Apply black and isort

demo.ipynb

@@ -14,7 +14,7 @@
     "# The highest accuracy is achieved with num_rotations=360\n",
     "# but num_rotations=64~128 is often sufficient.\n",
     "# To reduce the memory usage, we can reduce the tile size in the next cell.\n",
-    "demo = Demo(num_rotations=256, device='cpu')"
+    "demo = Demo(num_rotations=256, device=\"cpu\")"
    ]
   },
   {
@@ -135,19 +135,22 @@
     "\n",
     "# Show the query area in an interactive map\n",
     "from maploc.osm.viz import GeoPlotter\n",
+    "\n",
     "plot = GeoPlotter(zoom=16)\n",
     "plot.points(prior_latlon[:2], \"red\", name=\"location prior\", size=10)\n",
     "plot.bbox(proj.unproject(bbox), \"blue\", name=\"map tile\")\n",
     "plot.fig.show(\"notebook\")\n",
     "\n",
     "# Query OpenStreetMap for this area\n",
     "from maploc.osm.tiling import TileManager\n",
+    "\n",
     "tiler = TileManager.from_bbox(proj, bbox + 10, demo.config.data.pixel_per_meter)\n",
     "canvas = tiler.query(bbox)\n",
     "\n",
     "# Show the inputs to the model: image and raster map\n",
     "from maploc.osm.viz import Colormap, plot_nodes\n",
     "from maploc.utils.viz_2d import plot_images\n",
+    "\n",
     "map_viz = Colormap.apply(canvas.raster)\n",
     "plot_images([image, map_viz], titles=[\"input image\", \"OpenStreetMap raster\"])\n",
     "plot_nodes(1, canvas.raster[2], fontsize=6, size=10)"
@@ -1186,15 +1189,16 @@
     "\n",
     "# Run the inference\n",
     "uv, yaw, prob, neural_map, image_rectified = demo.localize(\n",
-    "    image, camera, canvas, roll_pitch=gravity)\n",
+    "    image, camera, canvas, roll_pitch=gravity\n",
+    ")\n",
     "\n",
     "# Visualize the predictions\n",
     "overlay = likelihood_overlay(prob.numpy().max(-1), map_viz.mean(-1, keepdims=True))\n",
     "(neural_map_rgb,) = features_to_RGB(neural_map.numpy())\n",
     "plot_images([overlay, neural_map_rgb], titles=[\"prediction\", \"neural map\"])\n",
     "ax = plt.gcf().axes[0]\n",
     "ax.scatter(*canvas.to_uv(bbox.center), s=5, c=\"red\")\n",
-    "plot_dense_rotations(ax, prob, w=0.005, s=1/25)\n",
+    "plot_dense_rotations(ax, prob, w=0.005, s=1 / 25)\n",
     "add_circle_inset(ax, uv)\n",
     "plt.show(\"notebook\")\n",
     "\n",

maploc/__init__.py

@@ -1,11 +1,10 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 
-from pathlib import Path
 import logging
+from pathlib import Path
 
 import pytorch_lightning  # noqa: F401
 
-
 formatter = logging.Formatter(
     fmt="[%(asctime)s %(name)s %(levelname)s] %(message)s",
     datefmt="%Y-%m-%d %H:%M:%S",

maploc/data/image.py

@@ -1,11 +1,11 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 
-from typing import Callable, Optional, Union, Sequence
+import collections
+from typing import Callable, Optional, Sequence, Union
 
 import numpy as np
 import torch
 import torchvision.transforms.functional as tvf
-import collections
 from scipy.spatial.transform import Rotation
 
 from ..utils.geometry import from_homogeneous, to_homogeneous

maploc/data/kitti/dataset.py

@@ -13,12 +13,12 @@
 from omegaconf import OmegaConf
 from scipy.spatial.transform import Rotation
 
-from ... import logger, DATASETS_PATH
+from ... import DATASETS_PATH, logger
 from ...osm.tiling import TileManager
 from ..dataset import MapLocDataset
 from ..sequential import chunk_sequence
 from ..torch import collate, worker_init_fn
-from .utils import parse_split_file, parse_gps_file, get_camera_calibration
+from .utils import get_camera_calibration, parse_gps_file, parse_split_file
 
 
 class KittiDataModule(pl.LightningDataModule):

maploc/data/kitti/prepare.py

@@ -1,9 +1,9 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 
 import argparse
-from pathlib import Path
 import shutil
 import zipfile
+from pathlib import Path
 
 import numpy as np
 from tqdm.auto import tqdm
@@ -12,9 +12,9 @@
 from ...osm.tiling import TileManager
 from ...osm.viz import GeoPlotter
 from ...utils.geo import BoundaryBox, Projection
-from ...utils.io import download_file, DATA_URL
-from .utils import parse_gps_file
+from ...utils.io import DATA_URL, download_file
 from .dataset import KittiDataModule
+from .utils import parse_gps_file
 
 split_files = ["test1_files.txt", "test2_files.txt", "train_files.txt"]
 

maploc/data/mapillary/dataset.py

@@ -1,10 +1,10 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 
 import json
-from collections import defaultdict
 import os
 import shutil
 import tarfile
+from collections import defaultdict
 from pathlib import Path
 from typing import Any, Dict, Optional
 
@@ -14,7 +14,7 @@
 import torch.utils.data as torchdata
 from omegaconf import DictConfig, OmegaConf
 
-from ... import logger, DATASETS_PATH
+from ... import DATASETS_PATH, logger
 from ...osm.tiling import TileManager
 from ..dataset import MapLocDataset
 from ..sequential import chunk_sequence

maploc/data/mapillary/download.py

@@ -1,25 +1,24 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 
+import asyncio
 import json
 from pathlib import Path
 
-import numpy as np
 import httpx
-import asyncio
-from aiolimiter import AsyncLimiter
+import numpy as np
 import tqdm
-
+from aiolimiter import AsyncLimiter
 from opensfm.pygeometry import Camera, Pose
 from opensfm.pymap import Shot
 
 from ... import logger
 from ...utils.geo import Projection
 
-
 semaphore = asyncio.Semaphore(100)  # number of parallel threads.
 image_filename = "{image_id}.jpg"
 info_filename = "{image_id}.json"
 
+
 def retry(times, exceptions):
     def decorator(func):
         async def wrapper(*args, **kwargs):
@@ -30,9 +29,12 @@ async def wrapper(*args, **kwargs):
                 except exceptions:
                     attempt += 1
             return await func(*args, **kwargs)
+
         return wrapper
+
     return decorator
 
+
 class MapillaryDownloader:
     image_fields = (
         "id",

maploc/data/mapillary/prepare.py

@@ -1,48 +1,47 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 
-import asyncio
 import argparse
-from collections import defaultdict
+import asyncio
 import json
 import shutil
+from collections import defaultdict
 from pathlib import Path
 from typing import List
 
-import numpy as np
 import cv2
-from tqdm import tqdm
-from tqdm.contrib.concurrent import thread_map
+import numpy as np
 from omegaconf import DictConfig, OmegaConf
 from opensfm.pygeometry import Camera
 from opensfm.pymap import Shot
 from opensfm.undistort import (
     perspective_camera_from_fisheye,
     perspective_camera_from_perspective,
 )
+from tqdm import tqdm
+from tqdm.contrib.concurrent import thread_map
 
 from ... import logger
 from ...osm.tiling import TileManager
 from ...osm.viz import GeoPlotter
 from ...utils.geo import BoundaryBox, Projection
-from ...utils.io import write_json, download_file, DATA_URL
+from ...utils.io import DATA_URL, download_file, write_json
 from ..utils import decompose_rotmat
+from .dataset import MapillaryDataModule
+from .download import (
+    MapillaryDownloader,
+    fetch_image_infos,
+    fetch_images_pixels,
+    image_filename,
+    opensfm_shot_from_info,
+)
 from .utils import (
+    CameraUndistorter,
+    PanoramaUndistorter,
     keyframe_selection,
     perspective_camera_from_pano,
     scale_camera,
-    CameraUndistorter,
-    PanoramaUndistorter,
     undistort_shot,
 )
-from .download import (
-    MapillaryDownloader,
-    opensfm_shot_from_info,
-    image_filename,
-    fetch_image_infos,
-    fetch_images_pixels,
-)
-from .dataset import MapillaryDataModule
-
 
 location_to_params = {
     "sanfrancisco_soma": {

maploc/data/mapillary/utils.py

@@ -6,7 +6,7 @@
 import cv2
 import numpy as np
 from opensfm import features
-from opensfm.pygeometry import Camera, compute_camera_mapping, Pose
+from opensfm.pygeometry import Camera, Pose, compute_camera_mapping
 from opensfm.pymap import Shot
 from scipy.spatial.transform import Rotation
 

maploc/data/torch.py

@@ -4,14 +4,14 @@
 import os
 
 import torch
+from lightning_fabric.utilities.apply_func import move_data_to_device
+from lightning_fabric.utilities.seed import pl_worker_init_function
+from lightning_utilities.core.apply_func import apply_to_collection
 from torch.utils.data import get_worker_info
 from torch.utils.data._utils.collate import (
     default_collate_err_msg_format,
     np_str_obj_array_pattern,
 )
-from lightning_fabric.utilities.seed import pl_worker_init_function
-from lightning_utilities.core.apply_func import apply_to_collection
-from lightning_fabric.utilities.apply_func import move_data_to_device
 
 
 def collate(batch):

maploc/demo.py

@@ -2,19 +2,19 @@
 
 from typing import Optional, Tuple
 
-import torch
 import numpy as np
+import torch
 
 from . import logger
-from .evaluation.run import resolve_checkpoint_path, pretrained_models
+from .data.image import pad_image, rectify_image, resize_image
+from .evaluation.run import pretrained_models, resolve_checkpoint_path
 from .models.orienternet import OrienterNet
-from .models.voting import fuse_gps, argmax_xyr
-from .data.image import resize_image, pad_image, rectify_image
+from .models.voting import argmax_xyr, fuse_gps
 from .osm.raster import Canvas
-from .utils.wrappers import Camera
-from .utils.io import read_image
-from .utils.geo import BoundaryBox, Projection
 from .utils.exif import EXIF
+from .utils.geo import BoundaryBox, Projection
+from .utils.io import read_image
+from .utils.wrappers import Camera
 
 try:
     from geopy.geocoders import Nominatim

maploc/evaluation/kitti.py

@@ -4,13 +4,12 @@
 from pathlib import Path
 from typing import Optional, Tuple
 
-from omegaconf import OmegaConf, DictConfig
+from omegaconf import DictConfig, OmegaConf
 
 from .. import logger
 from ..data import KittiDataModule
 from .run import evaluate
 
-
 default_cfg_single = OmegaConf.create({})
 # For the sequential evaluation, we need to center the map around the GT location,
 # since random offsets would accumulate and leave only the GT location with a valid mask.

maploc/evaluation/mapillary.py

@@ -4,14 +4,13 @@
 from pathlib import Path
 from typing import Optional, Tuple
 
-from omegaconf import OmegaConf, DictConfig
+from omegaconf import DictConfig, OmegaConf
 
 from .. import logger
 from ..conf import data as conf_data_dir
 from ..data import MapillaryDataModule
 from .run import evaluate
 
-
 split_overrides = {
     "val": {
         "scenes": [

maploc/evaluation/run.py

@@ -2,26 +2,25 @@
 
 import functools
 from itertools import islice
-from typing import Callable, Dict, Optional, Tuple
 from pathlib import Path
+from typing import Callable, Dict, Optional, Tuple
 
 import numpy as np
 import torch
 from omegaconf import DictConfig, OmegaConf
-from torchmetrics import MetricCollection
 from pytorch_lightning import seed_everything
+from torchmetrics import MetricCollection
 from tqdm import tqdm
 
-from .. import logger, EXPERIMENTS_PATH
+from .. import EXPERIMENTS_PATH, logger
 from ..data.torch import collate, unbatch_to_device
-from ..models.voting import argmax_xyr, fuse_gps
 from ..models.metrics import AngleError, LateralLongitudinalError, Location2DError
 from ..models.sequential import GPSAligner, RigidAligner
+from ..models.voting import argmax_xyr, fuse_gps
 from ..module import GenericModule
-from ..utils.io import download_file, DATA_URL
-from .viz import plot_example_single, plot_example_sequential
+from ..utils.io import DATA_URL, download_file
 from .utils import write_dump
-
+from .viz import plot_example_sequential, plot_example_single
 
 pretrained_models = dict(
     OrienterNet_MGL=("orienternet_mgl.ckpt", dict(num_rotations=256)),

maploc/evaluation/viz.py

@@ -1,18 +1,18 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 
+import matplotlib.pyplot as plt
 import numpy as np
 import torch
-import matplotlib.pyplot as plt
 
+from ..osm.viz import Colormap, plot_nodes
 from ..utils.io import write_torch_image
-from ..utils.viz_2d import plot_images, features_to_RGB, save_plot
+from ..utils.viz_2d import features_to_RGB, plot_images, save_plot
 from ..utils.viz_localization import (
+    add_circle_inset,
     likelihood_overlay,
-    plot_pose,
     plot_dense_rotations,
-    add_circle_inset,
+    plot_pose,
 )
-from ..osm.viz import Colormap, plot_nodes
 
 
 def plot_example_single(