0. Added notebook for visualization

1. Converted some of the features in _load_features from strs to ints

docs/NASA Cyclone Dataset.ipynb

@@ -0,0 +1,141 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "indirect-delivery",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import sys\n",
+    "sys.path.append(\"..\")\n",
+    "import os\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from torchgeo.datasets import TropicalCycloneWindEstimation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "advanced-gauge",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT_DIR = os.path.expanduser(\"~/mount/data/\")\n",
+    "API_KEY = \"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "tired-malta",
+   "metadata": {},
+   "source": [
+    "## Benchmarking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bigger-archive",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "# takes ~3 minutes and 20 seconds to download train split to SSD\n",
+    "# takes ~9 seconds to verify checksum\n",
+    "train_dataset = TropicalCycloneWindEstimation(\n",
+    "    ROOT_DIR,\n",
+    "    split=\"train\",\n",
+    "    download=True,\n",
+    "    api_key=API_KEY,\n",
+    "    checksum=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "reverse-bridges",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "test_dataset = TropicalCycloneWindEstimation(\n",
+    "    ROOT_DIR,\n",
+    "    split=\"test\",\n",
+    "    download=True,\n",
+    "    api_key=API_KEY,\n",
+    "    checksum=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "invisible-aurora",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(train_dataset), len(test_dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "banner-consultancy",
+   "metadata": {},
+   "source": [
+    "## Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "swiss-gates",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "for i in np.random.choice(len(train_dataset), size=10):\n",
+    "    \n",
+    "    sample = train_dataset[i]\n",
+    "    img = sample[\"image\"]\n",
+    "    wind_speed = sample[\"wind_speed\"]\n",
+    "    \n",
+    "    plt.figure(figsize=(5,5))\n",
+    "    plt.imshow(img, cmap=\"Greys_r\", vmin=0, vmax=255)\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.title(\"Windspeed: %d\" % (wind_speed))\n",
+    "    plt.show()\n",
+    "    plt.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "landcover",
+   "language": "python",
+   "name": "conda-env-landcover-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

torchgeo/datasets/cyclone.py

@@ -140,14 +140,13 @@ class TropicalCycloneWindEstimation(VisionDataset):
         Returns:
             the image
         """
-        print(directory)
         filename = os.path.join(directory.format("source"), "image.jpg")
         with Image.open(filename) as img:
             array = np.array(img)
             tensor: Tensor = torch.from_numpy(array)  # type: ignore[attr-defined]
             return tensor
 
-    def _load_features(self, directory: str) -> Dict[str, str]:
+    def _load_features(self, directory: str) -> Dict[str, Any]:
         """Load features for a single image.
 
         Parameters:
@@ -158,12 +157,16 @@ class TropicalCycloneWindEstimation(VisionDataset):
         """
         filename = os.path.join(directory.format("source"), "features.json")
         with open(filename) as f:
-            features: Dict[str, str] = json.load(f)
+            features: Dict[str, Any] = json.load(f)
 
         filename = os.path.join(directory.format("labels"), "labels.json")
         with open(filename) as f:
             features.update(json.load(f))
 
+        features["relative_time"] = int(features["relative_time"])
+        features["ocean"] = int(features["ocean"])
+        features["wind_speed"] = int(features["wind_speed"])
+
         return features
 
     def _check_integrity(self) -> bool: