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Use futures in triangulation #758

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Use futures in triangulation #758

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5 changes: 4 additions & 1 deletion gtsfm/data_association/data_assoc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
import dask
import numpy as np
from dask.delayed import Delayed
from distributed.worker import get_client
from gtsam import SfmTrack

import gtsfm.common.types as gtsfm_types
Expand Down Expand Up @@ -239,7 +240,9 @@ def triangulate_batch(
return batch_results

# Initialize 3D landmark for each track.
client = get_client()
point3d_initializer = Point3dInitializer(cameras, self.triangulation_options)
future_point3d_initializer = client.scatter(point3d_initializer, broadcast=True)

# Loop through tracks and and generate delayed triangulation tasks.
batch_size = int(np.ceil(len(tracks_2d) / MAX_DELAYED_TRIANGULATION_CALLS))
Expand All @@ -250,7 +253,7 @@ def triangulate_batch(
else:
for j in range(0, len(tracks_2d), batch_size):
triangulation_results.append(
dask.delayed(triangulate_batch)(point3d_initializer, tracks_2d[j : j + batch_size])
dask.delayed(triangulate_batch)(future_point3d_initializer, tracks_2d[j : j + batch_size])
)

# Perform triangulation in parallel.
Expand Down
4 changes: 2 additions & 2 deletions gtsfm/multi_view_optimizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@
from gtsfm.data_association.data_assoc import DataAssociation
from gtsfm.evaluation.metrics import GtsfmMetricsGroup
from gtsfm.view_graph_estimator.view_graph_estimator_base import ViewGraphEstimatorBase
from gtsfm.data_association.cpp_dsf_tracks_estimator import CppDsfTracksEstimator
from gtsfm.data_association.dsf_tracks_estimator import DsfTracksEstimator
from gtsfm.common.two_view_estimation_report import TwoViewEstimationReport


Expand Down Expand Up @@ -194,5 +194,5 @@ def init_cameras(
def get_2d_tracks(
corr_idxs_dict: Dict[Tuple[int, int], np.ndarray], keypoints_list: List[Keypoints]
) -> List[SfmTrack2d]:
tracks_estimator = CppDsfTracksEstimator()
tracks_estimator = DsfTracksEstimator()
return tracks_estimator.run(corr_idxs_dict, keypoints_list)
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