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Sen1Floods11/Sen1Floods11/README.md

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+# Sen1Floods11
+
+**NOTE: As of v1.1 of Sen1Floods11 the data has been moved to the `sen1floods11` bucket on GCS. The original v1.0 data is still hosted for now on the original `cnn_chips` bucket. v1.1 involved major restructing of the bucket and more understandable file naming as well as updates to the dataset and data format to comply with COG spec.**
+
+Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1 (Example). This data was generated by Cloud to Street, a Public Benefit Corporation: https://www.cloudtostreet.info/. For questions about this dataset or code please email support@cloudtostreet.info. Please cite this data as:
+
+Bonafilia, D., Tellman, B., Anderson, T., Issenberg, E. 2020. Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 210-211.
+
+Available Open access at: http://openaccess.thecvf.com/content_CVPRW_2020/html/w11/Bonafilia_Sen1Floods11_A_Georeferenced_Dataset_to_Train_and_Test_Deep_Learning_CVPRW_2020_paper.html
+
+## Dataset Access
+
+The dataset is available for access through Google Cloud Storage bucket at: `gs://senfloods11/`
+
+You can access the dataset bucket using the [gsutil](https://cloud.google.com/storage/docs/gsutil) command line tool. If you would like to download the entire dataset (~14 GB) you can use `gsutil rsync` to clone the bucket to a local directory. The `-m` flag is recommended to speed downloads. The `-r` flag will download sub-directories and folder recursively. See the example below.
+
+```bash
+$ gsutil -m rsync -r gs://sen1floods11 /YOUR/LOCAL/DIRECTORY/HERE
+```
+
+If using an example notebook, you can download the dataset to the folder that notebooks expect it to be in by running
+
+```bash
+$ mkdir /home/files
+$ gsutil -m rsync -r gs://sen1floods11 /home/files
+```
+
+## Bucket Structure
+
+The `sen1floods11` bucket is split into subfolders containing data, checkpoints, training/testing splits, and a [STAC](https://stacspec.org/) compliant catalog. More detail on each is provided in the docs README.
+
+## Dataset Information
+
+Each file follows the naming scheme EVENT_CHIPID_LAYER.tif (e.g. `Bolivia_103757_S2Hand.tif`). Chip IDs are unique, and not shared between events. Events are named by country and further information on each event (including dates) can be found in the event metadata below. Each layer has a separate GeoTIFF, and can contain multiple bands in a stacked GeoTIFF. All images are projected to WGS 84 (`EPSG:4326`) at 10 m ground resolution.
+
+| Layer | Description                                                                                                                                              | Values                                                  | Format                                           | Bands                                                                                                                                                                                                                                                                           |
+| ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------- | ------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| QC    | Hand labeled chips containing ground truth                                                                                                               | -1: No Data / Not Valid <br> 0: Not Water <br> 1: Water | GeoTIFF <br> 512 x 512 <br> 1 band <br> Int16    | 0: QC                                                                                                                                                                                                                                                                           |
+| S1    | Raw Sentinel-1 imagery. <br> IW mode, GRD product <br> See [here](https://developers.google.com/earth-engine/sentinel1) for information on preprocessing | Unit: dB                                                | GeoTIFF <br> 512 x 512 <br> 2 bands <br> Float32 | 0: VV <br> 1: VH                                                                                                                                                                                                                                                                |
+| S2    | Raw Sentinel-2 MSI Level-1C imagery <br> Contains all spectral bands (1 - 12) <br> Does not contain QA mask                                              | Unit: TOA reflectance <br> (scaled by 10000)            | GeoTIFF <br> 512 x 512 <br> 13 bands <br> UInt16 | 0: B1 (Coastal) <br> 1: B2 (Blue) <br> 2: B3 (Green) <br> 3: B4 (Red) <br> 4: B5 (RedEdge-1) <br> 5: B6 (RedEdge-2) <br> 6: B7 (RedEdge-3) <br> 7: B8 (NIR) <br> 8: B8A (Narrow NIR) <br> 9: B9 (Water Vapor) <br> 10: B10 (Cirrus) <br> 11: B11 (SWIR-1) <br> 12: B12 (SWIR-2) |
+
+### Example images
+
+A sample of the dataset for chip _Spain_7370579_ is provided at in `./sample`
+
+<div>
+  <img src="./docs/img/Spain_7370579_Label.png" height="256" hspace=3 >
+  <img src="./docs/img/Spain_7370579_S1.png" height="256" hspace=3 >
+  <img src="./docs/img/Spain_7370579_S2.png" height="256" hspace=3 >
+</div>
+
+## Example Use
+
+[Train.ipynb](Train.ipynb) shows how to train and validate the model on a dataset.
+
+## Event Metadata
+
+Locations of the flood events and metadata is contained in _Sen1Floods11_Metadata.geojson_. The following fields can be found:
+
+| Field           | Description                                                                            |
+| --------------- | -------------------------------------------------------------------------------------- |
+| ID              | Unique ID for each event                                                               |
+| location        | Flood event location (country)                                                         |
+| ISO_CC          | ISO Country Code for flood event location                                              |
+| s1_date         | Date (YYYY-MM-dd) that Sentinel-1 image was acquired                                   |
+| s2_date         | Date (YYYY-MM-dd) that Sentinel-2 image was acquired                                   |
+| orbit           | Orbit (ASCENDING or DESCENDING) that Sentinel-1 image was acquired                     |
+| rel_orbit_num   | Relative Orbit Number that Sentinel-1 image was acquired                               |
+| coincident_size | Number of coincident tiles from S2                                                     |
+| VH_thresh       | Threshold used for Sentinel-1 VH band to classify water in reference S1 classification |
+| train_chip      | Number of chips used for training                                                      |
+| val_chip        | Number of chips used for validation                                                    |

Sen1Floods11/Sen1Floods11/Sen1Floods11_Metadata.geojson

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+{ "type": "Feature", "properties": { "coincident_size": 8.0, "location": "USA", "orbit": "ASCENDING", "rel_orbit_num": 136.0, "s1_date": "2019\/05\/22", "s2_date": "2019\/05\/22", "ID": 11, "ISO_CC": "USA", "VH_thresh": -22.62, "train_chip": 486, "val_chip": 69 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -95.691758004799993, 38.357837829772819 ], [ -95.608594038100534, 38.357837889512247 ], [ -95.484020153361371, 38.357815956156962 ], [ -95.317921675840481, 38.357815960963336 ], [ -95.151823110070751, 38.357815913921293 ], [ -94.985724655291392, 38.357815893540149 ], [ -94.819626151024792, 38.357815906029799 ], [ -94.695052314729722, 38.357837924199501 ], [ -94.570478437577592, 38.357815923894755 ], [ -94.445904511854209, 38.357837886248639 ], [ -94.362740543720761, 38.357837821666749 ], [ -94.362735917281043, 41.061767135559627 ], [ -94.528953777955579, 41.061767195893367 ], [ -94.861150774815471, 41.061767190604321 ], [ -95.027249263225428, 41.061767232642516 ], [ -95.276397016907566, 41.06176723579587 ], [ -95.442495528882873, 41.061767222676252 ], [ -95.691762581460964, 41.061767167209496 ], [ -95.691758004799993, 38.357837829772819 ] ] ] } }
+]
+}

Sen1Floods11/Sen1Floods11/Train.ipynb

@@ -0,0 +1,836 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tbu_ucRSo5zT"
+   },
+   "source": [
+    "The following is an example of how to utilize our Sen1Floods11 dataset for training a FCNN. In this example, we train and validate on hand-labeled chips of flood events. However, our dataset includes several other options that are detailed in the README. To replace the dataset, as outlined further below, simply replace the train, test, and validation split csv's, and download the corresponding dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9TQtMrI_VhKk"
+   },
+   "source": [
+    "Authenticate Google Cloud Platform. Note that to run this code, you must connect your notebook runtime to a GPU. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "qCEt8eNtU9Zm"
+   },
+   "outputs": [],
+   "source": [
+    "from google.colab import auth\n",
+    "auth.authenticate_user()\n",
+    "\n",
+    "!curl https://sdk.cloud.google.com | bash\n",
+    "\n",
+    "!gcloud init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "YkUEnwXQVy4k"
+   },
+   "outputs": [],
+   "source": [
+    "!echo \"deb http://packages.cloud.google.com/apt gcsfuse-bionic main\" > /etc/apt/sources.list.d/gcsfuse.list\n",
+    "!curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -\n",
+    "!apt -qq update\n",
+    "!apt -qq install gcsfuse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vXGTA6vHVyJX"
+   },
+   "source": [
+    "Install RasterIO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "NLlVutLzV_pZ"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install rasterio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hLqL9C2Rg6eB"
+   },
+   "source": [
+    "Define a model checkpoint folder, for storing network checkpoints during training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "yLlIhE-Hg-Ym"
+   },
+   "outputs": [],
+   "source": [
+    "%cd /home\n",
+    "!sudo mkdir checkpoints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mwrDM4AjVnbU"
+   },
+   "source": [
+    "Download train, test, and validation splits for both flood water. To download different train, test, and validation splits, simply replace these paths with the path to a csv containing the desired splits. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RFLsGwdRWuO4"
+   },
+   "outputs": [],
+   "source": [
+    "!gsutil cp gs://sen1floods11/v1.1/splits/flood_handlabeled/flood_train_data.csv .\n",
+    "!gsutil cp gs://sen1floods11/v1.1/splits/flood_handlabeled/flood_test_data.csv .\n",
+    "!gsutil cp gs://sen1floods11/v1.1/splits/flood_handlabeled/flood_valid_data.csv ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZCAXpuKVW3eV"
+   },
+   "source": [
+    "Download raw train, test, and validation data. In this example, we are downloading train, test, and validation data of flood images which are hand labeled. However, you can simply replace these paths with whichever dataset you would like to use - further documentation of the Sen1Floods11 dataset and organization is available in the README."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ahAWnrSFW53S"
+   },
+   "outputs": [],
+   "source": [
+    "!sudo mkdir files\n",
+    "!sudo mkdir files/S1\n",
+    "!sudo mkdir files/Labels\n",
+    "\n",
+    "!gsutil -m rsync -r gs://sen1floods11/v1.1/data/flood_events/HandLabeled/S1Hand files/S1\n",
+    "!gsutil -m rsync -r gs://sen1floods11/v1.1/data/flood_events/HandLabeled/LabelHand files/Labels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_46CazV3XSCD"
+   },
+   "source": [
+    "Define model training hyperparameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "fNYQywdWXeLM"
+   },
+   "outputs": [],
+   "source": [
+    "LR = 5e-4\n",
+    "EPOCHS = 100\n",
+    "EPOCHS_PER_UPDATE = 1\n",
+    "RUNNAME = \"Sen1Floods11\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "W9FJmTnZXjxj"
+   },
+   "source": [
+    "Define functions to process and augment training and testing images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "mBkfav0Eajqg"
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'torchvision'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtorchvision\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m transforms\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtorchvision\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransforms\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfunctional\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mF\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mrandom\u001b[39;00m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'torchvision'"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from torchvision import transforms\n",
+    "import torchvision.transforms.functional as F\n",
+    "import random\n",
+    "from PIL import Image\n",
+    "\n",
+    "class InMemoryDataset(torch.utils.data.Dataset):\n",
+    "  \n",
+    "  def __init__(self, data_list, preprocess_func):\n",
+    "    self.data_list = data_list\n",
+    "    self.preprocess_func = preprocess_func\n",
+    "  \n",
+    "  def __getitem__(self, i):\n",
+    "    return self.preprocess_func(self.data_list[i])\n",
+    "  \n",
+    "  def __len__(self):\n",
+    "    return len(self.data_list)\n",
+    "\n",
+    "\n",
+    "def processAndAugment(data):\n",
+    "  (x,y) = data\n",
+    "  im,label = x.copy(), y.copy()\n",
+    "\n",
+    "  # convert to PIL for easier transforms\n",
+    "  im1 = Image.fromarray(im[0])\n",
+    "  im2 = Image.fromarray(im[1])\n",
+    "  label = Image.fromarray(label.squeeze())\n",
+    "\n",
+    "  # Get params for random transforms\n",
+    "  i, j, h, w = transforms.RandomCrop.get_params(im1, (256, 256))\n",
+    "  \n",
+    "  im1 = F.crop(im1, i, j, h, w)\n",
+    "  im2 = F.crop(im2, i, j, h, w)\n",
+    "  label = F.crop(label, i, j, h, w)\n",
+    "  if random.random() > 0.5:\n",
+    "    im1 = F.hflip(im1)\n",
+    "    im2 = F.hflip(im2)\n",
+    "    label = F.hflip(label)\n",
+    "  if random.random() > 0.5:\n",
+    "    im1 = F.vflip(im1)\n",
+    "    im2 = F.vflip(im2)\n",
+    "    label = F.vflip(label)\n",
+    "  \n",
+    "  norm = transforms.Normalize([0.6851, 0.5235], [0.0820, 0.1102])\n",
+    "  im = torch.stack([transforms.ToTensor()(im1).squeeze(), transforms.ToTensor()(im2).squeeze()])\n",
+    "  im = norm(im)\n",
+    "  label = transforms.ToTensor()(label).squeeze()\n",
+    "  if torch.sum(label.gt(.003) * label.lt(.004)):\n",
+    "    label *= 255\n",
+    "  label = label.round()\n",
+    "\n",
+    "  return im, label\n",
+    "\n",
+    "\n",
+    "def processTestIm(data):\n",
+    "  (x,y) = data\n",
+    "  im,label = x.copy(), y.copy()\n",
+    "  norm = transforms.Normalize([0.6851, 0.5235], [0.0820, 0.1102])\n",
+    "\n",
+    "  # convert to PIL for easier transforms\n",
+    "  im_c1 = Image.fromarray(im[0]).resize((512,512))\n",
+    "  im_c2 = Image.fromarray(im[1]).resize((512,512))\n",
+    "  label = Image.fromarray(label.squeeze()).resize((512,512))\n",
+    "\n",
+    "  im_c1s = [F.crop(im_c1, 0, 0, 256, 256), F.crop(im_c1, 0, 256, 256, 256),\n",
+    "            F.crop(im_c1, 256, 0, 256, 256), F.crop(im_c1, 256, 256, 256, 256)]\n",
+    "  im_c2s = [F.crop(im_c2, 0, 0, 256, 256), F.crop(im_c2, 0, 256, 256, 256),\n",
+    "            F.crop(im_c2, 256, 0, 256, 256), F.crop(im_c2, 256, 256, 256, 256)]\n",
+    "  labels = [F.crop(label, 0, 0, 256, 256), F.crop(label, 0, 256, 256, 256),\n",
+    "            F.crop(label, 256, 0, 256, 256), F.crop(label, 256, 256, 256, 256)]\n",
+    "\n",
+    "  ims = [torch.stack((transforms.ToTensor()(x).squeeze(),\n",
+    "                    transforms.ToTensor()(y).squeeze()))\n",
+    "                    for (x,y) in zip(im_c1s, im_c2s)]\n",
+    "  \n",
+    "  ims = [norm(im) for im in ims]\n",
+    "  ims = torch.stack(ims)\n",
+    "  \n",
+    "  labels = [(transforms.ToTensor()(label).squeeze()) for label in labels]\n",
+    "  labels = torch.stack(labels)\n",
+    "  \n",
+    "  if torch.sum(labels.gt(.003) * labels.lt(.004)):\n",
+    "    labels *= 255\n",
+    "  labels = labels.round()\n",
+    "  \n",
+    "  return ims, labels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uzmZIRuoeAuJ"
+   },
+   "source": [
+    "Load *flood water* train, test, and validation data from splits. In this example, this is the data we will use to train our model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "rQUnYCIBeG21"
+   },
+   "outputs": [],
+   "source": [
+    "from time import time\n",
+    "import csv\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import rasterio\n",
+    "\n",
+    "def getArrFlood(fname):\n",
+    "  return rasterio.open(fname).read()\n",
+    "\n",
+    "def download_flood_water_data_from_list(l):\n",
+    "  i = 0\n",
+    "  tot_nan = 0\n",
+    "  tot_good = 0\n",
+    "  flood_data = []\n",
+    "  for (im_fname, mask_fname) in l:\n",
+    "    if not os.path.exists(os.path.join(\"files/\", im_fname)):\n",
+    "      continue\n",
+    "    arr_x = np.nan_to_num(getArrFlood(os.path.join(\"files/\", im_fname)))\n",
+    "    arr_y = getArrFlood(os.path.join(\"files/\", mask_fname))\n",
+    "    arr_y[arr_y == -1] = 255 \n",
+    "    \n",
+    "    arr_x = np.clip(arr_x, -50, 1)\n",
+    "    arr_x = (arr_x + 50) / 51\n",
+    "      \n",
+    "    if i % 100 == 0:\n",
+    "      print(im_fname, mask_fname)\n",
+    "    i += 1\n",
+    "    flood_data.append((arr_x,arr_y))\n",
+    "\n",
+    "  return flood_data\n",
+    "\n",
+    "def load_flood_train_data(input_root, label_root):\n",
+    "  fname = \"flood_train_data.csv\"\n",
+    "  training_files = []\n",
+    "  with open(fname) as f:\n",
+    "    for line in csv.reader(f):\n",
+    "      training_files.append(tuple((input_root+line[0], label_root+line[1])))\n",
+    "\n",
+    "  return download_flood_water_data_from_list(training_files)\n",
+    "\n",
+    "def load_flood_valid_data(input_root, label_root):\n",
+    "  fname = \"flood_valid_data.csv\"\n",
+    "  validation_files = []\n",
+    "  with open(fname) as f:\n",
+    "    for line in csv.reader(f):\n",
+    "      validation_files.append(tuple((input_root+line[0], label_root+line[1])))\n",
+    "\n",
+    "  return download_flood_water_data_from_list(validation_files)\n",
+    "\n",
+    "def load_flood_test_data(input_root, label_root):\n",
+    "  fname = \"flood_test_data.csv\"\n",
+    "  testing_files = []\n",
+    "  with open(fname) as f:\n",
+    "    for line in csv.reader(f):\n",
+    "      testing_files.append(tuple((input_root+line[0], label_root+line[1])))\n",
+    "  \n",
+    "  return download_flood_water_data_from_list(testing_files)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cFp9jrHYfOUh"
+   },
+   "source": [
+    "Load training data and validation data. Note that here, we have chosen to train and validate our model on flood data. However, you can simply replace the load function call with one of the options defined above to load a different dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ZcqPlsjBffXx"
+   },
+   "outputs": [],
+   "source": [
+    "train_data = load_flood_train_data('S1/', 'Labels/')\n",
+    "train_dataset = InMemoryDataset(train_data, processAndAugment)\n",
+    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True, sampler=None,\n",
+    "                  batch_sampler=None, num_workers=0, collate_fn=None,\n",
+    "                  pin_memory=True, drop_last=False, timeout=0,\n",
+    "                  worker_init_fn=None)\n",
+    "train_iter = iter(train_loader)\n",
+    "\n",
+    "valid_data = load_flood_valid_data('S1/', 'Labels/') \n",
+    "valid_dataset = InMemoryDataset(valid_data, processTestIm)\n",
+    "valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=4, shuffle=True, sampler=None,\n",
+    "                  batch_sampler=None, num_workers=0, collate_fn=lambda x: (torch.cat([a[0] for a in x], 0), torch.cat([a[1] for a in x], 0)),\n",
+    "                  pin_memory=True, drop_last=False, timeout=0,\n",
+    "                  worker_init_fn=None)\n",
+    "valid_iter = iter(valid_loader)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i3aAhUi2fp7M"
+   },
+   "source": [
+    "Define the network. For our purposes, we use ResNet50. However, if you wish to test a different model framework, optimizer, or loss function you can simply replace those here. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "5cp4uXI1f9dr"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torchvision.models as models\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "net = models.segmentation.fcn_resnet50(pretrained=False, num_classes=2, pretrained_backbone=False)\n",
+    "net.backbone.conv1 = nn.Conv2d(2, 64, kernel_size=7, stride=2, padding=3, bias=False)\n",
+    "\n",
+    "criterion = nn.CrossEntropyLoss(weight=torch.tensor([1,8]).float().cuda(), ignore_index=255) \n",
+    "optimizer = torch.optim.AdamW(net.parameters(),lr=LR)\n",
+    "scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, len(train_loader) * 10, T_mult=2, eta_min=0, last_epoch=-1)\n",
+    "\n",
+    "def convertBNtoGN(module, num_groups=16):\n",
+    "  if isinstance(module, torch.nn.modules.batchnorm.BatchNorm2d):\n",
+    "    return nn.GroupNorm(num_groups, module.num_features,\n",
+    "                        eps=module.eps, affine=module.affine)\n",
+    "    if module.affine:\n",
+    "        mod.weight.data = module.weight.data.clone().detach()\n",
+    "        mod.bias.data = module.bias.data.clone().detach()\n",
+    "\n",
+    "  for name, child in module.named_children():\n",
+    "      module.add_module(name, convertBNtoGN(child, num_groups=num_groups))\n",
+    "\n",
+    "  return module\n",
+    "\n",
+    "net = convertBNtoGN(net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "g_Sy3ALGgQjf"
+   },
+   "source": [
+    "Define assessment metrics. For our purposes, we use overall accuracy and mean intersection over union. However, we also include functions for calculating true positives, false positives, true negatives, and false negatives."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "bwxC-fVBgUIb"
+   },
+   "outputs": [],
+   "source": [
+    "def computeIOU(output, target):\n",
+    "  output = torch.argmax(output, dim=1).flatten() \n",
+    "  target = target.flatten()\n",
+    "  \n",
+    "  no_ignore = target.ne(255).cuda()\n",
+    "  output = output.masked_select(no_ignore)\n",
+    "  target = target.masked_select(no_ignore)\n",
+    "  intersection = torch.sum(output * target)\n",
+    "  union = torch.sum(target) + torch.sum(output) - intersection\n",
+    "  iou = (intersection + .0000001) / (union + .0000001)\n",
+    "  \n",
+    "  if iou != iou:\n",
+    "    print(\"failed, replacing with 0\")\n",
+    "    iou = torch.tensor(0).float()\n",
+    "  \n",
+    "  return iou\n",
+    "  \n",
+    "def computeAccuracy(output, target):\n",
+    "  output = torch.argmax(output, dim=1).flatten() \n",
+    "  target = target.flatten()\n",
+    "  \n",
+    "  no_ignore = target.ne(255).cuda()\n",
+    "  output = output.masked_select(no_ignore)\n",
+    "  target = target.masked_select(no_ignore)\n",
+    "  correct = torch.sum(output.eq(target))\n",
+    "  \n",
+    "  return correct.float() / len(target)\n",
+    "\n",
+    "def truePositives(output, target):\n",
+    "  output = torch.argmax(output, dim=1).flatten() \n",
+    "  target = target.flatten()\n",
+    "  no_ignore = target.ne(255).cuda()\n",
+    "  output = output.masked_select(no_ignore)\n",
+    "  target = target.masked_select(no_ignore)\n",
+    "  correct = torch.sum(output * target)\n",
+    "  \n",
+    "  return correct\n",
+    "\n",
+    "def trueNegatives(output, target):\n",
+    "  output = torch.argmax(output, dim=1).flatten() \n",
+    "  target = target.flatten()\n",
+    "  no_ignore = target.ne(255).cuda()\n",
+    "  output = output.masked_select(no_ignore)\n",
+    "  target = target.masked_select(no_ignore)\n",
+    "  output = (output == 0)\n",
+    "  target = (target == 0)\n",
+    "  correct = torch.sum(output * target)\n",
+    "  \n",
+    "  return correct\n",
+    "\n",
+    "def falsePositives(output, target):\n",
+    "  output = torch.argmax(output, dim=1).flatten() \n",
+    "  target = target.flatten()\n",
+    "  no_ignore = target.ne(255).cuda()\n",
+    "  output = output.masked_select(no_ignore)\n",
+    "  target = target.masked_select(no_ignore)\n",
+    "  output = (output == 1)\n",
+    "  target = (target == 0)\n",
+    "  correct = torch.sum(output * target)\n",
+    "  \n",
+    "  return correct\n",
+    "\n",
+    "def falseNegatives(output, target):\n",
+    "  output = torch.argmax(output, dim=1).flatten() \n",
+    "  target = target.flatten()\n",
+    "  no_ignore = target.ne(255).cuda()\n",
+    "  output = output.masked_select(no_ignore)\n",
+    "  target = target.masked_select(no_ignore)\n",
+    "  output = (output == 0)\n",
+    "  target = (target == 1)\n",
+    "  correct = torch.sum(output * target)\n",
+    "  \n",
+    "  return correct"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lun5tGoYgjWX"
+   },
+   "source": [
+    "Define training loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "DubsYZ8GgkxD"
+   },
+   "outputs": [],
+   "source": [
+    "training_losses = []\n",
+    "training_accuracies = []\n",
+    "training_ious = []\n",
+    "\n",
+    "def train_loop(inputs, labels, net, optimizer, scheduler):\n",
+    "  global running_loss\n",
+    "  global running_iou\n",
+    "  global running_count\n",
+    "  global running_accuracy\n",
+    "  \n",
+    "  # zero the parameter gradients\n",
+    "  optimizer.zero_grad()\n",
+    "  net = net.cuda()\n",
+    "  \n",
+    "  # forward + backward + optimize\n",
+    "  outputs = net(inputs.cuda())\n",
+    "  loss = criterion(outputs[\"out\"], labels.long().cuda())\n",
+    "  loss.backward()\n",
+    "  optimizer.step()\n",
+    "  scheduler.step()\n",
+    "\n",
+    "  running_loss += loss\n",
+    "  running_iou += computeIOU(outputs[\"out\"], labels.cuda())\n",
+    "  running_accuracy += computeAccuracy(outputs[\"out\"], labels.cuda())\n",
+    "  running_count += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "iM3Jz__hgshh"
+   },
+   "source": [
+    "Define validation loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "_GmVaoRvguic"
+   },
+   "outputs": [],
+   "source": [
+    "valid_losses = []\n",
+    "valid_accuracies = []\n",
+    "valid_ious = []\n",
+    "\n",
+    "def validation_loop(validation_data_loader, net):\n",
+    "  global running_loss\n",
+    "  global running_iou\n",
+    "  global running_count\n",
+    "  global running_accuracy\n",
+    "  global max_valid_iou\n",
+    "\n",
+    "  global training_losses\n",
+    "  global training_accuracies\n",
+    "  global training_ious\n",
+    "  global valid_losses\n",
+    "  global valid_accuracies\n",
+    "  global valid_ious\n",
+    "\n",
+    "  net = net.eval()\n",
+    "  net = net.cuda()\n",
+    "  count = 0\n",
+    "  iou = 0\n",
+    "  loss = 0\n",
+    "  accuracy = 0\n",
+    "  with torch.no_grad():\n",
+    "      for (images, labels) in validation_data_loader:\n",
+    "          net = net.cuda()\n",
+    "          outputs = net(images.cuda())\n",
+    "          valid_loss = criterion(outputs[\"out\"], labels.long().cuda())\n",
+    "          valid_iou = computeIOU(outputs[\"out\"], labels.cuda())\n",
+    "          valid_accuracy = computeAccuracy(outputs[\"out\"], labels.cuda())\n",
+    "          iou += valid_iou\n",
+    "          loss += valid_loss\n",
+    "          accuracy += valid_accuracy\n",
+    "          count += 1\n",
+    "\n",
+    "  iou = iou / count\n",
+    "  accuracy = accuracy / count\n",
+    "\n",
+    "  if iou > max_valid_iou:\n",
+    "    max_valid_iou = iou\n",
+    "    save_path = os.path.join(\"checkpoints\", \"{}_{}_{}.cp\".format(RUNNAME, i, iou.item()))\n",
+    "    torch.save(net.state_dict(), save_path)\n",
+    "    print(\"model saved at\", save_path)\n",
+    "\n",
+    "  loss = loss / count\n",
+    "  print(\"Training Loss:\", running_loss / running_count)\n",
+    "  print(\"Training IOU:\", running_iou / running_count)\n",
+    "  print(\"Training Accuracy:\", running_accuracy / running_count)\n",
+    "  print(\"Validation Loss:\", loss)\n",
+    "  print(\"Validation IOU:\", iou)\n",
+    "  print(\"Validation Accuracy:\", accuracy)\n",
+    "\n",
+    "\n",
+    "  training_losses.append(running_loss / running_count)\n",
+    "  training_accuracies.append(running_accuracy / running_count)\n",
+    "  training_ious.append(running_iou / running_count)\n",
+    "  valid_losses.append(loss)\n",
+    "  valid_accuracies.append(accuracy)\n",
+    "  valid_ious.append(iou)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DBMattYshiUj"
+   },
+   "source": [
+    "Define testing loop (here, you can replace assessment metrics)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "mI_mhL_ehjot"
+   },
+   "outputs": [],
+   "source": [
+    "def test_loop(test_data_loader, net):\n",
+    "  net = net.eval()\n",
+    "  net = net.cuda()\n",
+    "  count = 0\n",
+    "  iou = 0\n",
+    "  loss = 0\n",
+    "  accuracy = 0\n",
+    "  with torch.no_grad():\n",
+    "      for (images, labels) in tqdm(test_data_loader):\n",
+    "          net = net.cuda()\n",
+    "          outputs = net(images.cuda())\n",
+    "          valid_loss = criterion(outputs[\"out\"], labels.long().cuda())\n",
+    "          valid_iou = computeIOU(outputs[\"out\"], labels.cuda())\n",
+    "          iou += valid_iou\n",
+    "          accuracy += computeAccuracy(outputs[\"out\"], labels.cuda())\n",
+    "          count += 1\n",
+    "\n",
+    "  iou = iou / count\n",
+    "  print(\"Test IOU:\", iou)\n",
+    "  print(\"Test Accuracy:\", accuracy / count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cy9Fii06h17Q"
+   },
+   "source": [
+    "Define training and validation scheme"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "NZuKVC6wh4Go"
+   },
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "running_loss = 0\n",
+    "running_iou = 0\n",
+    "running_count = 0\n",
+    "running_accuracy = 0\n",
+    "\n",
+    "training_losses = []\n",
+    "training_accuracies = []\n",
+    "training_ious = []\n",
+    "valid_losses = []\n",
+    "valid_accuracies = []\n",
+    "valid_ious = []\n",
+    "\n",
+    "\n",
+    "def train_epoch(net, optimizer, scheduler, train_iter):\n",
+    "  for (inputs, labels) in tqdm(train_iter):\n",
+    "    train_loop(inputs.cuda(), labels.cuda(), net.cuda(), optimizer, scheduler)\n",
+    " \n",
+    "\n",
+    "def train_validation_loop(net, optimizer, scheduler, train_loader,\n",
+    "                          valid_loader, num_epochs, cur_epoch):\n",
+    "  global running_loss\n",
+    "  global running_iou\n",
+    "  global running_count\n",
+    "  global running_accuracy\n",
+    "  net = net.train()\n",
+    "  running_loss = 0\n",
+    "  running_iou = 0\n",
+    "  running_count = 0\n",
+    "  running_accuracy = 0\n",
+    "  \n",
+    "  for i in tqdm(range(num_epochs)):\n",
+    "    train_iter = iter(train_loader)\n",
+    "    train_epoch(net, optimizer, scheduler, train_iter)\n",
+    "  clear_output()\n",
+    "  \n",
+    "  print(\"Current Epoch:\", cur_epoch)\n",
+    "  validation_loop(iter(valid_loader), net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "k3I88aY5iAWD"
+   },
+   "source": [
+    "Train model and assess metrics over epochs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "8MRpxUGWiDTu"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from IPython.display import display\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "max_valid_iou = 0\n",
+    "start = 0\n",
+    "\n",
+    "epochs = []\n",
+    "training_losses = []\n",
+    "training_accuracies = []\n",
+    "training_ious = []\n",
+    "valid_losses = []\n",
+    "valid_accuracies = []\n",
+    "valid_ious = []\n",
+    "\n",
+    "for i in range(start, 1000):\n",
+    "  train_validation_loop(net, optimizer, scheduler, train_loader, valid_loader, 10, i)\n",
+    "  epochs.append(i)\n",
+    "  x = epochs\n",
+    "  plt.plot(x, training_losses, label='training losses')\n",
+    "  plt.plot(x, training_accuracies, 'tab:orange', label='training accuracy')\n",
+    "  plt.plot(x, training_ious, 'tab:purple', label='training iou')\n",
+    "  plt.plot(x, valid_losses, label='valid losses')\n",
+    "  plt.plot(x, valid_accuracies, 'tab:red',label='valid accuracy')\n",
+    "  plt.plot(x, valid_ious, 'tab:green',label='valid iou')\n",
+    "  plt.legend(loc=\"upper left\")\n",
+    "\n",
+    "  display(plt.show())\n",
+    "\n",
+    "  print(\"max valid iou:\", max_valid_iou)"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Train.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "hydro2",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

Sen1Floods11/Sen1Floods11/docs/README.md

@@ -0,0 +1,46 @@
+# **Sen1Floods11 Documentation**
+
+## **Bucket Structure**
+
+Please refer below for a description of each file and folder inside the `sen1floods11` bucket that hosts Sen1Floods11.
+Note that the most recent data is available within the `v1.1` folder.
+
+## _**Folders**_
+
+### _**Data:**_
+
+_**Hand Labeled Data** Folder: data/flood_events/HandLabeled_
+
+`LabelHand/`: 446 labeled chips of Water/NoWater/NoData.
+
+`S1Hand/`: 446 Sentinel-1 GRD Chips overlapping labeled data.
+
+`S2Hand/`: 446 Sentinel-2 L1C Chips overlapping labeled data.
+
+`JRCWaterHand/`: JRC Permement Water overlapping labeled data
+
+`S1OtsuLabelHand/`: 446 chips of water/nowater derived from standard OTSU thresholding of Sentinel-1 VH band overlapping labeled data.
+
+_**Weakly Labeled Data** Folder: data/flood_events/WeaklyLabeled_
+
+`S1OtsuLabelWeak/`: 4,385 chips of water/nowater derived from standard OTSU thresholding of Sentinel-1 VH band overlapping weakly-labeled data.
+
+`S2IndexLabelWeak/`: 4,385 weakly-labeled chips derived from traditional Sentinel-2 Classification.
+
+`S1Weak/`: 4,385 Sentinel-1 GRD chips overlapping weakly-labeled data.
+
+_**JRC Labeled Data** Folder: data/perm_water_
+
+`S1Perm/`: 815 chips of Sentinel-1 overlapping JRC labels.
+
+`JRCPerm/`: 815 chips of permenent water derived from JRC.
+
+### _**Splits:**_
+
+`flood_handlabeled/`: contains train, test, and validation splits for handlabeled images of floods (see: `data/flood_events/HandLabeled`).
+
+`perm_water/`: contains train, test, and validation splits for permanent water (see: `data/perm_water/`)
+
+## _**Files**_
+
+`CNN_Chips_FTC.geojson`: _.geojson_ file containing bounding-box and meta-data for each chip in labeled data (does not contain weakly-labeled data).

Sen1Floods11/Sen1Floods11/old_training/Main_Training_Stuff.ipynb

@@ -0,0 +1,2001 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/dbonafilia/SGDWR-AdamWR-Keras/blob/master/Main_Training_Stuff.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "KaTdYzzy2E2K"
+   },
+   "outputs": [],
+   "source": [
+    "LR = 5e-4\n",
+    "EPOCHS = 1000\n",
+    "EPOCHS_PER_UPDATE = 1\n",
+    "RUNNAME = \"5e4_handlabeled_0\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home\n"
+     ]
+    }
+   ],
+   "source": [
+    "cd /home"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "0rKXiKodtzk_"
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torchvision import transforms\n",
+    "import torchvision.transforms.functional as F\n",
+    "import random\n",
+    "\n",
+    "class InMemoryDataset(torch.utils.data.Dataset):\n",
+    "  \n",
+    "  def __init__(self, data_list, preprocess_func):\n",
+    "    self.data_list = data_list\n",
+    "    self.preprocess_func = preprocess_func\n",
+    "  \n",
+    "  def __getitem__(self, i):\n",
+    "    return self.preprocess_func(self.data_list[i])\n",
+    "  \n",
+    "  def __len__(self):\n",
+    "    return len(self.data_list)\n",
+    "\n",
+    "\n",
+    "def processAndAugment(data):\n",
+    "  (x,y) = data\n",
+    "  im,label = x.copy(), y.copy()\n",
+    "\n",
+    "  # convert to PIL for easier transforms\n",
+    "  im1 = Image.fromarray(im[0])\n",
+    "  im2 = Image.fromarray(im[1])\n",
+    "  label = Image.fromarray(label.squeeze())\n",
+    "\n",
+    "  # Get params for random transforms\n",
+    "  i, j, h, w = transforms.RandomCrop.get_params(im1, (256, 256))\n",
+    "  \n",
+    "  im1 = F.crop(im1, i, j, h, w)\n",
+    "  im2 = F.crop(im2, i, j, h, w)\n",
+    "  label = F.crop(label, i, j, h, w)\n",
+    "  if random.random() > 0.5:\n",
+    "    im1 = F.hflip(im1)\n",
+    "    im2 = F.hflip(im2)\n",
+    "    label = F.hflip(label)\n",
+    "  if random.random() > 0.5:\n",
+    "    im1 = F.vflip(im1)\n",
+    "    im2 = F.vflip(im2)\n",
+    "    label = F.vflip(label)\n",
+    "  \n",
+    "  norm = transforms.Normalize([0.6851, 0.5235], [0.0820, 0.1102])\n",
+    "  im = torch.stack([transforms.ToTensor()(im1).squeeze(), transforms.ToTensor()(im2).squeeze()])\n",
+    "  im = norm(im)\n",
+    "  label = transforms.ToTensor()(label).squeeze()\n",
+    "  if torch.sum(label.gt(.003) * label.lt(.004)):\n",
+    "    label *= 255\n",
+    "  label = label.round()\n",
+    "\n",
+    "  return im, label\n",
+    "\n",
+    "\n",
+    "def processTestIm(data):\n",
+    "  (x,y) = data\n",
+    "  im,label = x.copy(), y.copy()\n",
+    "  norm = transforms.Normalize([0.6851, 0.5235], [0.0820, 0.1102])\n",
+    "  #label[0][0][0] = 255\n",
+    "  # convert to PIL for easier transforms\n",
+    "  im_c1 = Image.fromarray(im[0]).resize((512,512))\n",
+    "  im_c2 = Image.fromarray(im[1]).resize((512,512))\n",
+    "  label = Image.fromarray(label.squeeze()).resize((512,512))\n",
+    "\n",
+    "  im_c1s = [F.crop(im_c1, 0, 0, 256, 256), F.crop(im_c1, 0, 256, 256, 256),\n",
+    "            F.crop(im_c1, 256, 0, 256, 256), F.crop(im_c1, 256, 256, 256, 256)]\n",
+    "  im_c2s = [F.crop(im_c2, 0, 0, 256, 256), F.crop(im_c2, 0, 256, 256, 256),\n",
+    "            F.crop(im_c2, 256, 0, 256, 256), F.crop(im_c2, 256, 256, 256, 256)]\n",
+    "  labels = [F.crop(label, 0, 0, 256, 256), F.crop(label, 0, 256, 256, 256),\n",
+    "            F.crop(label, 256, 0, 256, 256), F.crop(label, 256, 256, 256, 256)]\n",
+    "\n",
+    "\n",
+    "  ims = [torch.stack((transforms.ToTensor()(x).squeeze(),\n",
+    "                    transforms.ToTensor()(y).squeeze()))\n",
+    "                    for (x,y) in zip(im_c1s, im_c2s)]\n",
+    "  ims = [norm(im) for im in ims]\n",
+    "  ims = torch.stack(ims)\n",
+    "  labels = [(transforms.ToTensor()(label).squeeze()) for label in labels]\n",
+    "  labels = torch.stack(labels)\n",
+    "  if torch.sum(labels.gt(.003) * labels.lt(.004)):\n",
+    "    labels *= 255\n",
+    "  labels = labels.round()\n",
+    "  return ims, labels\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "ImyxBVt52HnH"
+   },
+   "source": [
+    "Read in Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 190
+    },
+    "colab_type": "code",
+    "id": "AcdNIUJyw11-",
+    "outputId": "8cd3000e-33f8-49e2-b470-58362c763e0f"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: rasterio in /opt/anaconda3/lib/python3.7/site-packages (1.1.3)\n",
+      "Requirement already satisfied: affine in /opt/anaconda3/lib/python3.7/site-packages (from rasterio) (2.3.0)\n",
+      "Requirement already satisfied: cligj>=0.5 in /opt/anaconda3/lib/python3.7/site-packages (from rasterio) (0.5.0)\n",
+      "Requirement already satisfied: click-plugins in /opt/anaconda3/lib/python3.7/site-packages (from rasterio) (1.1.1)\n",
+      "Requirement already satisfied: attrs in /opt/anaconda3/lib/python3.7/site-packages (from rasterio) (19.3.0)\n",
+      "Requirement already satisfied: click<8,>=4.0 in /opt/anaconda3/lib/python3.7/site-packages (from rasterio) (7.0)\n",
+      "Requirement already satisfied: numpy in /opt/anaconda3/lib/python3.7/site-packages (from rasterio) (1.17.4)\n",
+      "Requirement already satisfied: snuggs>=1.4.1 in /opt/anaconda3/lib/python3.7/site-packages (from rasterio) (1.4.7)\n",
+      "Requirement already satisfied: pyparsing>=2.1.6 in /opt/anaconda3/lib/python3.7/site-packages (from snuggs>=1.4.1->rasterio) (2.4.5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install rasterio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "eQqagN7vu7jx"
+   },
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "from PIL import Image\n",
+    "import rasterio\n",
+    "import numpy as np\n",
+    "import os\n",
+    "BASEDIR = 'files3/'\n",
+    "\n",
+    "def getArr(fname):\n",
+    "  return rasterio.open(BASEDIR + fname).read()\n",
+    "\n",
+    "\n",
+    "def download_perm_water_data_from_file(fname):\n",
+    "  with open(fname) as f:\n",
+    "    data_fnames = [tuple(line) for line in csv.reader(f)]\n",
+    "  i = 0\n",
+    "  data = []\n",
+    "  for (x,y) in data_fnames:\n",
+    "    arr_x, arr_y = getArr(os.path.join(\"S1Perm\", x)), getArr(os.path.join(\"PermJRC\", y))\n",
+    "    if np.sum((arr_x != arr_x)) == 0:\n",
+    "      ignore = (arr_y == -1)\n",
+    "      ignore = ((np.uint8(ignore) * -1) * 256) + 1\n",
+    "      arr_y *= ignore\n",
+    "      data.append((arr_x, arr_y))\n",
+    "      i+=1\n",
+    "      print(i)\n",
+    "    else:\n",
+    "      print(\"skipping nan\")\n",
+    "  return data\n",
+    "\n",
+    "def download_perm_train_data():\n",
+    "  TRAINING_DATA_FILE = BASEDIR + 'permanent_water_train_data.csv'\n",
+    "  return download_perm_water_data_from_file(TRAINING_DATA_FILE)\n",
+    "\n",
+    "def download_perm_valid_data():\n",
+    "  VALID_DATA_FILE = BASEDIR + 'permanent_water_validation_data.csv'\n",
+    "  return download_perm_water_data_from_file(VALID_DATA_FILE)\n",
+    "\n",
+    "def download_perm_test_data():\n",
+    "  TEST_DATA_FILE = BASEDIR + 'permanent_water_test_data.csv'\n",
+    "  return download_perm_water_data_from_file(TEST_DATA_FILE)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ZjFk6FlhHAR4"
+   },
+   "outputs": [],
+   "source": [
+    "from time import time\n",
+    "\n",
+    "def getArrFlood(fname):\n",
+    "  return rasterio.open(fname).read()\n",
+    "\n",
+    "def download_flood_water_data_from_list(l):\n",
+    "  print(\"WHYYYYY\")\n",
+    "  i= 0\n",
+    "  tot_nan = 0\n",
+    "  tot_good = 0\n",
+    "  flood_data = []\n",
+    "  for (im_fname, mask_fname) in l:\n",
+    "    if not os.path.exists(os.path.join(\"files3/\", im_fname)):\n",
+    "      print(os.path.join(\"files3/\", im_fname))\n",
+    "      continue\n",
+    "    arr_x = np.nan_to_num(getArrFlood(os.path.join(\"files3/\", im_fname)))\n",
+    "    arr_y = getArrFlood(os.path.join(\"files3/\", mask_fname))\n",
+    "    ignore = (arr_y == -1)\n",
+    "    ignore = ((np.uint8(ignore) * -1) * 256) + 1\n",
+    "    arr_y *= ignore\n",
+    "    arr_y = np.uint8(getArrFlood(os.path.join(\"files3/\", mask_fname)))\n",
+    "    if np.sum((arr_y != arr_y)) == 0:\n",
+    "      arr_x = np.clip(arr_x, -50, 1)\n",
+    "      arr_x = (arr_x + 50) / 51\n",
+    "      if i % 100 == 0:\n",
+    "        print(i)\n",
+    "        print(im_fname, mask_fname)\n",
+    "      i += 1\n",
+    "      flood_data.append((arr_x,arr_y))\n",
+    "    else:\n",
+    "      print(\"skipping nan\")\n",
+    "  print(i)\n",
+    "  return flood_data\n",
+    "\n",
+    "\n",
+    "def load_flood_train_data():\n",
+    "  basedir = \"files4/\"\n",
+    "  fname = \"files3/flood_train_data.csv\"\n",
+    "  with open(fname) as f:\n",
+    "    fname = [tuple(line) for line in csv.reader(f)]\n",
+    "  return download_flood_water_data_from_list(fname)\n",
+    "\n",
+    "def load_weak_flood_train_data():\n",
+    "  basedir = \"files4/\"\n",
+    "  files = [(os.path.join(\"S1_NoQC\", x[1]), os.path.join(\"NoQC\", x[0])) for x in zip(sorted(os.listdir(\"files3/NoQC\")), sorted(os.listdir(\"files3/S1_NoQC\")))]\n",
+    "  files = [x for x in files if \"Bolivia\" not in x[0]]\n",
+    "  print(files[0:10])\n",
+    "  return download_flood_water_data_from_list(files)\n",
+    "\n",
+    "def load_weak_flood_S1_train_data():\n",
+    "  basedir = \"files4/\"\n",
+    "  files = [(os.path.join(\"S1_NoQC\", x[1]), os.path.join(\"S1Flood_NoQC\", x[0])) for x in zip(sorted(os.listdir(\"files3/S1Flood_NoQC\")), sorted(os.listdir(\"files3/S1_NoQC\")))]\n",
+    "  print(files[0:10])\n",
+    "  return download_flood_water_data_from_list(files)\n",
+    "\n",
+    "def load_flood_valid_data():\n",
+    "  basedir = \"files4/\"\n",
+    "  fname = \"files3/flood_valid_data.csv\"\n",
+    "  with open(fname) as f:\n",
+    "    fname = [tuple(line) for line in csv.reader(f)]\n",
+    "  print(fname, \"files!\")\n",
+    "  return download_flood_water_data_from_list(fname)\n",
+    "\n",
+    "def load_flood_test_data():\n",
+    "  basedir = \"files4/\"\n",
+    "  fname = \"files3/flood_test_data.csv\"\n",
+    "  with open(fname) as f:\n",
+    "    fname = [tuple(line) for line in csv.reader(f)]\n",
+    "  return download_flood_water_data_from_list(fname)\n",
+    "\n",
+    "def load_flood_bolivia_test_data():\n",
+    "  basedir = \"files4/\"\n",
+    "  fname = \"files3/flood_bolivia_data.csv\"\n",
+    "  with open(fname) as f:\n",
+    "    fname = [tuple(line) for line in csv.reader(f)]\n",
+    "  return download_flood_water_data_from_list(fname)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "colab_type": "code",
+    "id": "8kCCtfs7ULx_",
+    "outputId": "4c5f0e83-27b2-4fd1-c11a-326fd238197a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WHYYYYY\n",
+      "0\n",
+      "S1/Ghana_103272_S1.tif QC_v2/Ghana_103272_QC.tif\n",
+      "100\n",
+      "S1/Pakistan_132143_S1.tif QC_v2/Pakistan_132143_QC.tif\n",
+      "200\n",
+      "S1/Sri-Lanka_916628_S1.tif QC_v2/Sri-Lanka_916628_QC.tif\n",
+      "252\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_data = load_flood_train_data()#load_weak_flood_S1_train_data()\n",
+    "train_dataset = InMemoryDataset(train_data, processAndAugment)\n",
+    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True, sampler=None,\n",
+    "                  batch_sampler=None, num_workers=0, collate_fn=None,\n",
+    "                  pin_memory=True, drop_last=False, timeout=0,\n",
+    "                  worker_init_fn=None)\n",
+    "train_iter = iter(train_loader)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "colab_type": "code",
+    "id": "GDa86WdzgdkH",
+    "outputId": "f29debe4-669e-4955-d850-7aab77fd0834"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('S1/Ghana_5079_S1.tif', 'QC_v2/Ghana_5079_QC.tif'), ('S1/Ghana_895194_S1.tif', 'QC_v2/Ghana_895194_QC.tif'), ('S1/Ghana_868803_S1.tif', 'QC_v2/Ghana_868803_QC.tif'), ('S1/Ghana_142312_S1.tif', 'QC_v2/Ghana_142312_QC.tif'), ('S1/Ghana_234935_S1.tif', 'QC_v2/Ghana_234935_QC.tif'), ('S1/Ghana_132163_S1.tif', 'QC_v2/Ghana_132163_QC.tif'), ('S1/Ghana_495107_S1.tif', 'QC_v2/Ghana_495107_QC.tif'), ('S1/Ghana_124834_S1.tif', 'QC_v2/Ghana_124834_QC.tif'), ('S1/Ghana_1033830_S1.tif', 'QC_v2/Ghana_1033830_QC.tif'), ('S1/Ghana_277_S1.tif', 'QC_v2/Ghana_277_QC.tif'), ('S1/Ghana_308249_S1.tif', 'QC_v2/Ghana_308249_QC.tif'), ('S1/India_1050276_S1.tif', 'QC_v2/India_1050276_QC.tif'), ('S1/India_764946_S1.tif', 'QC_v2/India_764946_QC.tif'), ('S1/India_118868_S1.tif', 'QC_v2/India_118868_QC.tif'), ('S1/India_533192_S1.tif', 'QC_v2/India_533192_QC.tif'), ('S1/India_180633_S1.tif', 'QC_v2/India_180633_QC.tif'), ('S1/India_244057_S1.tif', 'QC_v2/India_244057_QC.tif'), ('S1/India_691027_S1.tif', 'QC_v2/India_691027_QC.tif'), ('S1/India_769408_S1.tif', 'QC_v2/India_769408_QC.tif'), ('S1/India_1018317_S1.tif', 'QC_v2/India_1018317_QC.tif'), ('S1/India_869358_S1.tif', 'QC_v2/India_869358_QC.tif'), ('S1/India_164336_S1.tif', 'QC_v2/India_164336_QC.tif'), ('S1/India_70352_S1.tif', 'QC_v2/India_70352_QC.tif'), ('S1/India_833266_S1.tif', 'QC_v2/India_833266_QC.tif'), ('S1/India_1068117_S1.tif', 'QC_v2/India_1068117_QC.tif'), ('S1/Mekong_1149855_S1.tif', 'QC_v2/Mekong_1149855_QC.tif'), ('S1/Mekong_977338_S1.tif', 'QC_v2/Mekong_977338_QC.tif'), ('S1/Mekong_474783_S1.tif', 'QC_v2/Mekong_474783_QC.tif'), ('S1/Mekong_293769_S1.tif', 'QC_v2/Mekong_293769_QC.tif'), ('S1/Mekong_1413877_S1.tif', 'QC_v2/Mekong_1413877_QC.tif'), ('S1/Mekong_98310_S1.tif', 'QC_v2/Mekong_98310_QC.tif'), ('S1/Nigeria_31096_S1.tif', 'QC_v2/Nigeria_31096_QC.tif'), ('S1/Nigeria_984831_S1.tif', 'QC_v2/Nigeria_984831_QC.tif'), ('S1/Nigeria_1095404_S1.tif', 'QC_v2/Nigeria_1095404_QC.tif'), ('S1/Nigeria_820924_S1.tif', 'QC_v2/Nigeria_820924_QC.tif'), ('S1/Pakistan_43105_S1.tif', 'QC_v2/Pakistan_43105_QC.tif'), ('S1/Pakistan_94095_S1.tif', 'QC_v2/Pakistan_94095_QC.tif'), ('S1/Pakistan_210595_S1.tif', 'QC_v2/Pakistan_210595_QC.tif'), ('S1/Pakistan_1027214_S1.tif', 'QC_v2/Pakistan_1027214_QC.tif'), ('S1/Pakistan_336228_S1.tif', 'QC_v2/Pakistan_336228_QC.tif'), ('S1/Pakistan_9684_S1.tif', 'QC_v2/Pakistan_9684_QC.tif'), ('S1/Paraguay_305760_S1.tif', 'QC_v2/Paraguay_305760_QC.tif'), ('S1/Paraguay_648632_S1.tif', 'QC_v2/Paraguay_648632_QC.tif'), ('S1/Paraguay_172476_S1.tif', 'QC_v2/Paraguay_172476_QC.tif'), ('S1/Paraguay_581976_S1.tif', 'QC_v2/Paraguay_581976_QC.tif'), ('S1/Paraguay_284928_S1.tif', 'QC_v2/Paraguay_284928_QC.tif'), ('S1/Paraguay_1019808_S1.tif', 'QC_v2/Paraguay_1019808_QC.tif'), ('S1/Paraguay_76868_S1.tif', 'QC_v2/Paraguay_76868_QC.tif'), ('S1/Paraguay_252217_S1.tif', 'QC_v2/Paraguay_252217_QC.tif'), ('S1/Paraguay_205585_S1.tif', 'QC_v2/Paraguay_205585_QC.tif'), ('S1/Paraguay_7894_S1.tif', 'QC_v2/Paraguay_7894_QC.tif'), ('S1/Paraguay_896458_S1.tif', 'QC_v2/Paraguay_896458_QC.tif'), ('S1/Paraguay_657443_S1.tif', 'QC_v2/Paraguay_657443_QC.tif'), ('S1/Paraguay_934240_S1.tif', 'QC_v2/Paraguay_934240_QC.tif'), ('S1/Paraguay_153941_S1.tif', 'QC_v2/Paraguay_153941_QC.tif'), ('S1/Somalia_12849_S1.tif', 'QC_v2/Somalia_12849_QC.tif'), ('S1/Somalia_256539_S1.tif', 'QC_v2/Somalia_256539_QC.tif'), ('S1/Somalia_61368_S1.tif', 'QC_v2/Somalia_61368_QC.tif'), ('S1/Somalia_649376_S1.tif', 'QC_v2/Somalia_649376_QC.tif'), ('S1/Somalia_167787_S1.tif', 'QC_v2/Somalia_167787_QC.tif'), ('S1/Spain_1199913_S1.tif', 'QC_v2/Spain_1199913_QC.tif'), ('S1/Spain_8372658_S1.tif', 'QC_v2/Spain_8372658_QC.tif'), ('S1/Spain_7604243_S1.tif', 'QC_v2/Spain_7604243_QC.tif'), ('S1/Spain_6537196_S1.tif', 'QC_v2/Spain_6537196_QC.tif'), ('S1/Spain_4282030_S1.tif', 'QC_v2/Spain_4282030_QC.tif'), ('S1/Spain_8565131_S1.tif', 'QC_v2/Spain_8565131_QC.tif'), ('S1/Sri-Lanka_85652_S1.tif', 'QC_v2/Sri-Lanka_85652_QC.tif'), ('S1/Sri-Lanka_63307_S1.tif', 'QC_v2/Sri-Lanka_63307_QC.tif'), ('S1/Sri-Lanka_612594_S1.tif', 'QC_v2/Sri-Lanka_612594_QC.tif'), ('S1/Sri-Lanka_132922_S1.tif', 'QC_v2/Sri-Lanka_132922_QC.tif'), ('S1/Sri-Lanka_236030_S1.tif', 'QC_v2/Sri-Lanka_236030_QC.tif'), ('S1/Sri-Lanka_31559_S1.tif', 'QC_v2/Sri-Lanka_31559_QC.tif'), ('S1/Sri-Lanka_236628_S1.tif', 'QC_v2/Sri-Lanka_236628_QC.tif'), ('S1/Sri-Lanka_101973_S1.tif', 'QC_v2/Sri-Lanka_101973_QC.tif'), ('S1/Sri-Lanka_321316_S1.tif', 'QC_v2/Sri-Lanka_321316_QC.tif'), ('S1/USA_826217_S1.tif', 'QC_v2/USA_826217_QC.tif'), ('S1/USA_741073_S1.tif', 'QC_v2/USA_741073_QC.tif'), ('S1/USA_275372_S1.tif', 'QC_v2/USA_275372_QC.tif'), ('S1/USA_19225_S1.tif', 'QC_v2/USA_19225_QC.tif'), ('S1/USA_366607_S1.tif', 'QC_v2/USA_366607_QC.tif'), ('S1/USA_308150_S1.tif', 'QC_v2/USA_308150_QC.tif'), ('S1/USA_1039203_S1.tif', 'QC_v2/USA_1039203_QC.tif'), ('S1/USA_251323_S1.tif', 'QC_v2/USA_251323_QC.tif'), ('S1/USA_1082482_S1.tif', 'QC_v2/USA_1082482_QC.tif'), ('S1/USA_225017_S1.tif', 'QC_v2/USA_225017_QC.tif'), ('S1/USA_986268_S1.tif', 'QC_v2/USA_986268_QC.tif'), ('S1/USA_646878_S1.tif', 'QC_v2/USA_646878_QC.tif'), ('S1/USA_761032_S1.tif', 'QC_v2/USA_761032_QC.tif'), ('S1/USA_741178_S1.tif', 'QC_v2/USA_741178_QC.tif')] files!\n",
+      "WHYYYYY\n",
+      "0\n",
+      "S1/Ghana_5079_S1.tif QC_v2/Ghana_5079_QC.tif\n",
+      "89\n"
+     ]
+    }
+   ],
+   "source": [
+    "valid_data = load_flood_valid_data() #download_perm_valid_data()\n",
+    "valid_dataset = InMemoryDataset(valid_data, processTestIm)\n",
+    "valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=4, shuffle=True, sampler=None,\n",
+    "                  batch_sampler=None, num_workers=0, collate_fn=lambda x: (torch.cat([a[0] for a in x], 0), torch.cat([a[1] for a in x], 0)),\n",
+    "                  pin_memory=True, drop_last=False, timeout=0,\n",
+    "                  worker_init_fn=None)\n",
+    "valid_iter = iter(valid_loader)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "Q00osps22aUU"
+   },
+   "source": [
+    "Set up net/parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "colab_type": "code",
+    "id": "DF4k-jQM2gCP",
+    "outputId": "1c189728-d219-4107-926b-523962735635"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FCN(\n",
+       "  (backbone): IntermediateLayerGetter(\n",
+       "    (conv1): Conv2d(2, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+       "    (bn1): GroupNorm(16, 64, eps=1e-05, affine=True)\n",
+       "    (relu): ReLU(inplace=True)\n",
+       "    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+       "    (layer1): Sequential(\n",
+       "      (0): Bottleneck(\n",
+       "        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 64, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn2): GroupNorm(16, 64, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "        (downsample): Sequential(\n",
+       "          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "          (1): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        )\n",
+       "      )\n",
+       "      (1): Bottleneck(\n",
+       "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 64, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn2): GroupNorm(16, 64, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "      )\n",
+       "      (2): Bottleneck(\n",
+       "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 64, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn2): GroupNorm(16, 64, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (layer2): Sequential(\n",
+       "      (0): Bottleneck(\n",
+       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "        (bn2): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 512, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "        (downsample): Sequential(\n",
+       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "          (1): GroupNorm(16, 512, eps=1e-05, affine=True)\n",
+       "        )\n",
+       "      )\n",
+       "      (1): Bottleneck(\n",
+       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn2): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 512, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "      )\n",
+       "      (2): Bottleneck(\n",
+       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn2): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 512, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "      )\n",
+       "      (3): Bottleneck(\n",
+       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn2): GroupNorm(16, 128, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 512, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (layer3): Sequential(\n",
+       "      (0): Bottleneck(\n",
+       "        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "        (bn2): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 1024, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "        (downsample): Sequential(\n",
+       "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "          (1): GroupNorm(16, 1024, eps=1e-05, affine=True)\n",
+       "        )\n",
+       "      )\n",
+       "      (1): Bottleneck(\n",
+       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
+       "        (bn2): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 1024, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "      )\n",
+       "      (2): Bottleneck(\n",
+       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
+       "        (bn2): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 1024, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "      )\n",
+       "      (3): Bottleneck(\n",
+       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
+       "        (bn2): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 1024, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "      )\n",
+       "      (4): Bottleneck(\n",
+       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
+       "        (bn2): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 1024, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "      )\n",
+       "      (5): Bottleneck(\n",
+       "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
+       "        (bn2): GroupNorm(16, 256, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 1024, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (layer4): Sequential(\n",
+       "      (0): Bottleneck(\n",
+       "        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 512, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)\n",
+       "        (bn2): GroupNorm(16, 512, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 2048, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "        (downsample): Sequential(\n",
+       "          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "          (1): GroupNorm(16, 2048, eps=1e-05, affine=True)\n",
+       "        )\n",
+       "      )\n",
+       "      (1): Bottleneck(\n",
+       "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 512, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)\n",
+       "        (bn2): GroupNorm(16, 512, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 2048, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "      )\n",
+       "      (2): Bottleneck(\n",
+       "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn1): GroupNorm(16, 512, eps=1e-05, affine=True)\n",
+       "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)\n",
+       "        (bn2): GroupNorm(16, 512, eps=1e-05, affine=True)\n",
+       "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (bn3): GroupNorm(16, 2048, eps=1e-05, affine=True)\n",
+       "        (relu): ReLU(inplace=True)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (classifier): FCNHead(\n",
+       "    (0): Conv2d(2048, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "    (1): GroupNorm(16, 512, eps=1e-05, affine=True)\n",
+       "    (2): ReLU()\n",
+       "    (3): Dropout(p=0.1, inplace=False)\n",
+       "    (4): Conv2d(512, 2, kernel_size=(1, 1), stride=(1, 1))\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torchvision.models as models\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "net = models.segmentation.fcn_resnet50(pretrained=False, num_classes=2, pretrained_backbone=False)\n",
+    "net.backbone.conv1 = nn.Conv2d(2, 64, kernel_size=7, stride=2, padding=3,\n",
+    "                               bias=False)\n",
+    "criterion = nn.CrossEntropyLoss(weight=torch.tensor([1,8]).float().cuda(), ignore_index=255) # \n",
+    "#criterion = nn.CrossEntropyLoss(ignore_index=255)\n",
+    "optimizer = torch.optim.AdamW(net.parameters(),lr=LR)\n",
+    "scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, len(train_loader) * 10, T_mult=2, eta_min=0, last_epoch=-1)\n",
+    "def convertBNtoGN(module, num_groups=16):\n",
+    "  if isinstance(module, torch.nn.modules.batchnorm.BatchNorm2d):\n",
+    "    return nn.GroupNorm(num_groups, module.num_features,\n",
+    "                        eps=module.eps, affine=module.affine)\n",
+    "    if module.affine:\n",
+    "        mod.weight.data = module.weight.data.clone().detach()\n",
+    "        mod.bias.data = module.bias.data.clone().detach()\n",
+    "\n",
+    "  for name, child in module.named_children():\n",
+    "      module.add_module(name, convertBNtoGN(child, num_groups=num_groups))\n",
+    "\n",
+    "  return module\n",
+    "\n",
+    "net = convertBNtoGN(net)\n",
+    "net"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "2jwEvqA885dz"
+   },
+   "source": [
+    "Utility Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "WMd65d3784oH"
+   },
+   "outputs": [],
+   "source": [
+    "def computeIOU(output, target):\n",
+    "  output = torch.argmax(output, dim=1).flatten() \n",
+    "  target = target.flatten()\n",
+    "  no_ignore = target.ne(255).cuda()\n",
+    "  output = output.masked_select(no_ignore)\n",
+    "  target = target.masked_select(no_ignore)\n",
+    "  intersection = torch.sum(output * target)\n",
+    "  union = torch.sum(target) + torch.sum(output) - intersection\n",
+    "  iou = (intersection + .0000001) / (union + .0000001)\n",
+    "  if iou != iou:\n",
+    "    print(\"failed, replacing with 0\")\n",
+    "    iou = torch.tensor(0).float()\n",
+    "  return iou\n",
+    "  \n",
+    "\n",
+    "def computeAccuracy(output, target):\n",
+    "  output = torch.argmax(output, dim=1).flatten() \n",
+    "  target = target.flatten()\n",
+    "  no_ignore = target.ne(255).cuda()\n",
+    "  output = output.masked_select(no_ignore)\n",
+    "  target = target.masked_select(no_ignore)\n",
+    "  correct = torch.sum(output.eq(target))\n",
+    "  return correct.float() / len(target)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 51
+    },
+    "colab_type": "code",
+    "id": "jiP-eu3NLxIA",
+    "outputId": "73ba33de-d99b-4957-f0e7-ee4f6beb172a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(7.6294e-13, device='cuda:0')\n",
+      "tensor(1., device='cuda:0')\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_1 = torch.ones(2, 2, 256, 256)\n",
+    "test_2 = torch.zeros(2, 256, 256)\n",
+    "test_3 = torch.ones(2, 256, 256)\n",
+    "\n",
+    "\n",
+    "print(computeIOU(test_1.cuda(), test_2.cuda()))\n",
+    "print(computeIOU(test_1.cuda(), test_3.cuda()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "XBHHATa-2h3A"
+   },
+   "source": [
+    "Train/Validation functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "pg1PiQ192g3g"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "training_losses = []\n",
+    "training_accuracies = []\n",
+    "training_ious = []\n",
+    "valid_losses = []\n",
+    "valid_accuracies = []\n",
+    "valid_ious = []\n",
+    "\n",
+    "def train(inputs, labels, net, optimizer, scheduler):\n",
+    "  global running_loss\n",
+    "  global running_iou\n",
+    "  global running_count\n",
+    "  global running_accuracy\n",
+    "  # zero the parameter gradients\n",
+    "  optimizer.zero_grad()\n",
+    "  net = net.cuda()\n",
+    "  # forward + backward + optimize\n",
+    "  outputs = net(inputs.cuda())\n",
+    "  loss = criterion(outputs[\"out\"], labels.long().cuda())\n",
+    "  loss.backward()\n",
+    "  optimizer.step()\n",
+    "  scheduler.step()\n",
+    "\n",
+    "  running_loss += loss\n",
+    "  running_iou += computeIOU(outputs[\"out\"], labels.cuda())\n",
+    "  running_accuracy += computeAccuracy(outputs[\"out\"], labels.cuda())\n",
+    "  running_count += 1\n",
+    "\n",
+    "def validation_loop(validation_data_loader, net):\n",
+    "  global running_loss\n",
+    "  global running_iou\n",
+    "  global running_count\n",
+    "  global running_accuracy\n",
+    "  global max_valid_iou\n",
+    "\n",
+    "  global training_losses\n",
+    "  global training_accuracies\n",
+    "  global training_ious\n",
+    "  global valid_losses\n",
+    "  global valid_accuracies\n",
+    "  global valid_ious\n",
+    "\n",
+    "  net = net.eval()\n",
+    "  net = net.cuda()\n",
+    "  count = 0\n",
+    "  iou = 0\n",
+    "  loss = 0\n",
+    "  accuracy = 0\n",
+    "  with torch.no_grad():\n",
+    "      for (images, labels) in validation_data_loader:\n",
+    "          net = net.cuda()\n",
+    "          outputs = net(images.cuda())\n",
+    "          valid_loss = criterion(outputs[\"out\"], labels.long().cuda())\n",
+    "          valid_iou = computeIOU(outputs[\"out\"], labels.cuda())\n",
+    "          valid_accuracy = computeAccuracy(outputs[\"out\"], labels.cuda())\n",
+    "          iou += valid_iou\n",
+    "          loss += valid_loss\n",
+    "          accuracy += valid_accuracy\n",
+    "          count += 1\n",
+    "\n",
+    "  iou = iou / count\n",
+    "  accuracy = accuracy / count\n",
+    "\n",
+    "  if iou > max_valid_iou:\n",
+    "    max_valid_iou = iou\n",
+    "    save_path = os.path.join(\"checkpoints\", \"{}_{}_{}.cp\".format(RUNNAME, i, iou.item()))\n",
+    "    torch.save(net.state_dict(), save_path)\n",
+    "    print(\"model saved at\", save_path)\n",
+    "\n",
+    "  loss = loss / count\n",
+    "  print(\"Training Loss:\", running_loss / running_count)\n",
+    "  print(\"Training IOU:\", running_iou / running_count)\n",
+    "  print(\"Training Accuracy:\", running_accuracy / running_count)\n",
+    "  print(\"Validation Loss:\", loss)\n",
+    "  print(\"Validation IOU:\", iou)\n",
+    "  print(\"Validation Accuracy:\", accuracy)\n",
+    "\n",
+    "\n",
+    "  training_losses.append(running_loss / running_count)\n",
+    "  training_accuracies.append(running_accuracy / running_count)\n",
+    "  training_ious.append(running_iou / running_count)\n",
+    "  valid_losses.append(loss)\n",
+    "  valid_accuracies.append(accuracy)\n",
+    "  valid_ious.append(iou)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test_loop(test_data_loader, net):\n",
+    "  net = net.eval()\n",
+    "  net = net.cuda()\n",
+    "  count = 0\n",
+    "  iou = 0\n",
+    "  loss = 0\n",
+    "  accuracy = 0\n",
+    "  with torch.no_grad():\n",
+    "      for (images, labels) in tqdm(test_data_loader):\n",
+    "          net = net.cuda()\n",
+    "          outputs = net(images.cuda())\n",
+    "          valid_loss = criterion(outputs[\"out\"], labels.long().cuda())\n",
+    "          valid_iou = computeIOU(outputs[\"out\"], labels.cuda())\n",
+    "          iou += valid_iou\n",
+    "          accuracy += computeAccuracy(outputs[\"out\"], labels.cuda())\n",
+    "          count += 1\n",
+    "\n",
+    "  iou = iou / count\n",
+    "  print(\"Test IOU:\", iou)\n",
+    "  print(\"Test Accuracy:\", accuracy / count)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "4nU8Rxpa2l3p"
+   },
+   "source": [
+    "Train Loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Y0h841wY2npi"
+   },
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "running_loss = 0\n",
+    "running_iou = 0\n",
+    "running_count = 0\n",
+    "running_accuracy = 0\n",
+    "\n",
+    "training_losses = []\n",
+    "training_accuracies = []\n",
+    "training_ious = []\n",
+    "valid_losses = []\n",
+    "valid_accuracies = []\n",
+    "valid_ious = []\n",
+    "\n",
+    "\n",
+    "def train_epoch(net, optimizer, scheduler, train_iter):\n",
+    "  for (inputs, labels) in tqdm(train_iter):\n",
+    "    train(inputs.cuda(), labels.cuda(), net.cuda(), optimizer, scheduler)\n",
+    " \n",
+    "\n",
+    "def train_validation_loop(net, optimizer, scheduler, train_loader,\n",
+    "                          valid_loader, num_epochs, cur_epoch):\n",
+    "  global running_loss\n",
+    "  global running_iou\n",
+    "  global running_count\n",
+    "  global running_accuracy\n",
+    "  net = net.train()\n",
+    "  running_loss = 0\n",
+    "  running_iou = 0\n",
+    "  running_count = 0\n",
+    "  running_accuracy = 0\n",
+    "  for i in tqdm(range(num_epochs)):\n",
+    "    train_iter = iter(train_loader)\n",
+    "    train_epoch(net, optimizer, scheduler, train_iter)\n",
+    "  clear_output()\n",
+    "  print(\"Current Epoch:\", cur_epoch)\n",
+    "  validation_loop(iter(valid_loader), net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "xPSjS-pf2vHD"
+   },
+   "source": [
+    "test_loader[0] == usa_loader[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 482,
+     "referenced_widgets": [
+      "ed787105fcaa4c3c812a76c4c603d5f5",
+      "25fb338b9ebb4053ac0218c253389a46",
+      "0a5bf508b6b44bee8ce55182e0af6fec",
+      "51b4002a832f4754b087443f75d3c97f",
+      "8a35991cccc84d0494384ac2c018727c",
+      "f93cc6f609e94593a6b7133ae868285e",
+      "b9ec39f9a47248928f102018e2e0f116",
+      "ccd281b6c8104e4fa01533165fbb2ea0",
+      "8c714337e16c446281218656cb330d37",
+      "82300de7ef9c4460886669b056253500",
+      "44ca7d253a0c46b3b704fe67a70c52eb",
+      "cca70317fa824574aa562d981391c46f",
+      "ca76bbb74d4d45bc8a7ac4e1b5466e1b",
+      "84eab0bbbc3d46e89df8e66cc3134be4",
+      "e5e4e39f80ff4ff4b1e2e723d71e64fa",
+      "c071047b9b5b420dbb5233397841a7b5"
+     ]
+    },
+    "colab_type": "code",
+    "id": "ugcVXfKj1kg8",
+    "outputId": "0cc7ebd8-a627-485b-c951-04398eff0fc2"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current Epoch: 0\n",
+      "model saved at checkpoints/5e4_handlabeled_0_0_0.1470426768064499.cp\n",
+      "Training Loss: tensor(0.7198, device='cuda:0', grad_fn=<DivBackward0>)\n",
+      "Training IOU: tensor(0.1032, device='cuda:0')\n",
+      "Training Accuracy: tensor(0.4429, device='cuda:0')\n",
+      "Validation Loss: tensor(0.6704, device='cuda:0')\n",
+      "Validation IOU: tensor(0.1470, device='cuda:0')\n",
+      "Validation Accuracy: tensor(0.4021, device='cuda:0')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "None"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max valid iou: tensor(0.1470, device='cuda:0')\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74191fb29cb844a38e740de2f8c53a01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=10.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d360cde868df4443bf6dbd7b77b9cc70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dddc30b971604eb5a97e2f9d092cbe26",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93af80f14c6a48fd94b0df773cd01ff2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05cfe88e167a4b17ab067a030a7aaacd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "310d52f06bc640518ab06bf161b2686f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e55b51bdc80459fa59cdf1ba4dc15d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a51d2dd5c9d453e8be0233d1337d2af",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "425eefb72ef047dfbccfc3576a37e79a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a29c2a5e936b4e0fa3890eb550026f08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from IPython.display import display\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "max_valid_iou = 0\n",
+    "start = 0\n",
+    "\n",
+    "epochs = []\n",
+    "training_losses = []\n",
+    "training_accuracies = []\n",
+    "training_ious = []\n",
+    "valid_losses = []\n",
+    "valid_accuracies = []\n",
+    "valid_ious = []\n",
+    "\n",
+    "\n",
+    "\n",
+    "for i in range(start, 1000):\n",
+    "  train_validation_loop(net, optimizer, scheduler, train_loader, valid_loader, 10, i)\n",
+    "  epochs.append(i)\n",
+    "  x = epochs\n",
+    "  plt.plot(x, training_losses, label='training losses')\n",
+    "  plt.plot(x, training_accuracies, 'tab:orange', label='training accuracy')\n",
+    "  plt.plot(x, training_ious, 'tab:purple', label='training iou')\n",
+    "  plt.plot(x, valid_losses, label='valid losses')\n",
+    "  plt.plot(x, valid_accuracies, 'tab:red',label='valid accuracy')\n",
+    "  plt.plot(x, valid_ious, 'tab:green',label='valid iou')\n",
+    "  plt.legend(loc=\"upper left\")\n",
+    "\n",
+    "  display(plt.show())\n",
+    "\n",
+    "  print(\"max valid iou:\", max_valid_iou)\n",
+    "  #test_loop(test_loader, net)\n",
+    "  #test_loop(bolivia_loader, net)\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "authorship_tag": "ABX9TyMf48pVkyLxuYqUrzgjC1Nw",
+   "collapsed_sections": [],
+   "include_colab_link": true,
+   "machine_shape": "hm",
+   "name": "Main Training Stuff.ipynb",
+   "provenance": []
+  },
+  "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",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
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Sen1Floods11/gsutil.log

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+nohup: ignoring input
+CommandException: The rsync command requires at least 2 arguments. Usage:
+
+  gsutil rsync [OPTION]... src_url dst_url
+
+For additional help run:
+  gsutil help rsync

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Sen1Floods11/v1.1/catalog.zip

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Sen1Floods11/v1.1/catalog/catalog.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Bolivia_103757_label/Bolivia_103757_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Bolivia_129334_label/Bolivia_129334_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Bolivia_195474_label/Bolivia_195474_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Bolivia_23014_label/Bolivia_23014_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Bolivia_233925_label/Bolivia_233925_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Bolivia_242570_label/Bolivia_242570_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Bolivia_290290_label/Bolivia_290290_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Bolivia_294583_label/Bolivia_294583_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Bolivia_312675_label/Bolivia_312675_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Bolivia_360519_label/Bolivia_360519_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Bolivia_379434_label/Bolivia_379434_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Bolivia_432776_label/Bolivia_432776_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Bolivia_60373_label/Bolivia_60373_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Bolivia_76104_label/Bolivia_76104_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Ghana_11745_label/Ghana_11745_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Ghana_124834_label/Ghana_124834_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Ghana_128663_label/Ghana_128663_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Ghana_135389_label/Ghana_135389_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Ghana_141910_label/Ghana_141910_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Ghana_142312_label/Ghana_142312_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Ghana_144050_label/Ghana_144050_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Ghana_146222_label/Ghana_146222_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Ghana_147015_label/Ghana_147015_label.json

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Sen1Floods11/v1.1/catalog/sen1floods11_hand_labeled_label/Ghana_154838_label/Ghana_154838_label.json

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