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ultralytics 8.0.52 reduced TAL CUDA usage and AMP check fix (#1333)

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This commit is contained in:
Glenn Jocher
2023-03-10 03:27:06 +01:00
committed by GitHub
parent 790f9c067c
commit 177a68b39f
21 changed files with 132 additions and 147 deletions
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24
ultralytics/tracker/utils/matching.py
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@ -36,18 +36,26 @@ def _indices_to_matches(cost_matrix, indices, thresh):
return matches, unmatched_a, unmatched_b
def linear_assignment(cost_matrix, thresh):
def linear_assignment(cost_matrix, thresh, use_lap=True):
# Linear assignment implementations with scipy and lap.lapjv
if cost_matrix.size == 0:
return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
matches, unmatched_a, unmatched_b = [], [], []
# TODO: investigate scipy.optimize.linear_sum_assignment() for lap.lapjv()
cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)
if use_lap:
_, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)
matches = [[ix, mx] for ix, mx in enumerate(x) if mx >= 0]
unmatched_a = np.where(x < 0)[0]
unmatched_b = np.where(y < 0)[0]
else:
# Scipy linear sum assignment is NOT working correctly, DO NOT USE
y, x = scipy.optimize.linear_sum_assignment(cost_matrix) # row y, col x
matches = np.asarray([[i, x] for i, x in enumerate(x) if cost_matrix[i, x] <= thresh])
unmatched = np.ones(cost_matrix.shape)
for i, xi in matches:
unmatched[i, xi] = 0.0
unmatched_a = np.where(unmatched.all(1))[0]
unmatched_b = np.where(unmatched.all(0))[0]
matches.extend([ix, mx] for ix, mx in enumerate(x) if mx >= 0)
unmatched_a = np.where(x < 0)[0]
unmatched_b = np.where(y < 0)[0]
matches = np.asarray(matches)
return matches, unmatched_a, unmatched_b