR9DRC+7E3mUusZm/rk+l6dXZ3G0op7xqTH86OJ0zk+P1yJQ6gxoAXiK/E3w0iyIT4eb34fAMLsTeZy6xmaWZOby9Kp9HKmoY0TvSO64cCCzRvbC30+3TCrVUVoAnmT3B7D4Ohg8E+b9Axx+difySPVNzbzzdT7PrtlHdmE1/WJDue38NOaM60tIoP6ZKeUuLQBP89VzsPznMHEBzPid3Wk8WkuL4aOdR3lm9T425ZYRFRLA/Al9+d7E/qTEhNodTymPdzoF4N9VYRRwzh1Qkg1rn4LYNJhwm92JPJbDIVw6ohfThyexPqeUlz7fz6I12Sxak82lI3rx/XNTmZAWq/sJlOpEWgBd7dLfQekBa00guj8Mnm53Io8mIkxIi2VCWiyHymp55cscFq/LZfm2IwxICOPajL5cc3YKCRFBdkdVyuvpJqDuUF8FL8201gZu+QB6jbI7kVepbWjm3S35LFmfS+aBUvwdwsVDE5k3vi8XDk7QncZKofsAPFtFPiyaaj2+7WOI7G1vHi+VVVDJksw83tqYR1FVA4kRQVwzLoW541IYkBBudzylbKMF4OmObIUXZ0DsALh5OQTpL6zT1dDUwqrdBbyRmcuq3YU0txgmpMZyzbg+zBiRTFRogN0RlepWXVYAIjIDeALwA543xjzaZnoQ8AowDigG5hljckQkFdgJ7HbOutYYc+fJflaPLgCAPR/C6/OsYSPmv6aHh3aCoxV1vLXxEG9k5pJdVE2An3Dh4ESuOCuZS4YnERqou7pUz9clBSAifsAe4BIgD1gPXGeM2eEyzw+B0caYO0VkPnC1MWaeswDeM8a4PSZCjy8AgHWL4P374Zw7YeYf7E7TYxhj2HqonGWb8nlvy2GOVNQREuDH1GGJXD46mSlDEgkO0MJVPVNXHQY6AcgyxmQ7f8hi4ErA9VqIVwIPOx8vBf4merzeiU24DUr2Ow8PHWAdLqrOmIgwOiWa0SnR/HLWMNbllLBscz4rth3hvS2HCQ30Y9qwJGaNSubCwQl6opnyee4UQB8g1+V5HnDOieYxxjSJSDkQ55yWJiJfAxXAfxtjPm37A0TkduB2gH79+nXoA3it6b+xBo374AHr8NAhM+xO1KM4HMLEAXFMHBDHI7NH8NX+Et7bcpgPth1m2eZ8QgL8uHBwApeOTOLioUlEheg+A+V73NkENAeYYYz5gfP594BzjDF3u8yzzTlPnvP5PqySqATCjTHFIjIO+BcwwhhTcaKf5xObgFo1VFtjBhXthVuWQ/JZdifq8ZqaW1ibXcKK7Uf4cMcRjlbU4+8QJg2MY9qwJKYOS9Qzj5VX6qp9AJOAh40xlzqfPwhgjPm9yzwrnPN8KSL+wBEgwbR5cxFZDdxvjDnhb3ifKgCAisPw/DQwzfCDjyGqj92JfEZLi2FTXplVBtuPsr+oGoBhyZFcMiyRi4clMbpPFA6Hbs1Unq+rCsAfayfwVOAQ1k7g640x213mWQCMctkJ/B1jzLUikgCUGGOaRWQA8KlzvpIT/TyfKwCAI9usw0NjUq01gaAIuxP5pH2FVXy88ygrdxSQeaCEFgNxYYFcODiBC4ckcEF6AjFhgXbHVKpdXXkY6CzgL1iHgb5ojPmtiDwCZBpjlolIMPAPYCxQAsw3xmSLyDXAI0Aj0AL8yhjz7sl+lk8WAEDWSnj1Whg0Fea/Dn566KKdSqsbWLO3kFW7CvjPnkJKaxpxCIxOiea8QfGclx7P2H7RBPnrjmTlGfREMG+X+SK892Pr+sKzHrMuNals19xi2JJXxqrdhXy2t5DNeeU0txhCAvw4Z0Ask5w7m0f0jtRhKZRttAB6gg//27q4/IxHrYvNK49TUdfIV9klfJ5VxGdZRWQVVAEQEeTP+LRYJg6IZUKaVQgBWgiqm+hw0D3BtEech4c+CNH9YOhldidSbUQGB3DJ8CQuGZ4EQEFlHV9ll/BldjFrs4v5ZFcBAKGBfpzdL4bxqbGMT4thTN9oPStZeZuS/aIAAA5nSURBVBRdA/BEDTXw98ugcBdc8SSMnmt3ItUBBRV1rM8pZd3+YtbllLLrSAXGgL9DGN47koz+sWSkxjCufwxJkcF2x1U9hG4C6kmqCuCf34PctXDW9TDrj3p0kJcqr21k44FSMg+UkJlTyqbcMuqbWgDoFRnMmL7RjOkXzVkp0YxKiSI8SNcSVMdpAfQ0zU2w5o+w5jHrENE5L0LvsXanUmeooamF7fnlfH2wjE251u1gSQ1g7fcflBDO6JRozuobxeiUaIb2itAxjNQpaQH0VDmfw1u3WWsF035lXWPYoTsXe5KS6gY255axOa+MLXnlbMkro6iqAQA/h5CeGM6I3lGM7BPJyD5RDO0VQUSwDl+hvqEF0JPVlMCyH8Gu92DgxXDVsxCRZHcq1UWMMeSX17Elt4zt+RVsyy9n26EKiqrqj83TLzaU4cmRDO8dybDkSIb2iiAlJkSvm+yjtAB6OmNgw0vWEUJBEXDVM5B+id2pVDcqqKhjW345Ow9XsiO/gp2HK9hfXE3rP+OIIH+GJkcwtFckQ3pFMKRXBIMTI/QCOT5AC8BXFOyEpbdAwQ6YdDdMfQj89SLpvqq6vondRyvZdbiSnYcr2HWkgl2HK6msbzo2T1JkEIOTIkhPjGBwUjjpSRGkJ4UTqZuRegwtAF/SWAsf/j9Yvwh6jYY5L0H8ILtTKQ/Ruglpz9FK9hypZM/RKvYcrSSroIraxuZj8yVHBTMwIZyBCWEMSAhnYEI4AxLCSI4K1k1JXkYLwBft+je8swCaGqzhI8Zcr0NIqBNqaTHkldZaxVBQSdbRKvYVVrGvsJoqlzWGkAA/UuPDGJAQxoD4MFLjwkiNDyM1LpTYsEAtBw+kBeCryg/B23dAzqcw8hq4/HEIjrI7lfIixhgKKuuPlcH+wmr2F1Wxv6ia3NJamlu++T0REeRP//hQ+seG0S8ulP6xofSLC6VfbCjJUSH46fDZttAC8GUtzfDZ47Dqd9Y1Ba55EfqOtzuV6gEamlrILa3hQHE1OUU15BRXW8VQUkNeaS1NLuUQ4Cf0jg6hX2woKTGh9I0NoW9MKH1jQ+kbE6JrD11IC0BB7jp481ZrreCiX8J5PwaHnkSkukZTcwuHy+s4WFLDgeIacktrOFhSQ15JDbmltZRUNxw3f1igHykxoaTEhNAnJoSUmBBSYkLpHR1C76hg4sOD9AI8p0kLQFnqyuHd+2D7W5B6PnxnIUT2tjuV8kFV9U3kldaQW1JLbolVELkltRwqqyWvpOa4I5XAWoPoFRVMcpRVCMnOYkiOCiE52rqPCQ3QtYh2aAGobxgDm16F938G/sFw1dMwZKbdqZQ6TnltI3mlNeSX1XG4vJb8sjryy2qPPT5aUXfcJiaAQH8HSZFB9IoMJunYLYikyGASI4LpFWU997WRV7UA1LcV7bXOGTiyBSbcDpf8BgJ0BErlHZpbDEVV9c5SqONIuVUKRyq+eXy0ov64Q1tbRQT5k+Qsg8SIYBIjgkhw3hIjgo89jgz27xFrFFoAqn1N9bDy17D2KUgcYQ0qlzjU7lRKdQpjDFX1TRytqHcWQt1xj49U1FFQUU9hVT0NzlFYXQX6OUiICCI+PJD48CDiw4OIcz52vY8LCyImNMBjr/qmBaBObu9K+NedUF8FM34H427WcwaUzzDGUFHbREFlHQWV9RRV1VNYaRVDYWU9RVUNFFXWU1xdT3FVw7c2PYH1zyUqJIDYsEDiwgKJdd7iwoKse2dRxIUHEhMaSHRoQLeN5KoFoE6t8qhVAvs+gWFXWBecCY21O5VSHqWlxVBR10hRlVUGxdUNFFfVU1jVQEl1PaXVjRRX11NS3XDs1k5fABAc4CA6xCqD6NAAZzEEOh8HEB1qlUXr4+jQAKJDOr6moQWg3NPSAl/+DT5+BMIT4TuLIHWy3amU8lotLYby2sZjaw/F1Q2U1TRSWtNAeW0jZTUNlNYcf19W09juWkariGD/Y4URFRJwbI0iOiSAqNBALh+dfNwV5fSawMo9DgdMvgdSz7POGXj5crjg53DBz8BPvxJKdZTDIcSEBRITFsigRPeWad130VoUrsVQ6rw/Vhi1jeSW1FBW20h5bSPGwPjUM7+kqP5r92V9zoY71liHiv7nUcheDdcssi5Gr5TqUiJCRHAAEcEB9I0NdXu51s1TYZ1w6VDP3J2tuk9QBFz9LHzneTi6HZ49D7b/y+5USqkTcDiE6NBAAjrhaCQtAGUZPRfu/BTiBsEbN8Gye6Chxu5USqkupAWgvhGbBressMYP2vgKLJwCR7banUop1UV0H4A6nl8ATHsYBkyBt+6ARVNh1ByIT4fYgdYaQmwaBITYm1Mpdca0AFT7BkyBuz6H5b+AvR9Z4wodIxCVArEDrEKIG/hNOcT0t0pEKeXxtADUiYXFw5wXrMd1FVCyD4qdt5J9UJwF25Zao4+2Ej+rBFoLIW7gNwURlaJDUyvlQbQAlHuCI6H3WOvmyhioKfmmEIqd9yX74MDn0OiyI9kvyNp8FDfo22sPEb10WAqlupkWgDozIhAWZ936Tjh+mjFQeeSbQijOguJs637vh9DscrGQwHCXcnBdexikQ1Uo1UW0AFTXEYHIZOuWdv7x01qaoTzPWQ7Z36w95G+CHcvAuAzvGxxtbVYKCLOGsvYPBv8g8A9x3ge7vO4yPcBleuvtRMv7B1tnSCvlQ7QAlD0czn0FMf2BqcdPa2qAsgPHb04qy4WmOmtfRFOh9bj11ui8b2k8s0x+ge2USpsC8Qu0hstwBFg7ux3+zvvWxyeYdtxz/5NMC/jmPVznP25ePxDHt2+I87Ho5jTlFrcKQERmAE8AfsDzxphH20wPAl4BxgHFwDxjTI5z2oPArUAzcI8xZkWnpVc9k3+gddhpfHrHlmtpdpZCvbMYar953LYsmuqhyWX6KV+vg+pCaGmC5karbJqbnPeuz52vmW+PO9+9XMvgZEXhaH8+d+c54Y8/WQGdYNpJO+t0f9Yp37hz3qNTCrf7S/uUBSAifsBTwCVAHrBeRJYZY3a4zHYrUGqMGSQi84E/APNEZDgwHxgB9AZWishgY8y3L9+j1Jly+EFgmHWzW0tLO+XQ+rzJjSJxzneiacY4S8Z5b1qcr7k+bznBPC2nP1/rPC2n+U/4hKMPn2RU4pOOWHyK0YzdGu34TN+jE0ZUPuNRmU9veXfWACYAWcaYbAARWQxcCbgWwJXAw87HS4G/iXWNtSuBxcaYemC/iGQ53+/L00qrlLdwOMARZG1CUqo73NjxNQh3CqAPkOvyPA8450TzGGOaRKQciHO+vrbNsn3a/gARuR243fm0XkS2uZXeM8UDRXaHOAOa316a3z7enB1gSEcX8IidwMaYhcBCABHJ7OhFDTyJ5reX5reXN+f35uxg5e/oMu4c93YI6OvyPMX5WrvziIg/EIW1M9idZZVSStnAnQJYD6SLSJqIBGLt1F3WZp5lwE3Ox3OAT4x1rcllwHwRCRKRNCAdWNc50ZVSSp2JU24Ccm7TvxtYgXUY6IvGmO0i8giQaYxZBrwA/MO5k7cEqyRwzrcEa4dxE7DAjSOAFp7+x/EImt9emt9e3pzfm7PDaeT3uIvCK6WU6h567rtSSvkoLQCllPJRHlUAIjJDRHaLSJaIPGB3nlMRkRdFpMD1vAURiRWRj0Rkr/M+xs6MJyMifUVklYjsEJHtInKv83WP/wwiEiwi60RkszP7r52vp4nIV87v0D+dBy54LBHxE5GvReQ953OvyS8iOSKyVUQ2tR6C6A3fnVYiEi0iS0Vkl4jsFJFJ3pJfRIY4/9xbbxUicl9H83tMAbgMOTETGA5c5xxKwpP9HZjR5rUHgI+NMenAx87nnqoJ+KkxZjgwEVjg/DP3hs9QD1xsjDkLGAPMEJGJWMOQPG6MGQSUYg1T4snuBXa6PPe2/BcZY8a4HD/vDd+dVk8AHxhjhgJnYf09eEV+Y8xu55/7GKwx2GqAt+lofmOMR9yAScAKl+cPAg/ancuN3KnANpfnu4Fk5+NkYLfdGTvwWd7BGvPJqz4DEApsxDpDvQjwb+875Wk3rPNiPgYuBt7DGg3Mm/LnAPFtXvOK7w7WuUr7cR4I423522SeDnx+Ovk9Zg2A9oec+NawEV4gyRhz2Pn4CJBkZxh3iUgqMBb4Ci/5DM7NJ5uAAuAjYB9QZoxpcs7i6d+hvwA/B1qHDo3Du/Ib4EMR2eAczgW85LsDpAGFwEvOTXDPi0gY3pPf1XzgdefjDuX3pALocYxVwx5/nK2IhANvAvcZYypcp3nyZzDGNBtrFTgFa5DBoTZHcpuIXA4UGGM22J3lDJxnjDkba7PtAhG5wHWiJ393sM6BOht4xhgzFqimzeYSD88PgHMf0WzgjbbT3MnvSQXQU4aNOCoiyQDO+wKb85yUiARg/fJ/1RjzlvNlr/oMxpgyYBXWJpNo53Ak4NnfocnAbBHJARZjbQZ6Au/JjzHmkPO+AGv78wS857uTB+QZY75yPl+KVQjekr/VTGCjMeao83mH8ntSAbgz5IQ3cB0W4yas7eoeSUQE6yzuncaYP7tM8vjPICIJIhLtfByCte9iJ1YRzHHO5pHZAYwxDxpjUowxqVjf9U+MMTfgJflFJExEIlofY22H3oYXfHcAjDFHgFwRaR1BcyrWiAVekd/FdXyz+Qc6mt/uHRhtdmbMAvZgbcv9L7vzuJH3deAw0Ij1P4pbsbbjfgzsBVYCsXbnPEn+87BWEbcAm5y3Wd7wGYDRwNfO7NuAh5yvD8AabyoLa7U4yO6sbnyWKcB73pTfmXOz87a99d+rN3x3XD7DGCDT+R36FxDjZfnDsAbdjHJ5rUP5dSgIpZTyUZ60CUgppVQ30gJQSikfpQWglFI+SgtAKaV8lBaAUkr5KC0ApZTyUVoASinlo/4/v7wXHG5PctIAAAAASUVORK5CYII=\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: 0.03928452357649803.\n",
            "Better model found at epoch 2 with valid_loss value: 0.010401451960206032.\n",
            "Better model found at epoch 3 with valid_loss value: 0.004569319076836109.\n",
            "Better model found at epoch 4 with valid_loss value: 0.0032658525742590427.\n",
            "Better model found at epoch 5 with valid_loss value: 0.0025061550550162792.\n",
            "Better model found at epoch 6 with valid_loss value: 0.0022239424288272858.\n",
            "Better model found at epoch 7 with valid_loss value: 0.002200094284489751.\n",
            "Better model found at epoch 8 with valid_loss value: 0.002184587763622403.\n",
            "Better model found at epoch 9 with valid_loss value: 0.002172737382352352.\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": "8dc8b46c-98ac-40fb-fa14-ce545e0793ea"
      },
      "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",
        "# upsampling is done by the algorithm from this path:\n",
        "path_lr_ds = '/content/gdrive/MyDrive/fastai/data_test_100/lr'\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",
        "    current_lr = os.path.join(path_lr_ds, file)\n",
        "    f = rasterio.open(current_lr)\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",
        "\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",
        "    # 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": 25,
      "outputs": [
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean MAE LR 0.8654804018139839\n",
            "Mean MAE SR 40.828428745269775\n",
            "Difference LR - SR MAE  -39.96294834345579\n",
            "Mean RMSE LR 1.5166528463363647\n",
            "Mean RMSE SR 53.91793758392334\n",
            "Difference SR - LR RMSE  -52.40128473758698\n",
            "MAX LR 24.815706787109374\n",
            "MAX SR 242.4800946044922\n",
            "Difference LR - SR MAX  -217.66438781738282\n"
          ]
        }
      ]
    }
  ]
}