SegNet_S2_Philab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports and stuff\n",
    "import numpy as np\n",
    "from skimage import io\n",
    "from glob import glob\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import random\n",
    "import itertools\n",
    "# Matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# Torch imports\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.utils.data as data\n",
    "import torch.optim as optim\n",
    "import torch.optim.lr_scheduler\n",
    "import torch.nn.init\n",
    "from torch.autograd import Variable\n",
    "from scipy import sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "WINDOW_SIZE = (288, 288) # Patch size\n",
    "STRIDE = 32 # Stride for testing\n",
    "BANDS = {'1': 60, '2': 10, '3': 10, '4': 10, '5': 20, '6': 20,\n",
    "         '7': 20, '8': 10, '8A': 20, '9': 60, '10': 60, '11': 20, '12': 20}\n",
    "RGB_BANDS = (3,2,1)\n",
    "\n",
    "#BANDS = {'2': 10, '3': 10, '4': 10, '8': 10}\n",
    "TCI = False\n",
    "IN_CHANNELS = len(BANDS)\n",
    "PRETRAINED = False\n",
    "FOLDER = \"./ISPRS_dataset/\" # Replace with your \"/path/to/the/ISPRS/dataset/folder/\"\n",
    "BATCH_SIZE = 10 # Number of samples in a mini-batch\n",
    "\n",
    "LABEL_DETAILS = [('No data', (0,0,0)),\n",
    "          ('Tree cover areas', (0,160,0)),\n",
    "          ('Shrubs cover areas', (150,100,0)),\n",
    "          ('Grassland', (255,180,0)),\n",
    "          ('Cropland', (255,255,100)),\n",
    "          ('Vegetation aquatic or regularly flooded', (0,220,130)),\n",
    "          ('Lichens Mosses / Sparse vegetation', (255,235,175)),\n",
    "          ('Bare areas',(255,245,215)),\n",
    "          ('Built up areas',(195,20,0)),\n",
    "          ('Snow and/or Ice',(255,255,255)),\n",
    "          ('Open Water',(0,70,200)),\n",
    "          ('Cloud', (175,175,175))]\n",
    "\n",
    "LABELS = [l[0] for l in LABEL_DETAILS]\n",
    "N_CLASSES = len(LABELS) # Number of classes\n",
    "WEIGHTS = torch.ones(N_CLASSES) # Weights for class balancing\n",
    "WEIGHTS[0] = 0.\n",
    "CACHE = True # Store the dataset in-memory\n",
    "\n",
    "DATASET = '../east_africa.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "palette = {v: k[1] for v,k in enumerate(LABEL_DETAILS)}\n",
    "\n",
    "invert_palette = {v: k for k, v in palette.items()}\n",
    "\n",
    "def normalize(img):\n",
    "    img[img > 0.2] = 0.2\n",
    "    img *= 5\n",
    "    return img\n",
    "\n",
    "def bounding_box(mask):\n",
    "    # Find rows containing at least a True\n",
    "    rows = np.any(mask, axis=1)\n",
    "    # Find columns containing at least a True\n",
    "    cols = np.any(mask, axis=0)\n",
    "    x_min, x_max = np.where(rows)[0][[0, -1]]\n",
    "    y_min, y_max = np.where(cols)[0][[0, -1]]\n",
    "    return x_min, y_min, x_max, y_max\n",
    "\n",
    "\n",
    "def convert_to_color(arr_2d, palette=palette):\n",
    "    \"\"\" Numeric labels to RGB-color encoding \"\"\"\n",
    "    arr_3d = np.zeros((arr_2d.shape[0], arr_2d.shape[1], 3), dtype=np.uint8)\n",
    "\n",
    "    for c, i in palette.items():\n",
    "        m = arr_2d == c\n",
    "        arr_3d[m] = i\n",
    "\n",
    "    return arr_3d\n",
    "\n",
    "def convert_from_color(arr_3d, palette=invert_palette):\n",
    "    \"\"\" RGB-color encoding to grayscale labels \"\"\"\n",
    "    arr_2d = np.zeros((arr_3d.shape[0], arr_3d.shape[1]), dtype=np.uint8)\n",
    "\n",
    "    for c, i in palette.items():\n",
    "        m = np.all(arr_3d == np.array(c).reshape(1, 1, 3), axis=2)\n",
    "        arr_2d[m] = i\n",
    "\n",
    "    return arr_2d\n",
    "\n",
    "from s2reader import s2reader as s2\n",
    "import math\n",
    "\n",
    "def rowcol(x, y, affine, op=math.floor):\n",
    "    \"\"\" Get row/col for a x/y\n",
    "    \"\"\"\n",
    "    r = int(op((y - affine.f) / affine.e))\n",
    "    c = int(op((x - affine.c) / affine.a))\n",
    "    return r, c\n",
    "def bounds_window(bounds, affine):\n",
    "    \"\"\"Create a full cover rasterio-style window\n",
    "    \"\"\"\n",
    "    w, s, e, n = bounds\n",
    "    row_start, col_start = rowcol(w, n, affine)\n",
    "    row_stop, col_stop = rowcol(e, s, affine, op=math.ceil)\n",
    "    return (row_start, row_stop), (col_start, col_stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyproj\n",
    "from shapely.geometry import Polygon\n",
    "from functools import partial\n",
    "\n",
    "def project_bbox(crs_in, crs_out, bounds):\n",
    "    \"\"\"\n",
    "        Project a bounding box from a CRS to another\n",
    "\n",
    "        :param crs_in: an input CoordinateReferenceSystem\n",
    "        :param crs_out: the target CoordinateReferenceSystem\n",
    "        :param bounds: a tuple of bounds (xmin, ymin, xmax, ymax)\n",
    "        :param return: the tuple of projected bounds\n",
    "    \"\"\"\n",
    "    xmin, ymin, xmax, ymax = bounds\n",
    "    bbox = [(xmin,ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)]\n",
    "    transform = partial(pyproj.transform, pyproj.Proj(crs_in), pyproj.Proj(crs_out))\n",
    "    new_coords = []\n",
    "    for x1, y1 in bbox:\n",
    "        x2, y2 = transform(x1, y1)\n",
    "        new_coords.append((x2, y2))\n",
    "    return Polygon(new_coords).bounds\n",
    "\n",
    "def get_rgb(data):\n",
    "    return normalize(np.transpose(data[RGB_BANDS,:,:],(1,2,0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(DATASET) as f:\n",
    "    urls = [p.replace('\\n','') for p in f.readlines()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utils\n",
    "\n",
    "def _get_random_pos(img_shape, window_shape):\n",
    "    w, h = window_shape\n",
    "    W, H = img_shape\n",
    "    x1 = random.randint(0, W - w - 1)\n",
    "    x2 = x1 + w\n",
    "    y1 = random.randint(0, H - h - 1)\n",
    "    y2 = y1 + h\n",
    "    return x1, x2, y1, y2\n",
    "\n",
    "def get_random_pos(img_shape, window_shape, mask=None):\n",
    "    \"\"\" Extract of 2D random patch of shape window_shape in the image \"\"\"\n",
    "    if mask is None:\n",
    "        return _get_random_pos(img_shape, window_shape)\n",
    "    else:\n",
    "        x1, x2, y1, y2 = _get_random_pos(img_shape, window_shape)\n",
    "        while np.count_nonzero(mask[x1:x2,y1:y2]) < 0.8 * mask[x1:x2,y1:y2].size:\n",
    "            x1, x2, y1, y2 = _get_random_pos(img_shape, window_shape)\n",
    "    return x1, x2, y1, y2\n",
    "\n",
    "def accuracy(input, target):\n",
    "    return 100 * float(np.count_nonzero(input == target)) / target.size\n",
    "\n",
    "def sliding_window(top, step=10, window_size=(20,20)):\n",
    "    \"\"\" Slide a window_shape window across the image with a stride of step \"\"\"\n",
    "    for x in range(0, top.shape[0], step):\n",
    "        if x + window_size[0] > top.shape[0]:\n",
    "            x = top.shape[0] - window_size[0]\n",
    "        for y in range(0, top.shape[1], step):\n",
    "            if y + window_size[1] > top.shape[1]:\n",
    "                y = top.shape[1] - window_size[1]\n",
    "            yield x, y, window_size[0], window_size[1]\n",
    "            \n",
    "def count_sliding_window(top, step=10, window_size=(20,20)):\n",
    "    \"\"\" Count the number of windows in an image \"\"\"\n",
    "    c = 0\n",
    "    for x in range(0, top.shape[0], step):\n",
    "        if x + window_size[0] > top.shape[0]:\n",
    "            x = top.shape[0] - window_size[0]\n",
    "        for y in range(0, top.shape[1], step):\n",
    "            if y + window_size[1] > top.shape[1]:\n",
    "                y = top.shape[1] - window_size[1]\n",
    "            c += 1\n",
    "    return c\n",
    "\n",
    "def grouper(n, iterable):\n",
    "    \"\"\" Browse an iterator by chunk of n elements \"\"\"\n",
    "    it = iter(iterable)\n",
    "    while True:\n",
    "        chunk = tuple(itertools.islice(it, n))\n",
    "        if not chunk:\n",
    "            return\n",
    "        yield chunk\n",
    "\n",
    "def metrics(predictions, gts, label_values=LABELS):\n",
    "    cm = confusion_matrix(\n",
    "            gts,\n",
    "            predictions,\n",
    "            range(len(label_values)))\n",
    "    \n",
    "    print(\"Confusion matrix :\")\n",
    "    print(cm)\n",
    "    \n",
    "    print(\"---\")\n",
    "    \n",
    "    # Compute global accuracy\n",
    "    total = sum(sum(cm))\n",
    "    accuracy = sum([cm[x][x] for x in range(len(cm))])\n",
    "    accuracy *= 100 / float(total)\n",
    "    print(\"{} pixels processed\".format(total))\n",
    "    print(\"Total accuracy : {}%\".format(accuracy))\n",
    "    \n",
    "    print(\"---\")\n",
    "    \n",
    "    # Compute F1 score\n",
    "    F1Score = np.zeros(len(label_values))\n",
    "    for i in range(len(label_values)):\n",
    "        try:\n",
    "            F1Score[i] = 2. * cm[i,i] / (np.sum(cm[i,:]) + np.sum(cm[:,i]))\n",
    "        except:\n",
    "            # Ignore exception if there is no element in class i for test set\n",
    "            pass\n",
    "    print(\"F1Score :\")\n",
    "    for l_id, score in enumerate(F1Score):\n",
    "        print(\"{}: {}\".format(label_values[l_id], score))\n",
    "\n",
    "    print(\"---\")\n",
    "        \n",
    "    # Compute kappa coefficient\n",
    "    total = np.sum(cm)\n",
    "    pa = np.trace(cm) / float(total)\n",
    "    pe = np.sum(np.sum(cm, axis=0) * np.sum(cm, axis=1)) / float(total*total)\n",
    "    kappa = (pa - pe) / (1 - pe);\n",
    "    print(\"Kappa: \" + str(kappa))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely.ops import transform\n",
    "import rasterio.features\n",
    "from skimage.transform import resize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.debugger import set_trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset class\n",
    "class S2CCI_dataset(torch.utils.data.Dataset):\n",
    "    __data_cache = []\n",
    "    __cache_renewal = 0.005\n",
    "    __cache_size = 10\n",
    "    \n",
    "    def __init__(self, data_files, ground_truth, bands=BANDS, tci=False, window_size=WINDOW_SIZE, cache=True):\n",
    "        super(S2CCI_dataset, self).__init__()\n",
    "        self.cci = rasterio.open(ground_truth)\n",
    "        self.tci = tci\n",
    "        self.bands = bands\n",
    "        self.window_size = window_size\n",
    "        self.data_files = []\n",
    "        self.__preload(data_files)\n",
    "        self.cache = cache\n",
    "        if cache:\n",
    "            while len(self.__data_cache) < self.__cache_size:\n",
    "                self.__data_cache.append(self.load_random())\n",
    "    \n",
    "    def __del__(self):\n",
    "        self.cci.close()\n",
    "    \n",
    "    def __len__(self):\n",
    "        # Default epoch size is 10 000 samples\n",
    "        return 10000\n",
    "    \n",
    "    def __preload(self, data_files):\n",
    "        self.data_files = []\n",
    "        for f in tqdm(data_files):\n",
    "            try:\n",
    "                with s2.open(f) as product:\n",
    "                    print(product.path)\n",
    "                    #shape = rasterio.open(product.granule_paths(2)[0]).shape\n",
    "                    for granule in product.granules:\n",
    "                        if self.tci and granule.tci_path is None:\n",
    "                            print(\"Skipping because no TCI is available\")\n",
    "                            continue\n",
    "                        d = granule.__dict__\n",
    "                        d['shape'] = (10980, 10980) # S2 tile shape at 10m/px\n",
    "                        d['bands'] = [product.granule_paths(b)[0] for b in self.bands.keys()]\n",
    "                        #cci_win, nodata_mask = S2CCI_dataset.get_cci(product, self.cci), S2CCI_dataset.get_nodata(product)\n",
    "                        #self._cache[granule.granule_path] = (cci_win, nodata_mask)\n",
    "                        self.data_files.append(d)\n",
    "            except Exception as e:\n",
    "                print(e)\n",
    "                pass\n",
    "        print(\"Loaded {} data files\".format(len(self.data_files)))\n",
    "    \n",
    "    @staticmethod\n",
    "    def get_nodata(product):\n",
    "        nodata_mask = product.granules[0].nodata_mask\n",
    "        # Open band 1 (60m, to choose the coordinates)\n",
    "        with rasterio.Env(GDAL_CACHEMAX=0) as env, rasterio.open(product.granule_paths(1)[0]) as b1:\n",
    "            project = partial(\n",
    "                pyproj.transform,\n",
    "                pyproj.Proj(init='epsg:4326'), # source coordinate system\n",
    "                pyproj.Proj(init=b1.crs['init'])) # destination coordinate system\n",
    "            if isinstance(nodata_mask, Polygon) and nodata_mask.is_empty:\n",
    "                nodata_mask = np.zeros(b1.shape, dtype='bool')\n",
    "            else:\n",
    "                projected_nodata = transform(project,nodata_mask)\n",
    "                if isinstance(nodata_mask, Polygon):\n",
    "                    projected_nodata = [projected_nodata]\n",
    "                nodata_mask = rasterio.features.rasterize(projected_nodata, out_shape=b1.shape, transform=b1.transform,fill=0)\n",
    "                nodata_mask = nodata_mask.astype('bool')\n",
    "            nodata_mask[b1.read()[0] == 0] = True\n",
    "        return nodata_mask\n",
    "    \n",
    "    @staticmethod\n",
    "    def get_cci(product, cci):\n",
    "        print(\"Generating cloud mask\")\n",
    "        try:\n",
    "            cloud_mask = product.granules[0].cloudmask\n",
    "        except AttributeError:\n",
    "            cloud_mask = None\n",
    "        # Open band 5 (20m, to generate cloud mask)\n",
    "        with rasterio.Env(GDAL_CACHEMAX=0) as env, rasterio.open(product.granule_paths(5)[0]) as b5:\n",
    "            project = partial(\n",
    "                pyproj.transform,\n",
    "                pyproj.Proj(init='epsg:4326'), # source coordinate system\n",
    "                pyproj.Proj(init=b5.crs['init'])) # destination coordinate system\n",
    "            if cloud_mask is None or (isinstance(cloud_mask, Polygon) and cloud_mask.is_empty): # Empty polygon\n",
    "                cloud_mask = np.zeros(b5.shape)\n",
    "            else: # Polygon or Multipolygon\n",
    "                projected_cm = transform(project, cloud_mask)\n",
    "                if isinstance(cloud_mask, Polygon):\n",
    "                    projected_cm = [projected_cm]\n",
    "                cloud_mask = rasterio.features.rasterize(projected_cm, out_shape=b5.shape, transform=b5.transform,fill=0)\n",
    "            print(\"Done\")\n",
    "            cci_crop_coord = project_bbox(b5.crs, cci.crs, b5.bounds)\n",
    "            print(cci_crop_coord)\n",
    "            cci_win = cci.read(window=bounds_window(cci_crop_coord, cci.affine))[0]\n",
    "            print(\"CCI window : {}\".format(cci_win))\n",
    "            print(cci_win.shape, cloud_mask.shape)\n",
    "            cci_win = resize(cci_win, cloud_mask.shape, order=0, preserve_range=True).astype('uint8')\n",
    "            print(cci_win.shape, cloud_mask.shape)\n",
    "            cci_win[cloud_mask > 0] = 11\n",
    "            cci_win[cci_win > 11] = 0\n",
    "            #plt.imshow(cloud_mask > 0) and plt.show()\n",
    "            print(\"CCI window with clouds: {}\".format(cci_win))\n",
    "        return cci_win\n",
    "    \n",
    "    def load_random(self):\n",
    "        res = self.__load_random()\n",
    "        while res is None:\n",
    "            res = self.__load_random()\n",
    "        return res\n",
    "    \n",
    "    def __load_random(self):\n",
    "        # Pick a random image\n",
    "        rand_idx = random.randint(0, len(self.data_files) - 1)\n",
    "        random_granule = self.data_files[rand_idx]\n",
    "        random_path = random_granule['granule_path']\n",
    "        print(\"Looking into \" + random_path)\n",
    "        \n",
    "        try:\n",
    "            with random_granule['dataset'] as product:\n",
    "                cci_win, nodata_mask = S2CCI_dataset.get_cci(product, self.cci), S2CCI_dataset.get_nodata(product)\n",
    "                nodata_mask = resize(nodata_mask, cci_win.shape[:2], preserve_range=True, order=0).astype('bool')\n",
    "                x1, y1, x2, y2 = bounding_box(~nodata_mask)\n",
    "                cci_win = cci_win[x1:x2, y1:y2]\n",
    "                nodata_mask = nodata_mask[x1:x2, y1:y2]\n",
    "                cci_win[nodata_mask] = 0\n",
    "                if np.count_nonzero(cci_win) - np.count_nonzero(cci_win == 11) < 0.5*cci_win.size:\n",
    "                    raise Exception('Not enough data')\n",
    "\n",
    "                if self.tci: # Use true color image only\n",
    "                    print(\"Loading band TCI\")\n",
    "                    x_min, x_max, y_min, y_max = map(lambda x: x * 20 // 10, (x1,x2,y1,y2))\n",
    "                    print(product.granules[0].tci_path)\n",
    "                    with rasterio.Env(GDAL_CACHEMAX=0) as env, rasterio.open(product.granules[0].tci_path) as raster:\n",
    "                        data_window = raster.read(window=((x_min, x_max), (y_min, y_max)))\n",
    "                else:\n",
    "                    x_min, x_max, y_min, y_max = map(lambda x: x * 20 // 10, (x1,x2,y1,y2))\n",
    "                    w, h = x_max-x_min, y_max-y_min\n",
    "                    data_window = np.zeros((len(self.bands), w, h), dtype='uint16')\n",
    "                    for idx, (band, resolution) in enumerate(self.bands.items()):\n",
    "                        print(\"Loading band {}\".format(band))\n",
    "                        #import ipdb; ipdb.set_trace()\n",
    "                        x_min, x_max, y_min, y_max = map(lambda x: x * 20 // resolution, (x1,x2,y1,y2))\n",
    "                        print(x_min, x_max, y_min, y_max)\n",
    "                        with rasterio.Env(GDAL_CACHEMAX=0) as env, rasterio.open(product.granule_paths(band)[0]) as raster:\n",
    "                            data_window[idx] = resize(raster.read(window=((x_min, x_max), (y_min, y_max)))[0], (w,h), order=0, preserve_range=True).astype('uint16', copy=False)\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "            self.data_files.remove(random_granule)\n",
    "            return None\n",
    "        return data_window.astype('uint16', copy=False), cci_win.astype('uint8', copy=False)\n",
    "    \n",
    "    def __getitem__(self, i):\n",
    "\n",
    "        if self.cache:\n",
    "            random_idx = random.randint(0, len(self.__data_cache) - 1)\n",
    "            if random.random() < self.__cache_renewal: # % chance of replacing the data\n",
    "                print(\"Replacing from cache\")\n",
    "                del(self.__data_cache[random_idx])\n",
    "                data, label = self.load_random()\n",
    "                self.__data_cache.append((data, label))\n",
    "            else: # else just use what's in the cache\n",
    "                data, label = self.__data_cache[random_idx]\n",
    "        else:\n",
    "            data, label = self.load_random()\n",
    "\n",
    "        # Get a random patch\n",
    "        w, h = self.window_size\n",
    "        x1, x2, y1, y2 = get_random_pos(label.shape, (w//2, h//2), mask=label)\n",
    "        label_p = label[x1:x2,y1:y2]\n",
    "        data_p = data[:, 2*x1:2*x2,2*y1:2*y2].astype('float32')\n",
    "        data_p /= 10000\n",
    "\n",
    "        # Data augmentation\n",
    "        #data_p, label_p = self.data_augmentation(data_p, label_p)\n",
    "\n",
    "        # Return the torch.Tensor values\n",
    "        return (torch.from_numpy(data_p).float(),\n",
    "                torch.from_numpy(label_p).long())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SegNet(nn.Module):\n",
    "    # SegNet network\n",
    "    @staticmethod\n",
    "    def weight_init(m):\n",
    "        if isinstance(m, (nn.Linear, nn.Conv2d)):\n",
    "            torch.nn.init.kaiming_normal(m.weight.data)\n",
    "    \n",
    "    def __init__(self, in_channels=IN_CHANNELS, out_channels=N_CLASSES):\n",
    "        super(SegNet, self).__init__()\n",
    "        self.pool = nn.MaxPool2d(2, return_indices=True)\n",
    "        self.unpool = nn.MaxUnpool2d(2)\n",
    "        \n",
    "        self.conv1_1 = nn.Conv2d(in_channels, 64, 3, padding=1)\n",
    "        self.conv1_1_bn = nn.BatchNorm2d(64)\n",
    "        self.conv1_2 = nn.Conv2d(64, 64, 3, padding=1)\n",
    "        self.conv1_2_bn = nn.BatchNorm2d(64)\n",
    "        \n",
    "        self.conv2_1 = nn.Conv2d(64, 128, 3, padding=1)\n",
    "        self.conv2_1_bn = nn.BatchNorm2d(128)\n",
    "        self.conv2_2 = nn.Conv2d(128, 128, 3, padding=1)\n",
    "        self.conv2_2_bn = nn.BatchNorm2d(128)\n",
    "        \n",
    "        self.conv3_1 = nn.Conv2d(128, 256, 3, padding=1)\n",
    "        self.conv3_1_bn = nn.BatchNorm2d(256)\n",
    "        self.conv3_2 = nn.Conv2d(256, 256, 3, padding=1)\n",
    "        self.conv3_2_bn = nn.BatchNorm2d(256)\n",
    "        self.conv3_3 = nn.Conv2d(256, 256, 3, padding=1)\n",
    "        self.conv3_3_bn = nn.BatchNorm2d(256)\n",
    "        \n",
    "        self.conv4_1 = nn.Conv2d(256, 512, 3, padding=1)\n",
    "        self.conv4_1_bn = nn.BatchNorm2d(512)\n",
    "        self.conv4_2 = nn.Conv2d(512, 512, 3, padding=1)\n",
    "        self.conv4_2_bn = nn.BatchNorm2d(512)\n",
    "        self.conv4_3 = nn.Conv2d(512, 512, 3, padding=1)\n",
    "        self.conv4_3_bn = nn.BatchNorm2d(512)\n",
    "        \n",
    "        self.conv5_1 = nn.Conv2d(512, 512, 3, padding=1)\n",
    "        self.conv5_1_bn = nn.BatchNorm2d(512)\n",
    "        self.conv5_2 = nn.Conv2d(512, 512, 3, padding=1)\n",
    "        self.conv5_2_bn = nn.BatchNorm2d(512)\n",
    "        self.conv5_3 = nn.Conv2d(512, 512, 3, padding=1)\n",
    "        self.conv5_3_bn = nn.BatchNorm2d(512)\n",
    "        \n",
    "        self.conv5_3_D = nn.Conv2d(512, 512, 3, padding=1)\n",
    "        self.conv5_3_D_bn = nn.BatchNorm2d(512)\n",
    "        self.conv5_2_D = nn.Conv2d(512, 512, 3, padding=1)\n",
    "        self.conv5_2_D_bn = nn.BatchNorm2d(512)\n",
    "        self.conv5_1_D = nn.Conv2d(512, 512, 3, padding=1)\n",
    "        self.conv5_1_D_bn = nn.BatchNorm2d(512)\n",
    "        \n",
    "        self.conv4_3_D = nn.Conv2d(512, 512, 3, padding=1)\n",
    "        self.conv4_3_D_bn = nn.BatchNorm2d(512)\n",
    "        self.conv4_2_D = nn.Conv2d(512, 512, 3, padding=1)\n",
    "        self.conv4_2_D_bn = nn.BatchNorm2d(512)\n",
    "        self.conv4_1_D = nn.Conv2d(512, 256, 3, padding=1)\n",
    "        self.conv4_1_D_bn = nn.BatchNorm2d(256)\n",
    "        \n",
    "        self.conv3_3_D = nn.Conv2d(256, 256, 3, padding=1)\n",
    "        self.conv3_3_D_bn = nn.BatchNorm2d(256)\n",
    "        self.conv3_2_D = nn.Conv2d(256, 256, 3, padding=1)\n",
    "        self.conv3_2_D_bn = nn.BatchNorm2d(256)\n",
    "        self.conv3_1_D = nn.Conv2d(256, 128, 3, padding=1)\n",
    "        self.conv3_1_D_bn = nn.BatchNorm2d(128)\n",
    "        \n",
    "        self.conv2_2_D = nn.Conv2d(128, 128, 3, padding=1)\n",
    "        self.conv2_2_D_bn = nn.BatchNorm2d(128)\n",
    "        self.conv2_1_D = nn.Conv2d(128, 64, 3, padding=1)\n",
    "        self.conv2_1_D_bn = nn.BatchNorm2d(64)\n",
    "        \n",
    "        self.conv1_2_D = nn.Conv2d(64, 64, 3, padding=1)\n",
    "        self.conv1_2_D_bn = nn.BatchNorm2d(64)\n",
    "        self.conv1_1_D = nn.Conv2d(64, out_channels, 3, padding=1)\n",
    "        \n",
    "        self.apply(self.weight_init)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # Encoder block 1\n",
    "        x = self.conv1_1_bn(F.relu(self.conv1_1(x)))\n",
    "        x = self.conv1_2_bn(F.relu(self.conv1_2(x)))\n",
    "        x, mask1 = self.pool(x)\n",
    "        \n",
    "        # Encoder block 2\n",
    "        x = self.conv2_1_bn(F.relu(self.conv2_1(x)))\n",
    "        x = self.conv2_2_bn(F.relu(self.conv2_2(x)))\n",
    "        x, mask2 = self.pool(x)\n",
    "        \n",
    "        # Encoder block 3\n",
    "        x = self.conv3_1_bn(F.relu(self.conv3_1(x)))\n",
    "        x = self.conv3_2_bn(F.relu(self.conv3_2(x)))\n",
    "        x = self.conv3_3_bn(F.relu(self.conv3_3(x)))\n",
    "        x, mask3 = self.pool(x)\n",
    "        \n",
    "        # Encoder block 4\n",
    "        x = self.conv4_1_bn(F.relu(self.conv4_1(x)))\n",
    "        x = self.conv4_2_bn(F.relu(self.conv4_2(x)))\n",
    "        x = self.conv4_3_bn(F.relu(self.conv4_3(x)))\n",
    "        x, mask4 = self.pool(x)\n",
    "        \n",
    "        # Encoder block 5\n",
    "        x = self.conv5_1_bn(F.relu(self.conv5_1(x)))\n",
    "        x = self.conv5_2_bn(F.relu(self.conv5_2(x)))\n",
    "        x = self.conv5_3_bn(F.relu(self.conv5_3(x)))\n",
    "        x, mask5 = self.pool(x)\n",
    "        \n",
    "        # Decoder block 5\n",
    "        x = self.unpool(x, mask5)\n",
    "        x = self.conv5_3_D_bn(F.relu(self.conv5_3_D(x)))\n",
    "        x = self.conv5_2_D_bn(F.relu(self.conv5_2_D(x)))\n",
    "        x = self.conv5_1_D_bn(F.relu(self.conv5_1_D(x)))\n",
    "        \n",
    "        # Decoder block 4\n",
    "        x = self.unpool(x, mask4)\n",
    "        x = self.conv4_3_D_bn(F.relu(self.conv4_3_D(x)))\n",
    "        x = self.conv4_2_D_bn(F.relu(self.conv4_2_D(x)))\n",
    "        x = self.conv4_1_D_bn(F.relu(self.conv4_1_D(x)))\n",
    "        \n",
    "        # Decoder block 3\n",
    "        x = self.unpool(x, mask3)\n",
    "        x = self.conv3_3_D_bn(F.relu(self.conv3_3_D(x)))\n",
    "        x = self.conv3_2_D_bn(F.relu(self.conv3_2_D(x)))\n",
    "        x = self.conv3_1_D_bn(F.relu(self.conv3_1_D(x)))\n",
    "        \n",
    "        # Decoder block 2\n",
    "        x = self.unpool(x, mask2)\n",
    "        x = self.conv2_2_D_bn(F.relu(self.conv2_2_D(x)))\n",
    "        x = self.conv2_1_D_bn(F.relu(self.conv2_1_D(x)))\n",
    "        \n",
    "        # Decoder block 1\n",
    "        #x = self.unpool(x, mask1)\n",
    "        x = self.conv1_2_D_bn(F.relu(self.conv1_2_D(x)))\n",
    "        x = self.conv1_1_D(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/naudebert/.anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# instantiate the network\n",
    "net = SegNet()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We download and load the pre-trained weights from VGG-16 on ImageNet. This step is optional but it makes the network converge faster. We skip the weights from VGG-16 that have no counterpart in SegNet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "if PRETRAINED:\n",
    "    import os\n",
    "    try:\n",
    "        from urllib.request import URLopener\n",
    "    except ImportError:\n",
    "        from urllib import URLopener\n",
    "\n",
    "    # Download VGG-16 weights from PyTorch\n",
    "    vgg_url = 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth'\n",
    "    if not os.path.isfile('./vgg16_bn-6c64b313.pth'):\n",
    "        weights = URLopener().retrieve(vgg_url, './vgg16_bn-6c64b313.pth')\n",
    "\n",
    "    vgg16_weights = torch.load('./vgg16_bn-6c64b313.pth')\n",
    "    mapped_weights = {}\n",
    "    for k_vgg, k_segnet in zip(vgg16_weights.keys(), net.state_dict().keys()):\n",
    "        if \"features\" in k_vgg:\n",
    "            mapped_weights[k_segnet] = vgg16_weights[k_vgg]\n",
    "            print(\"Mapping {} to {}\".format(k_vgg, k_segnet))\n",
    "\n",
    "    try:\n",
    "        net.load_state_dict(mapped_weights)\n",
    "        print(\"Loaded VGG-16 weights in SegNet !\")\n",
    "    except:\n",
    "        # Ignore missing keys\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we load the network on GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SegNet(\n",
       "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (unpool): MaxUnpool2d(kernel_size=(2, 2), stride=(2, 2), padding=(0, 0))\n",
       "  (conv1_1): Conv2d(13, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv1_1_bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv1_2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv1_2_bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv2_1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv2_1_bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv2_2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv2_2_bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv3_1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3_1_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv3_2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3_2_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv3_3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3_3_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv4_1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4_1_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv4_2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4_2_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv4_3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4_3_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv5_1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5_1_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv5_2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5_2_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv5_3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5_3_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv5_3_D): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5_3_D_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv5_2_D): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5_2_D_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv5_1_D): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5_1_D_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv4_3_D): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4_3_D_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv4_2_D): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4_2_D_bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv4_1_D): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4_1_D_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv3_3_D): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3_3_D_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv3_2_D): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3_2_D_bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv3_1_D): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3_1_D_bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv2_2_D): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv2_2_D_bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv2_1_D): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv2_1_D_bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv1_2_D): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv1_2_D_bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (conv1_1_D): Conv2d(64, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       ")"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20151217T142523_R106_V20151217T073600_20151217T073600.SAFE', '/home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20151218T170746_R121_V20151218T084420_20151218T084420.SAFE']\n"
     ]
    }
   ],
   "source": [
    "print(urls[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "79b9bc8514fb4597a2b855bac766325d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=35), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20151217T142523_R106_V20151217T073600_20151217T073600.SAFE\n",
      "/home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20151218T170746_R121_V20151218T084420_20151218T084420.SAFE\n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-23e92173789d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mS2CCI_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'../ESA_CCI_African_LandCover_20m/ESACCI-LC-L4-LC10-Map-20m-P1Y-2016-v1.0.tif'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtrain_loader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_set\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-10-8443bd6036be>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data_files, ground_truth, bands, tci, window_size, cache)\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwindow_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwindow_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_files\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__preload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_files\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-10-8443bd6036be>\u001b[0m in \u001b[0;36m__preload\u001b[0;34m(self, data_files)\u001b[0m\n\u001b[1;32m     38\u001b[0m                         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgranule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m                         \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'shape'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m10980\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10980\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# S2 tile shape at 10m/px\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m                         \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'bands'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mproduct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgranule_paths\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbands\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m                         \u001b[0;31m#cci_win, nodata_mask = S2CCI_dataset.get_cci(product, self.cci), S2CCI_dataset.get_nodata(product)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m                         \u001b[0;31m#self._cache[granule.granule_path] = (cci_win, nodata_mask)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-10-8443bd6036be>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     38\u001b[0m                         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgranule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m                         \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'shape'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m10980\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10980\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# S2 tile shape at 10m/px\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m                         \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'bands'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mproduct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgranule_paths\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbands\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m                         \u001b[0;31m#cci_win, nodata_mask = S2CCI_dataset.get_cci(product, self.cci), S2CCI_dataset.get_nodata(product)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m                         \u001b[0;31m#self._cache[granule.granule_path] = (cci_win, nodata_mask)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/DeepNetsForEO/s2reader/s2reader/s2reader.py\u001b[0m in \u001b[0;36mgranule_paths\u001b[0;34m(self, band_id)\u001b[0m\n\u001b[1;32m    216\u001b[0m         return [\n\u001b[1;32m    217\u001b[0m             \u001b[0mgranule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mband_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mband_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mgranule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgranules\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    219\u001b[0m             ]\n\u001b[1;32m    220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/DeepNetsForEO/s2reader/s2reader/s2reader.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    216\u001b[0m         return [\n\u001b[1;32m    217\u001b[0m             \u001b[0mgranule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mband_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mband_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mgranule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgranules\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    219\u001b[0m             ]\n\u001b[1;32m    220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/DeepNetsForEO/s2reader/s2reader/s2reader.py\u001b[0m in \u001b[0;36mband_path\u001b[0;34m(self, band_id, for_gdal, absolute)\u001b[0m\n\u001b[1;32m    386\u001b[0m         granule_item = [\n\u001b[1;32m    387\u001b[0m             \u001b[0mg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mchain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgl\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mgl\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mproduct_org\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Granule_List\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgranule_identifier\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrib\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"granuleIdentifier\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m         ]\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "train_set = S2CCI_dataset(urls, '../ESA_CCI_African_LandCover_20m/ESACCI-LC-L4-LC10-Map-20m-P1Y-2016-v1.0.tif')\n",
    "train_loader = torch.utils.data.DataLoader(train_set,batch_size=BATCH_SIZE, num_workers=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Designing the optimizer\n",
    "\n",
    "We use the standard Stochastic Gradient Descent algorithm to optimize the network's weights.\n",
    "\n",
    "The encoder is trained at half the learning rate of the decoder, as we rely on the pre-trained VGG-16 weights. We use the ``torch.optim.lr_scheduler`` to reduce the learning rate by 10 after 25, 35 and 45 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_lr = 0.01\n",
    "params_dict = dict(net.named_parameters())\n",
    "params = []\n",
    "for key, value in params_dict.items():\n",
    "    if '_D' in key:\n",
    "        # Decoder weights are trained at the nominal learning rate\n",
    "        params += [{'params':[value],'lr': base_lr}]\n",
    "    else:\n",
    "        # Encoder weights are trained at lr / 2 (we have VGG-16 weights as initialization)\n",
    "        params += [{'params':[value],'lr': base_lr / 2}]\n",
    "\n",
    "optimizer = optim.SGD(net.parameters(), lr=base_lr, momentum=0.9, weight_decay=0.0005)\n",
    "#optimizer = optim.Adam(net.parameters(), lr=base_lr, weight_decay=0.0005, amsgrad=True)\n",
    "# We define the scheduler\n",
    "scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [100, 150], gamma=0.1)\n",
    "#scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [50, 75, 90], gamma=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import clear_output\n",
    "\n",
    "def train(net, optimizer, epochs, scheduler=None, weights=WEIGHTS, save_epoch = 5):\n",
    "    losses = np.zeros(1000000)\n",
    "    mean_losses = np.zeros(1000000)\n",
    "    weights = weights.cuda()\n",
    "    iter_ = 0\n",
    "    \n",
    "    for e in range(1, epochs + 1):\n",
    "        if scheduler is not None:\n",
    "            scheduler.step()\n",
    "        net.train()\n",
    "        for batch_idx, (data, target) in enumerate(train_loader):\n",
    "            data, target = Variable(data.cuda()), Variable(target.cuda())\n",
    "            optimizer.zero_grad()\n",
    "            output = net(data)\n",
    "            loss = F.cross_entropy(output, target, weight=weights)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            losses[iter_] = loss.item()\n",
    "            mean_losses[iter_] = np.mean(losses[max(0,iter_-100):iter_])\n",
    "            \n",
    "            if iter_ % 100 == 0:\n",
    "                clear_output()\n",
    "                rgb = get_rgb(data.cpu().numpy()[0])\n",
    "                pred = np.argmax(output.data.cpu().numpy()[0], axis=0)\n",
    "                gt = target.data.cpu().numpy()[0]\n",
    "                print('Train (epoch {}/{}) [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\tAccuracy: {}'.format(\n",
    "                    e, epochs, batch_idx, len(train_loader),\n",
    "                    100. * batch_idx / len(train_loader), loss.data[0], accuracy(pred, gt)))\n",
    "                plt.plot(mean_losses[:iter_]) and plt.show()\n",
    "                fig = plt.figure()\n",
    "                fig.add_subplot(131)\n",
    "                plt.imshow(rgb)\n",
    "                plt.title('RGB')\n",
    "                fig.add_subplot(132)\n",
    "                plt.imshow(convert_to_color(gt))\n",
    "                plt.title('Ground truth')\n",
    "                fig.add_subplot(133)\n",
    "                plt.title('Prediction')\n",
    "                plt.imshow(convert_to_color(pred))\n",
    "                plt.show()\n",
    "            iter_ += 1\n",
    "            \n",
    "            del(data, target, loss)\n",
    "            \n",
    "        if e % save_epoch == 0:\n",
    "            # We validate with the largest possible stride for faster computing\n",
    "            #acc = test(net, test_ids, all=False, stride=min(WINDOW_SIZE))\n",
    "            acc = 0.\n",
    "            torch.save(net.state_dict(), './segnet256_epoch{}_{}'.format(e, acc))\n",
    "    torch.save(net.state_dict(), './segnet_final')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the network\n",
    "\n",
    "Let's train the network for 50 epochs. The `matplotlib` graph is periodically udpated with the loss plot and a sample inference. Depending on your GPU, this might take from a few hours (Titan Pascal) to a full day (old K20)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train (epoch 1/200) [300/1000 (30%)]\tLoss: 0.911221\tAccuracy: 95.93942901234568\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replacing from cache\n",
      "Looking into /home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20151223T021840_R035_V20151222T081758_20151222T081758.SAFE/GRANULE/S2A_OPER_MSI_L1C_TL_MTI__20151222T101055_A002606_T37PBP_N02.01\n",
      "Generating cloud mask\n",
      "Done\n",
      "(32.999816437010196, 10.765404630173208, 34.00752284452154, 11.760044027808236)\n",
      "CCI window : [[4 4 4 ... 3 3 3]\n",
      " [4 4 4 ... 3 3 3]\n",
      " [4 4 4 ... 3 3 3]\n",
      " ...\n",
      " [3 3 3 ... 2 2 3]\n",
      " [3 3 3 ... 2 2 2]\n",
      " [3 3 3 ... 2 2 2]]\n",
      "(5372, 5443) (5490, 5490)\n",
      "(5490, 5490) (5490, 5490)\n",
      "CCI window with clouds: [[4 4 4 ... 3 3 3]\n",
      " [4 4 4 ... 3 3 3]\n",
      " [4 4 4 ... 3 3 3]\n",
      " ...\n",
      " [3 3 3 ... 2 2 3]\n",
      " [3 3 3 ... 2 2 2]\n",
      " [3 3 3 ... 2 2 2]]\n",
      "Loading band 1\n",
      "0 1829 912 1829\n",
      "Loading band 2\n",
      "0 10978 5472 10978\n",
      "Loading band 3\n",
      "0 10978 5472 10978\n",
      "Loading band 4\n",
      "0 10978 5472 10978\n",
      "Loading band 5\n",
      "0 5489 2736 5489\n",
      "Loading band 6\n",
      "0 5489 2736 5489\n",
      "Loading band 7\n",
      "0 5489 2736 5489\n",
      "Loading band 8\n",
      "0 10978 5472 10978\n",
      "Loading band 8A\n",
      "0 5489 2736 5489\n",
      "Loading band 9\n",
      "0 1829 912 1829\n",
      "Loading band 10\n",
      "0 1829 912 1829\n",
      "Loading band 11\n",
      "0 5489 2736 5489\n",
      "Loading band 12\n",
      "0 5489 2736 5489\n",
      "Replacing from cache\n",
      "Looking into /home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20151224T145956_R063_V20151224T072025_20151224T072025.SAFE/GRANULE/S2A_OPER_MSI_L1C_TL_SGS__20151224T105828_A002634_T39NUB_N02.01\n",
      "Generating cloud mask\n",
      "Done\n",
      "(45.898638724445924, -0.08847177100919484, 46.88531054969977, 0.9047995023032807)\n",
      "CCI window : [[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n",
      "(5364, 5329) (5490, 5490)\n",
      "(5490, 5490) (5490, 5490)\n",
      "CCI window with clouds: [[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n",
      "Not enough data\n",
      "Looking into /home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20151229T153055_R135_V20151229T081422_20151229T081422.SAFE/GRANULE/S2A_OPER_MSI_L1C_TL_SGS__20151229T114601_A002706_T36MWA_N02.01\n",
      "Generating cloud mask\n",
      "Done\n",
      "(32.09810273469394, -4.611853289812001, 33.0879936236692, -3.6180663265462494)\n",
      "CCI window : [[2 4 4 ... 4 4 4]\n",
      " [2 2 4 ... 4 4 4]\n",
      " [1 2 2 ... 4 4 4]\n",
      " ...\n",
      " [4 2 2 ... 2 2 4]\n",
      " [4 4 2 ... 2 2 2]\n",
      " [4 4 4 ... 2 2 2]]\n",
      "(5367, 5347) (5490, 5490)\n",
      "(5490, 5490) (5490, 5490)\n",
      "CCI window with clouds: [[2 4 4 ... 4 4 4]\n",
      " [2 2 4 ... 4 4 4]\n",
      " [1 2 2 ... 4 4 4]\n",
      " ...\n",
      " [4 2 2 ... 2 2 4]\n",
      " [4 4 2 ... 2 2 2]\n",
      " [4 4 4 ... 2 2 2]]\n",
      "Not enough data\n",
      "Looking into /home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20151227T143338_R106_V20151227T073351_20151227T073351.SAFE/GRANULE/S2A_OPER_MSI_L1C_TL_SGS__20151227T110132_A002677_T38MLB_N02.01\n",
      "Generating cloud mask\n",
      "Done\n",
      "(41.7031950592368, -5.510539806964896, 42.697002469198495, -4.5139729726073865)\n",
      "CCI window : [[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n",
      "(5382, 5367) (5490, 5490)\n",
      "(5490, 5490) (5490, 5490)\n",
      "CCI window with clouds: [[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n",
      "Not enough data\n",
      "Looking into /home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20151230T163414_R006_V20151230T073929_20151230T073929.SAFE/GRANULE/S2A_OPER_MSI_L1C_TL_SGS__20151230T111307_A002720_T37NHH_N02.01\n",
      "Generating cloud mask\n",
      "Done\n",
      "(41.706493502980614, 5.328635058318423, 42.70242171928553, 6.325963636514764)\n",
      "CCI window : [[3 3 3 ... 2 2 2]\n",
      " [3 3 3 ... 2 2 2]\n",
      " [4 2 2 ... 2 2 2]\n",
      " ...\n",
      " [3 3 3 ... 4 4 4]\n",
      " [3 3 3 ... 3 4 4]\n",
      " [3 3 3 ... 3 4 4]]\n",
      "(5387, 5379) (5490, 5490)\n",
      "(5490, 5490) (5490, 5490)\n",
      "CCI window with clouds: [[3 3 3 ... 2 2 2]\n",
      " [3 3 3 ... 2 2 2]\n",
      " [4 2 2 ... 2 2 2]\n",
      " ...\n",
      " [3 3 3 ... 4 4 4]\n",
      " [3 3 3 ... 3 4 4]\n",
      " [3 3 3 ... 3 4 4]]\n",
      "Loading band 1\n",
      "0 1829 1233 1829\n",
      "Loading band 2\n",
      "0 10978 7398 10978\n",
      "Loading band 3\n",
      "0 10978 7398 10978\n",
      "Loading band 4\n",
      "0 10978 7398 10978\n",
      "Loading band 5\n",
      "0 5489 3699 5489\n",
      "Loading band 6\n",
      "0 5489 3699 5489\n",
      "Loading band 7\n",
      "0 5489 3699 5489\n",
      "Loading band 8\n",
      "0 10978 7398 10978\n",
      "Loading band 8A\n",
      "0 5489 3699 5489\n",
      "Loading band 9\n",
      "0 1829 1233 1829\n",
      "Loading band 10\n",
      "0 1829 1233 1829\n",
      "Loading band 11\n",
      "0 5489 3699 5489\n",
      "Loading band 12\n",
      "0 5489 3699 5489\n",
      "Replacing from cache\n",
      "Looking into /home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20151218T170746_R121_V20151218T084420_20151218T084420.SAFE/GRANULE/S2A_OPER_MSI_L1C_TL_SGS__20151218T122110_A002549_T36PTR_N02.01\n",
      "Generating cloud mask\n",
      "Done\n",
      "(26.99981801611544, 8.05235960109292, 27.998857367364227, 9.046743365076452)\n",
      "CCI window : [[2 2 2 ... 3 3 3]\n",
      " [2 4 4 ... 3 3 3]\n",
      " [2 4 4 ... 3 3 3]\n",
      " ...\n",
      " [1 1 1 ... 1 1 2]\n",
      " [1 1 1 ... 1 2 2]\n",
      " [1 1 1 ... 1 2 2]]\n",
      "(5371, 5396) (5490, 5490)\n",
      "(5490, 5490) (5490, 5490)\n",
      "CCI window with clouds: [[2 2 2 ... 3 3 3]\n",
      " [2 4 4 ... 3 3 3]\n",
      " [2 4 4 ... 3 3 3]\n",
      " ...\n",
      " [1 1 1 ... 1 1 2]\n",
      " [1 1 1 ... 1 2 2]\n",
      " [1 1 1 ... 1 2 2]]\n",
      "Not enough data\n",
      "Looking into /home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20151229T153334_R135_V20151229T081422_20151229T081422.SAFE/GRANULE/S2A_OPER_MSI_L1C_TL_SGS__20151229T114601_A002706_T36MYD_N02.01\n",
      "Generating cloud mask\n",
      "Done\n",
      "(32.99982005124601, -2.8026544009499745, 33.987689344391114, -1.8090056811827624)\n",
      "CCI window : [[10 10 10 ...  4  4  2]\n",
      " [10 10 10 ...  4  4  2]\n",
      " [10 10 10 ...  4  4  4]\n",
      " ...\n",
      " [ 2  2  2 ...  8  3  8]\n",
      " [ 2  2  2 ...  8  8  8]\n",
      " [ 4  2  2 ...  8  8  8]]\n",
      "(5367, 5336) (5490, 5490)\n",
      "(5490, 5490) (5490, 5490)\n",
      "CCI window with clouds: [[10 10 10 ...  4  4  2]\n",
      " [10 10 10 ...  4  4  2]\n",
      " [10 10 10 ...  4  4  4]\n",
      " ...\n",
      " [ 2  2  2 ...  8  3  8]\n",
      " [ 2  2  2 ...  8  8  8]\n",
      " [ 4  2  2 ...  8  8  8]]\n",
      "Loading band 1\n",
      "0 1829 62 1829\n",
      "Loading band 2\n",
      "0 10978 372 10978\n",
      "Loading band 3\n",
      "0 10978 372 10978\n",
      "Loading band 4\n",
      "0 10978 372 10978\n",
      "Loading band 5\n",
      "0 5489 186 5489\n",
      "Loading band 6\n",
      "0 5489 186 5489\n",
      "Loading band 7\n",
      "0 5489 186 5489\n",
      "Loading band 8\n",
      "0 10978 372 10978\n",
      "Loading band 8A\n",
      "0 5489 186 5489\n",
      "Loading band 9\n",
      "0 1829 62 1829\n",
      "Loading band 10\n",
      "0 1829 62 1829\n",
      "Loading band 11\n",
      "0 5489 186 5489\n",
      "Loading band 12\n",
      "0 5489 186 5489\n",
      "Replacing from cache\n",
      "Looking into /home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20151219T163650_R135_V20151219T081647_20151219T081647.SAFE/GRANULE/S2A_OPER_MSI_L1C_TL_SGS__20151219T114135_A002563_T37PDK_N02.01\n",
      "Generating cloud mask\n",
      "Done\n",
      "(34.81445884289826, 8.043948211356376, 35.817394952355585, 9.04222968127126)\n",
      "CCI window : [[1 1 1 ... 1 1 1]\n",
      " [1 1 1 ... 1 1 1]\n",
      " [1 1 1 ... 1 1 1]\n",
      " ...\n",
      " [1 1 1 ... 1 1 1]\n",
      " [1 1 1 ... 1 1 1]\n",
      " [1 1 1 ... 1 1 1]]\n",
      "(5391, 5417) (5490, 5490)\n",
      "(5490, 5490) (5490, 5490)\n",
      "CCI window with clouds: [[ 1  1  1 ...  1  1  1]\n",
      " [ 1  1  1 ...  1  1  1]\n",
      " [ 1  1  1 ...  1  1  1]\n",
      " ...\n",
      " [ 1  1  1 ... 11 11 11]\n",
      " [ 1  1  1 ... 11 11 11]\n",
      " [ 1  1  1 ... 11 11 11]]\n",
      "Loading band 1\n",
      "0 1829 1112 1829\n",
      "Loading band 2\n",
      "0 10978 6672 10978\n",
      "Loading band 3\n",
      "0 10978 6672 10978\n",
      "Loading band 4\n",
      "0 10978 6672 10978\n",
      "Loading band 5\n",
      "0 5489 3336 5489\n",
      "Loading band 6\n",
      "0 5489 3336 5489\n",
      "Loading band 7\n",
      "0 5489 3336 5489\n",
      "Loading band 8\n",
      "0 10978 6672 10978\n",
      "Loading band 8A\n",
      "0 5489 3336 5489\n",
      "Loading band 9\n",
      "0 1829 1112 1829\n",
      "Loading band 10\n",
      "0 1829 1112 1829\n",
      "Loading band 11\n",
      "0 5489 3336 5489\n",
      "Loading band 12\n",
      "0 5489 3336 5489\n",
      "Replacing from cache\n",
      "Looking into /home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20151229T154525_R135_V20151229T081422_20151229T081422.SAFE/GRANULE/S2A_OPER_MSI_L1C_TL_SGS__20151229T114601_A002706_T37NBD_N02.01\n",
      "Generating cloud mask\n",
      "Done\n",
      "(33.899575633553205, 2.624261411230142, 34.888816018284274, 3.6186094351190454)\n",
      "CCI window : [[3 3 3 ... 2 2 2]\n",
      " [3 3 3 ... 2 2 2]\n",
      " [2 3 3 ... 2 2 2]\n",
      " ...\n",
      " [3 3 3 ... 2 2 2]\n",
      " [3 3 3 ... 2 2 2]\n",
      " [3 3 3 ... 2 2 2]]\n",
      "(5371, 5343) (5490, 5490)\n",
      "(5490, 5490) (5490, 5490)\n",
      "CCI window with clouds: [[3 3 3 ... 2 2 2]\n",
      " [3 3 3 ... 2 2 2]\n",
      " [2 3 3 ... 2 2 2]\n",
      " ...\n",
      " [3 3 3 ... 2 2 2]\n",
      " [3 3 3 ... 2 2 2]\n",
      " [3 3 3 ... 2 2 2]]\n",
      "Loading band 1\n",
      "0 1829 612 1829\n",
      "Loading band 2\n",
      "0 10978 3672 10978\n",
      "Loading band 3\n",
      "0 10978 3672 10978\n",
      "Loading band 4\n",
      "0 10978 3672 10978\n",
      "Loading band 5\n",
      "0 5489 1836 5489\n",
      "Loading band 6\n",
      "0 5489 1836 5489\n",
      "Loading band 7\n",
      "0 5489 1836 5489\n",
      "Loading band 8\n",
      "0 10978 3672 10978\n",
      "Loading band 8A\n",
      "0 5489 1836 5489\n",
      "Loading band 9\n",
      "0 1829 612 1829\n",
      "Loading band 10\n",
      "0 1829 612 1829\n",
      "Loading band 11\n",
      "0 5489 1836 5489\n",
      "Loading band 12\n",
      "0 5489 1836 5489\n",
      "Replacing from cache\n",
      "Looking into /home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20160101T162404_R035_V20160101T082628_20160101T082628.SAFE/GRANULE/S2A_OPER_MSI_L1C_TL_SGS__20160101T115610_A002749_T36NWJ_N02.01\n",
      "Generating cloud mask\n",
      "Done\n",
      "(31.1993826817371, 2.6243919699767395, 32.18857650988357, 3.618693063366469)\n",
      "CCI window : [[1 1 1 ... 2 2 2]\n",
      " [1 1 1 ... 2 2 2]\n",
      " [1 1 1 ... 2 2 2]\n",
      " ...\n",
      " [3 3 3 ... 4 4 4]\n",
      " [3 3 3 ... 4 4 4]\n",
      " [3 3 3 ... 4 4 4]]\n",
      "(5370, 5343) (5490, 5490)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5490, 5490) (5490, 5490)\n",
      "CCI window with clouds: [[1 1 1 ... 2 2 2]\n",
      " [1 1 1 ... 2 2 2]\n",
      " [1 1 1 ... 2 2 2]\n",
      " ...\n",
      " [3 3 3 ... 4 4 4]\n",
      " [3 3 3 ... 4 4 4]\n",
      " [3 3 3 ... 4 4 4]]\n",
      "Loading band 1\n",
      "0 1829 946 1829\n",
      "Loading band 2\n",
      "0 10978 5676 10978\n",
      "Loading band 3\n",
      "0 10978 5676 10978\n",
      "Loading band 4\n",
      "0 10978 5676 10978\n",
      "Loading band 5\n",
      "0 5489 2838 5489\n",
      "Loading band 6\n",
      "0 5489 2838 5489\n",
      "Loading band 7\n",
      "0 5489 2838 5489\n",
      "Loading band 8\n",
      "0 10978 5676 10978\n",
      "Loading band 8A\n",
      "0 5489 2838 5489\n",
      "Loading band 9\n",
      "0 1829 946 1829\n",
      "Loading band 10\n",
      "0 1829 946 1829\n",
      "Loading band 11\n",
      "0 5489 2838 5489\n",
      "Loading band 12\n",
      "0 5489 2838 5489\n",
      "Replacing from cache\n",
      "Looking into /home/naudebert/east_africa/S2A_OPER_PRD_MSIL1C_PDMC_20151229T153334_R135_V20151229T081422_20151229T081422.SAFE/GRANULE/S2A_OPER_MSI_L1C_TL_SGS__20151229T114601_A002706_T36MWE_N02.01\n",
      "Generating cloud mask\n",
      "Done\n",
      "(32.99982005124601, -2.8026544009499745, 33.987689344391114, -1.8090056811827624)\n",
      "CCI window : [[10 10 10 ...  4  4  2]\n",
      " [10 10 10 ...  4  4  2]\n",
      " [10 10 10 ...  4  4  4]\n",
      " ...\n",
      " [ 2  2  2 ...  8  3  8]\n",
      " [ 2  2  2 ...  8  8  8]\n",
      " [ 4  2  2 ...  8  8  8]]\n",
      "(5367, 5336) (5490, 5490)\n",
      "(5490, 5490) (5490, 5490)\n",
      "CCI window with clouds: [[10 10 10 ...  4  4  2]\n",
      " [10 10 10 ...  4  4  2]\n",
      " [10 10 10 ...  4  4  4]\n",
      " ...\n",
      " [ 2  2  2 ...  8  3  8]\n",
      " [ 2  2  2 ...  8  8  8]\n",
      " [ 4  2  2 ...  8  8  8]]\n",
      "Loading band 1\n",
      "0 1829 62 1829\n",
      "Loading band 2\n",
      "0 10978 372 10978\n"
     ]
    }
   ],
   "source": [
    "train(net, optimizer, 200, scheduler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = S2CCI_dataset.__dict__['_S2CCI_dataset__data_cache']\n",
    "print(len(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "299\n"
     ]
    }
   ],
   "source": [
    "print(len(train_set.data_files))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.load_state_dict(torch.load('./segnet256_epoch155_0.0'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(net, products, cci, stride=WINDOW_SIZE[0]//2, bands=BANDS, batch_size=BATCH_SIZE, window_size=WINDOW_SIZE):  \n",
    "    # Switch the network to inference mode\n",
    "    net.eval()\n",
    "    with torch.no_grad():\n",
    "        for i, product in enumerate(products):\n",
    "            try:\n",
    "                product = s2.open(product)\n",
    "                cci_win, nodata_mask = S2CCI_dataset.get_cci(product, cci), S2CCI_dataset.get_nodata(product)\n",
    "                nodata_mask = resize(nodata_mask, cci_win.shape[:2], preserve_range=True, order=0).astype('bool')\n",
    "                x1, y1, x2, y2 = bounding_box(~nodata_mask)\n",
    "                cci_win = cci_win[x1:x2, y1:y2]\n",
    "                nodata_mask = nodata_mask[x1:x2, y1:y2]\n",
    "                cci_win[nodata_mask] = 0\n",
    "                if np.count_nonzero(cci_win) - np.count_nonzero(cci_win == 11) < 0.5*cci_win.size:\n",
    "                    raise Exception('Not enough data')\n",
    "\n",
    "                if TCI: # Use true color image only\n",
    "                    print(\"Loading band TCI\")\n",
    "                    x_min, x_max, y_min, y_max = map(lambda x: x * 20 // 10, (x1,x2,y1,y2))\n",
    "                    print(product.granules[0].tci_path)\n",
    "                    data_window = rasterio.open(product.granules[0].tci_path).read(window=((x_min, x_max), (y_min, y_max)))\n",
    "                else:\n",
    "                    x_min, x_max, y_min, y_max = map(lambda x: x * 20 // 10, (x1,x2,y1,y2))\n",
    "                    w, h = x_max-x_min, y_max-y_min\n",
    "                    data_window = np.zeros((len(bands), w, h), dtype='float32')\n",
    "                    for idx, (band, resolution) in enumerate(bands.items()):\n",
    "                        print(\"Loading band {}\".format(band))\n",
    "                        x_min, x_max, y_min, y_max = map(lambda x: x * 20 // resolution, (x1,x2,y1,y2))\n",
    "                        print(x_min, x_max, y_min, y_max)\n",
    "                        raster = rasterio.open(product.granule_paths(band)[0])\n",
    "                        data_window[idx] = resize(raster.read(window=((x_min, x_max), (y_min, y_max)))[0], (w,h), order=0, preserve_range=True).astype('uint16', copy=False)\n",
    "                #set_trace()\n",
    "                img = data_window/10000\n",
    "                gt = cci_win\n",
    "                pred = np.zeros(gt.shape + (N_CLASSES,))\n",
    "                print(img.shape)\n",
    "\n",
    "                plt.rcParams['figure.figsize'] = (15, 15)\n",
    "\n",
    "                total = count_sliding_window(img[0], step=stride, window_size=window_size) // batch_size\n",
    "                for i, coords in enumerate(tqdm(grouper(batch_size, sliding_window(img[0], step=stride, window_size=window_size)), total=total, leave=False)):\n",
    "                    # Build the tensor\n",
    "                    image_patches = [np.copy(img[:,x:x+w, y:y+h]) for x,y,w,h in coords]\n",
    "                    image_patches = np.asarray(image_patches)\n",
    "                    image_patches = Variable(torch.from_numpy(image_patches).cuda(), volatile=True)\n",
    "\n",
    "                    # Do the inference\n",
    "                    outs = net(image_patches)\n",
    "                    outs = outs.data.cpu().numpy()\n",
    "\n",
    "                    # Fill in the results array\n",
    "                    for out, (x, y, w, h) in zip(outs, coords):\n",
    "                        out = out.transpose((1,2,0))\n",
    "                        pred[x//2:(x+w)//2, y//2:(y+h)//2] += out\n",
    "                    del(outs)\n",
    "\n",
    "                pred = np.argmax(pred, axis=-1)\n",
    "\n",
    "                # Display the result\n",
    "                fig = plt.figure()\n",
    "                fig.add_subplot(1,3,1)\n",
    "                rgb = get_rgb(img.copy())\n",
    "                plt.imshow(rgb)\n",
    "                fig.add_subplot(1,3,2)\n",
    "                plt.imshow(convert_to_color(pred))\n",
    "                fig.add_subplot(1,3,3)\n",
    "                plt.imshow(convert_to_color(gt))\n",
    "                plt.show()\n",
    "\n",
    "                # Compute some metrics\n",
    "                metrics(pred[~nodata_mask].ravel(), gt[~nodata_mask].ravel())\n",
    "                filename = str(i)\n",
    "                io.imsave(filename + '_rgb.tif', rgb)\n",
    "                io.imsave(filename + '_gt.tif', convert_to_color(gt))\n",
    "                io.imsave(filename + '_pred.tif', convert_to_color(pred))\n",
    "            except Exception as e:\n",
    "                print(e)\n",
    "                pass\n",
    "    if all:\n",
    "        return accuracy\n",
    "    else:\n",
    "        return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating cloud mask\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/naudebert/.anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:8: FutureWarning: The value of this property will change in version 1.0. Please see https://github.com/mapbox/rasterio/issues/86 for details.\n",
      "  \n",
      "/home/naudebert/.anaconda3/lib/python3.6/site-packages/rasterio/features.py:303: FutureWarning: GDAL-style transforms are deprecated and will not be supported in Rasterio 1.0.\n",
      "  transform = guard_transform(transform)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n",
      "(32.095365802682714, -6.421506326508901, 33.08826069638461, -5.427550802596051)\n",
      "CCI window : [[3 1 1 ... 2 2 2]\n",
      " [3 1 1 ... 1 1 1]\n",
      " [3 3 1 ... 1 1 1]\n",
      " ...\n",
      " [3 3 3 ... 1 1 1]\n",
      " [1 2 2 ... 1 1 1]\n",
      " [1 2 2 ... 1 1 1]]\n",
      "(5369, 5363) (5490, 5490)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/naudebert/.anaconda3/lib/python3.6/site-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5490, 5490) (5490, 5490)\n",
      "CCI window with clouds: [[3 1 1 ... 2 2 2]\n",
      " [3 1 1 ... 1 1 1]\n",
      " [3 3 1 ... 1 1 1]\n",
      " ...\n",
      " [3 3 3 ... 1 1 1]\n",
      " [1 2 2 ... 1 1 1]\n",
      " [1 2 2 ... 1 1 1]]\n",
      "Loading band 1\n",
      "0 1829 0 1013\n",
      "Loading band 2\n",
      "0 10978 0 6082\n",
      "Loading band 3\n",
      "0 10978 0 6082\n",
      "Loading band 4\n",
      "0 10978 0 6082\n",
      "Loading band 5\n",
      "0 5489 0 3041\n",
      "Loading band 6\n",
      "0 5489 0 3041\n",
      "Loading band 7\n",
      "0 5489 0 3041\n",
      "Loading band 8\n",
      "0 10978 0 6082\n",
      "Loading band 8A\n",
      "0 5489 0 3041\n",
      "Loading band 9\n",
      "0 1829 0 1013\n",
      "Loading band 10\n",
      "0 1829 0 1013\n",
      "Loading band 11\n",
      "0 5489 0 3041\n",
      "Loading band 12\n",
      "0 5489 0 3041\n",
      "(13, 10978, 6082)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=331), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/naudebert/.anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:45: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix :\n",
      "[[      0       0       0       0       0       0       0       0       0\n",
      "        0       0       0]\n",
      " [      0 5147196   76143   75214  444470       0       0       0      22\n",
      "        0     301    2911]\n",
      " [      0 1333089  252698  257285  127425       0       0       0      16\n",
      "        0      69    1460]\n",
      " [      0 4202805  594163  476486  169068       0       0       0       2\n",
      "        0      93    3010]\n",
      " [      0    8654   26196   19206  193659       0       0       0      18\n",
      "        0      15     164]\n",
      " [      0     278       2      87       7       0       0       0       0\n",
      "        0       0       8]\n",
      " [      0      18       0       2      12       0       0       0       0\n",
      "        0       0       0]\n",
      " [      0     134      25      60       6       0       0       0       0\n",
      "        0       0       0]\n",
      " [      0       2       0       1     146       0       0       0       0\n",
      "        0       0       0]\n",
      " [      0       0       0       0       0       0       0       0       0\n",
      "        0       0       0]\n",
      " [      0    3616     103     334      31       0       0       0       0\n",
      "        0       0       1]\n",
      " [      0      38      16      40      11       0       0       0       0\n",
      "        0       0    4089]]\n",
      "---\n",
      "13420905 pixels processed\n",
      "Total accuracy : 45.25870647322219%\n",
      "---\n",
      "F1Score :\n",
      "No data: nan\n",
      "Tree cover areas: 0.626100080847401\n",
      "Shrubs cover areas: 0.17299858834225376\n",
      "Grassland: 0.15188397444704163\n",
      "Cropland: 0.3274732466030351\n",
      "Vegetation aquatic or regularly flooded: 0.0\n",
      "Lichens Mosses / Sparse vegetation: 0.0\n",
      "Bare areas: 0.0\n",
      "Built up areas: 0.0\n",
      "Snow and/or Ice: nan\n",
      "Open Water: 0.0\n",
      "Cloud: 0.5163856791058913\n",
      "---\n",
      "Kappa: 0.11997618388725531\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/naudebert/.anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:80: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating cloud mask\n",
      "Done\n",
      "(35.69784549363998, -3.7034039262628853, 36.68768474372165, -2.708535417688948)\n",
      "CCI window : [[7 3 3 ... 3 1 1]\n",
      " [7 7 7 ... 3 3 1]\n",
      " [7 7 7 ... 3 1 1]\n",
      " ...\n",
      " [1 1 1 ... 2 2 2]\n",
      " [1 1 1 ... 2 2 2]\n",
      " [1 1 1 ... 2 2 2]]\n",
      "(5373, 5346) (5490, 5490)\n",
      "(5490, 5490) (5490, 5490)\n",
      "CCI window with clouds: [[7 3 3 ... 3 1 1]\n",
      " [7 7 7 ... 3 3 1]\n",
      " [7 7 7 ... 3 1 1]\n",
      " ...\n",
      " [1 1 1 ... 2 2 2]\n",
      " [1 1 1 ... 2 2 2]\n",
      " [1 1 1 ... 2 2 2]]\n",
      "Not enough data\n",
      "Generating cloud mask\n",
      "Done\n",
      "(34.7969157148421, -1.896817942587377, 35.78414747428953, -0.903295103929261)\n",
      "CCI window : [[4 4 4 ... 1 1 1]\n",
      " [4 4 4 ... 1 1 1]\n",
      " [4 4 4 ... 1 1 1]\n",
      " ...\n",
      " [4 3 3 ... 4 4 3]\n",
      " [4 4 3 ... 4 4 3]\n",
      " [4 4 3 ... 4 4 3]]\n",
      "(5366, 5332) (5490, 5490)\n",
      "(5490, 5490) (5490, 5490)\n",
      "CCI window with clouds: [[4 4 4 ... 1 1 1]\n",
      " [4 4 4 ... 1 1 1]\n",
      " [4 4 4 ... 1 1 1]\n",
      " ...\n",
      " [4 3 3 ... 4 4 3]\n",
      " [4 4 3 ... 4 4 3]\n",
      " [4 4 3 ... 4 4 3]]\n",
      "Loading band 1\n",
      "0 1829 0 1829\n",
      "Loading band 2\n",
      "0 10978 0 10978\n",
      "Loading band 3\n",
      "0 10978 0 10978\n",
      "Loading band 4\n",
      "0 10978 0 10978\n"
     ]
    }
   ],
   "source": [
    "test_products = (l.replace('\\n','') for l in open('../tanzania_s2_paths.txt').readlines()[35:])\n",
    "test(net, test_products, rasterio.open('../ESA_CCI_African_LandCover_20m/ESACCI-LC-L4-LC10-Map-20m-P1Y-2016-v1.0.tif'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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