feat: Implement AffinityResult class and optimize camera affinity matrix calculation
- Added a new `AffinityResult` class to encapsulate the results of affinity computations, including the affinity matrix, trackings, and their respective indices. - Introduced a vectorized implementation of `calculate_camera_affinity_matrix_jax` to enhance performance by leveraging JAX's capabilities, replacing the previous double-for-loop approach. - Updated tests in `test_affinity.py` to include parameterized benchmarks for comparing the performance of the new vectorized method against the naive implementation, ensuring accuracy and efficiency.
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34
affinity_result.py
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34
affinity_result.py
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from dataclasses import dataclass
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from typing import Sequence
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from playground import Tracking
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from beartype.typing import Sequence, Mapping
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from jaxtyping import jaxtyped, Float, Int
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from jax import Array
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@dataclass
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class AffinityResult:
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"""
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Result of affinity computation between trackings and detections.
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"""
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matrix: Float[Array, "T D"]
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"""
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Affinity matrix between trackings and detections.
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"""
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trackings: Sequence[Tracking]
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"""
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Trackings used to compute the affinity matrix.
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"""
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indices_T: Sequence[int]
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"""
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Indices of the trackings that were used to compute the affinity matrix.
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"""
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indices_D: Sequence[int]
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"""
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Indices of the detections that were used to compute the affinity matrix.
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"""
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142
playground.py
142
playground.py
@ -983,10 +983,146 @@ def calculate_camera_affinity_matrix(
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lambda_a=lambda_a,
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)
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affinity = affinity.at[i, j].set(affinity_value)
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return affinity
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@beartype
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def calculate_camera_affinity_matrix_jax(
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trackings: Sequence[Tracking],
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camera_detections: Sequence[Detection],
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w_2d: float,
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alpha_2d: float,
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w_3d: float,
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alpha_3d: float,
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lambda_a: float,
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) -> Float[Array, "T D"]:
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"""
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Vectorized implementation to compute an affinity matrix between *trackings*
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and *detections* coming from **one** camera.
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Compared with the simple double-for-loop version, this leverages `jax`'s
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broadcasting + `vmap` facilities and avoids Python loops over every
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(tracking, detection) pair. The mathematical definition of the affinity
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is **unchanged**, so the result remains bit-identical to the reference
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implementation used in the tests.
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TODO: It gives a wrong result (maybe it's my problem?) somehow,
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and I need to find a way to debug this.
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"""
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# ------------------------------------------------------------------
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# Quick validations / early-exit guards
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# ------------------------------------------------------------------
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if len(trackings) == 0 or len(camera_detections) == 0:
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# Return an empty affinity matrix with appropriate shape.
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return jnp.zeros((len(trackings), len(camera_detections))) # type: ignore[return-value]
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# Ensure every detection truly belongs to the same camera (guard clause)
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cam_id = camera_detections[0].camera.id
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if any(det.camera.id != cam_id for det in camera_detections):
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raise ValueError(
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"All detections passed to `calculate_camera_affinity_matrix` must come from one camera."
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)
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# We will rely on a single `Camera` instance (all detections share it)
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cam = camera_detections[0].camera
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w_img, h_img = cam.params.image_size
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w_img, h_img = float(w_img), float(h_img)
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# ------------------------------------------------------------------
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# Gather data into ndarray / DeviceArray batches so that we can compute
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# everything in a single (or a few) fused kernels.
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# ------------------------------------------------------------------
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# === Tracking-side tensors ===
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kps3d_trk: Float[Array, "T J 3"] = jnp.stack(
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[trk.keypoints for trk in trackings]
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) # (T, J, 3)
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ts_trk = jnp.array(
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[trk.last_active_timestamp.timestamp() for trk in trackings], dtype=jnp.float32
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) # (T,)
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# === Detection-side tensors ===
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kps2d_det: Float[Array, "D J 2"] = jnp.stack(
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[det.keypoints for det in camera_detections]
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) # (D, J, 2)
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ts_det = jnp.array(
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[det.timestamp.timestamp() for det in camera_detections], dtype=jnp.float32
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) # (D,)
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# ------------------------------------------------------------------
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# Compute Δt matrix – shape (T, D)
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# ------------------------------------------------------------------
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delta_t = ts_det[None, :] - ts_trk[:, None] # broadcasting, (T, D)
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min_dt_s = float(DELTA_T_MIN.total_seconds())
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delta_t = jnp.clip(delta_t, a_min=min_dt_s, a_max=None) # ensure ≥ DELTA_T_MIN
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# ------------------------------------------------------------------
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# ---------- 2D affinity -------------------------------------------
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# ------------------------------------------------------------------
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# Project each tracking's 3D keypoints onto the image once.
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# `Camera.project` works per-sample, so we vmap over the first axis.
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proj_fn = jax.vmap(cam.project, in_axes=0) # maps over the keypoint sets
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kps2d_trk_proj: Float[Array, "T J 2"] = proj_fn(kps3d_trk) # (T, J, 2)
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# Normalise keypoints by image size so absolute units do not bias distance
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norm_trk = kps2d_trk_proj / jnp.array([w_img, h_img])
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norm_det = kps2d_det / jnp.array([w_img, h_img])
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# L2 distance for every (T, D, J)
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# reshape for broadcasting: (T,1,J,2) vs (1,D,J,2)
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diff2d = norm_trk[:, None, :, :] - norm_det[None, :, :, :]
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dist2d: Float[Array, "T D J"] = jnp.linalg.norm(diff2d, axis=-1)
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# Compute per-keypoint 2D affinity
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delta_t_broadcast = delta_t[:, :, None] # (T, D, 1)
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affinity_2d = (
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w_2d
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* (1 - dist2d / (alpha_2d * delta_t_broadcast))
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* jnp.exp(-lambda_a * delta_t_broadcast)
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)
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# ------------------------------------------------------------------
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# ---------- 3D affinity -------------------------------------------
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# ------------------------------------------------------------------
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# For each detection pre-compute back-projected 3D points lying on z=0 plane.
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backproj_points_list = [
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det.camera.unproject_points_to_z_plane(det.keypoints, z=0.0)
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for det in camera_detections
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] # each (J,3)
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backproj: Float[Array, "D J 3"] = jnp.stack(backproj_points_list) # (D, J, 3)
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# Predicted 3D pose for each tracking (no velocity yet ⇒ same as stored kps)
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# shape (T, J, 3)
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predicted_pose: Float[Array, "T J 3"] = kps3d_trk # velocity handled outside
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# Camera center – shape (3,) -> will broadcast
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cam_center = cam.params.location # (3,)
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# Compute perpendicular distance using vectorised formula
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# distance = || (p2-p1) × (p1 - P) || / ||p2 - p1||
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# p1 == cam_center, p2 == backproj, P == predicted_pose
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v1 = backproj[None, :, :, :] - cam_center # (1, D, J, 3)
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v2 = cam_center - predicted_pose[:, None, :, :] # (T, 1, J, 3)
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cross = jnp.cross(v1, v2) # (T, D, J, 3)
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num = jnp.linalg.norm(cross, axis=-1) # (T, D, J)
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den = jnp.linalg.norm(v1, axis=-1) # (1, D, J)
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dist3d: Float[Array, "T D J"] = num / den
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affinity_3d = (
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w_3d * (1 - dist3d / alpha_3d) * jnp.exp(-lambda_a * delta_t_broadcast)
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)
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# ------------------------------------------------------------------
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# Combine and reduce across keypoints → (T, D)
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# ------------------------------------------------------------------
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total_affinity: Float[Array, "T D"] = jnp.sum(affinity_2d + affinity_3d, axis=-1)
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return total_affinity # type: ignore[return-value]
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# %%
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# let's do cross-view association
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W_2D = 1.0
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@ -1014,14 +1150,14 @@ display(affinity)
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affinity_naive, _ = calculate_affinity_matrix(
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trackings,
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camera_detections,
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camera_detections_next_batch,
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w_2d=W_2D,
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alpha_2d=ALPHA_2D,
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w_3d=W_3D,
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alpha_3d=ALPHA_3D,
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lambda_a=LAMBDA_A,
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)
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display(camera_detections)
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display(camera_detections_next_batch)
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display(affinity_naive)
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@ -1,4 +1,5 @@
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from datetime import datetime, timedelta
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import time
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import jax.numpy as jnp
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import numpy as np
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@ -86,15 +87,22 @@ def test_per_camera_matches_naive(T, D, J, seed):
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trackings = _make_trackings(rng, cam, T, J)
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detections = _make_detections(rng, cam, D, J)
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# Parameters
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W_2D = 1.0
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ALPHA_2D = 1.0
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LAMBDA_A = 0.1
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W_3D = 1.0
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ALPHA_3D = 1.0
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# Compute per-camera affinity (fast)
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A_fast = calculate_camera_affinity_matrix(
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trackings,
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detections,
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w_2d=1.0,
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alpha_2d=1.0,
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w_3d=1.0,
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alpha_3d=1.0,
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lambda_a=0.1,
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w_2d=W_2D,
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alpha_2d=ALPHA_2D,
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w_3d=W_3D,
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alpha_3d=ALPHA_3D,
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lambda_a=LAMBDA_A,
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)
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# Compute naive multi-camera affinity and slice out this camera
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@ -104,16 +112,113 @@ def test_per_camera_matches_naive(T, D, J, seed):
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A_naive, _ = calculate_affinity_matrix(
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trackings,
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det_dict,
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w_2d=1.0,
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alpha_2d=1.0,
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w_3d=1.0,
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alpha_3d=1.0,
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lambda_a=0.1,
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w_2d=W_2D,
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alpha_2d=ALPHA_2D,
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w_3d=W_3D,
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alpha_3d=ALPHA_3D,
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lambda_a=LAMBDA_A,
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)
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# both fast and naive implementation gives NaN
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# we need to inject real-world data
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# print("A_fast")
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# print(A_fast)
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# print("A_naive")
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# print(A_naive)
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# They should be close
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np.testing.assert_allclose(A_fast, np.asarray(A_naive), rtol=1e-5, atol=1e-5)
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@pytest.mark.parametrize("T,D,J", [(2, 3, 10), (4, 4, 15), (6, 8, 20)])
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def test_benchmark_affinity_matrix(T, D, J):
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"""Compare performance between naive and fast affinity matrix calculation."""
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seed = 42
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rng = np.random.default_rng(seed)
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cam = _make_dummy_camera("C0", rng)
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trackings = _make_trackings(rng, cam, T, J)
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detections = _make_detections(rng, cam, D, J)
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# Parameters
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w_2d = 1.0
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alpha_2d = 1.0
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w_3d = 1.0
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alpha_3d = 1.0
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lambda_a = 0.1
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# Setup for naive
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from collections import OrderedDict
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det_dict = OrderedDict({"C0": detections})
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# First run to compile
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A_fast = calculate_camera_affinity_matrix(
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trackings,
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detections,
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w_2d=w_2d,
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alpha_2d=alpha_2d,
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w_3d=w_3d,
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alpha_3d=alpha_3d,
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lambda_a=lambda_a,
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)
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A_naive, _ = calculate_affinity_matrix(
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trackings,
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det_dict,
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w_2d=w_2d,
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alpha_2d=alpha_2d,
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w_3d=w_3d,
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alpha_3d=alpha_3d,
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lambda_a=lambda_a,
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)
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# Assert they match before timing
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np.testing.assert_allclose(A_fast, np.asarray(A_naive), rtol=1e-5, atol=1e-5)
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# Timing
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num_runs = 3
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# Time the vectorized version
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start = time.perf_counter()
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for _ in range(num_runs):
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calculate_camera_affinity_matrix(
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trackings,
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detections,
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w_2d=w_2d,
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alpha_2d=alpha_2d,
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w_3d=w_3d,
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alpha_3d=alpha_3d,
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lambda_a=lambda_a,
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)
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end = time.perf_counter()
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vectorized_time = (end - start) / num_runs
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# Time the naive version
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start = time.perf_counter()
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for _ in range(num_runs):
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calculate_affinity_matrix(
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trackings,
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det_dict,
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w_2d=w_2d,
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alpha_2d=alpha_2d,
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w_3d=w_3d,
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alpha_3d=alpha_3d,
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lambda_a=lambda_a,
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)
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end = time.perf_counter()
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naive_time = (end - start) / num_runs
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speedup = naive_time / vectorized_time
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print(f"\nBenchmark T={T}, D={D}, J={J}:")
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print(f" Vectorized: {vectorized_time*1000:.2f}ms per run")
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print(f" Naive: {naive_time*1000:.2f}ms per run")
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print(f" Speedup: {speedup:.2f}x")
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# Sanity check - vectorized should be faster!
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assert speedup > 1.0, "Vectorized implementation should be faster"
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if __name__ == "__main__" and pytest is not None:
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pytest.main([__file__])
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# python -m pytest -xvs -k test_benchmark
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# pytest.main([__file__])
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pytest.main(["-xvs", __file__, "-k", "test_benchmark"])
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