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AutoAnchor improved initialization robustness #6854

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merged 3 commits into from
Mar 4, 2022
Merged

AutoAnchor improved initialization robustness #6854

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31a04e6
Update AutoAnchor
glenn-jocher Mar 4, 2022
1df411e
Update AutoAnchor
glenn-jocher Mar 4, 2022
c9d0cfe
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Mar 4, 2022
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18 changes: 10 additions & 8 deletions utils/autoanchor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -125,15 +125,17 @@ def print_results(k, verbose=True):
wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels
# wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1

# Kmeans calculation
LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...')
s = wh.std(0) # sigmas for whitening
k = kmeans(wh / s, n, iter=30)[0] * s # points
if len(k) != n: # kmeans may return fewer points than requested if wh is insufficient or too similar
LOGGER.warning(f'{PREFIX}WARNING: scipy.cluster.vq.kmeans returned only {len(k)} of {n} requested points')
# Kmeans init
try:
LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...')
assert n <= len(wh) # apply overdetermined constraint
s = wh.std(0) # sigmas for whitening
k = kmeans(wh / s, n, iter=30)[0] * s # points
assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar
except Exception:
LOGGER.warning(f'{PREFIX}WARNING: switching strategies from kmeans to random init')
k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init
wh = torch.tensor(wh, dtype=torch.float32) # filtered
wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered
wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0))
k = print_results(k, verbose=False)

# Plot
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