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import logging
import zipfile
from collections import defaultdict
from collections.abc import Iterable
from pathlib import Path
from pprint import pprint
import matplotlib.pyplot as plt
import numpy as np
import torch
from omegaconf import OmegaConf
from tqdm import tqdm
from ..datasets import get_dataset
from ..models.cache_loader import CacheLoader
from ..settings import DATA_PATH, EVAL_PATH
from ..utils.export_predictions import export_predictions
from ..visualization.viz2d import plot_cumulative
from .eval_pipeline import EvalPipeline
from .io import get_eval_parser, load_model, parse_eval_args
from .utils import eval_matches_epipolar, eval_poses, eval_relative_pose_robust
logger = logging.getLogger(__name__)
class MegaDepth1500Pipeline(EvalPipeline):
default_conf = {
"data": {
"name": "image_pairs",
"pairs": "megadepth1500/pairs_calibrated.txt",
"root": "megadepth1500/images/",
"extra_data": "relative_pose",
"preprocessing": {
"side": "long",
},
},
"model": {
"ground_truth": {
"name": None, # remove gt matches
}
},
"eval": {
"estimator": "poselib",
"ransac_th": 1.0, # -1 runs a bunch of thresholds and selects the best
},
}
export_keys = [
"keypoints0",
"keypoints1",
"keypoint_scores0",
"keypoint_scores1",
"matches0",
"matches1",
"matching_scores0",
"matching_scores1",
]
optional_export_keys = []
def _init(self, conf):
if not (DATA_PATH / "megadepth1500").exists():
logger.info("Downloading the MegaDepth-1500 dataset.")
url = "https://cvg-data.inf.ethz.ch/megadepth/megadepth1500.zip"
zip_path = DATA_PATH / url.rsplit("/", 1)[-1]
zip_path.parent.mkdir(exist_ok=True, parents=True)
torch.hub.download_url_to_file(url, zip_path)
with zipfile.ZipFile(zip_path) as fid:
fid.extractall(DATA_PATH)
zip_path.unlink()
@classmethod
def get_dataloader(self, data_conf=None):
"""Returns a data loader with samples for each eval datapoint"""
data_conf = data_conf if data_conf else self.default_conf["data"]
dataset = get_dataset(data_conf["name"])(data_conf)
return dataset.get_data_loader("test")
def get_predictions(self, experiment_dir, model=None, overwrite=False):
"""Export a prediction file for each eval datapoint"""
pred_file = experiment_dir / "predictions.h5"
if not pred_file.exists() or overwrite:
if model is None:
model = load_model(self.conf.model, self.conf.checkpoint)
export_predictions(
self.get_dataloader(self.conf.data),
model,
pred_file,
keys=self.export_keys,
optional_keys=self.optional_export_keys,
)
return pred_file
def run_eval(self, loader, pred_file):
"""Run the eval on cached predictions"""
conf = self.conf.eval
results = defaultdict(list)
test_thresholds = (
([conf.ransac_th] if conf.ransac_th > 0 else [0.5, 1.0, 1.5, 2.0, 2.5, 3.0])
if not isinstance(conf.ransac_th, Iterable)
else conf.ransac_th
)
pose_results = defaultdict(lambda: defaultdict(list))
cache_loader = CacheLoader({"path": str(pred_file), "collate": None}).eval()
for i, data in enumerate(tqdm(loader)):
pred = cache_loader(data)
# add custom evaluations here
results_i = eval_matches_epipolar(data, pred)
for th in test_thresholds:
pose_results_i = eval_relative_pose_robust(
data,
pred,
{"estimator": conf.estimator, "ransac_th": th},
)
[pose_results[th][k].append(v) for k, v in pose_results_i.items()]
# we also store the names for later reference
results_i["names"] = data["name"][0]
if "scene" in data.keys():
results_i["scenes"] = data["scene"][0]
for k, v in results_i.items():
results[k].append(v)
# summarize results as a dict[str, float]
# you can also add your custom evaluations here
summaries = {}
for k, v in results.items():
arr = np.array(v)
if not np.issubdtype(np.array(v).dtype, np.number):
continue
summaries[f"m{k}"] = round(np.mean(arr), 3)
best_pose_results, best_th = eval_poses(
pose_results, auc_ths=[5, 10, 20], key="rel_pose_error"
)
results = {**results, **pose_results[best_th]}
summaries = {
**summaries,
**best_pose_results,
}
figures = {
"pose_recall": plot_cumulative(
{self.conf.eval.estimator: results["rel_pose_error"]},
[0, 30],
unit="°",
title="Pose ",
)
}
return summaries, figures, results
if __name__ == "__main__":
from .. import logger # overwrite the logger
dataset_name = Path(__file__).stem
parser = get_eval_parser()
args = parser.parse_intermixed_args()
default_conf = OmegaConf.create(MegaDepth1500Pipeline.default_conf)
# mingle paths
output_dir = Path(EVAL_PATH, dataset_name)
output_dir.mkdir(exist_ok=True, parents=True)
name, conf = parse_eval_args(
dataset_name,
args,
"configs/",
default_conf,
)
experiment_dir = output_dir / name
experiment_dir.mkdir(exist_ok=True)
pipeline = MegaDepth1500Pipeline(conf)
s, f, r = pipeline.run(
experiment_dir,
overwrite=args.overwrite,
overwrite_eval=args.overwrite_eval,
)
pprint(s)
if args.plot:
for name, fig in f.items():
fig.canvas.manager.set_window_title(name)
plt.show()
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