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[MRG] GPU bugs solve #288

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Merged
rflamary merged 5 commits into from
Sep 30, 2021
Merged

[MRG] GPU bugs solve #288

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96f0404
gpus tests now passing
ncassereau Sep 28, 2021
a18c099
Merge branch 'master' into GPU_sinkhorn_lpl1_typerror
ncassereau Sep 29, 2021
7a52f56
pep8 compliance
ncassereau Sep 29, 2021
649b5eb
GPU tests succeeding even if b has rank higher than 1
ncassereau Sep 29, 2021
c4ae944
Merge branch 'master' into GPU_sinkhorn_lpl1_typerror
rflamary Sep 29, 2021
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2 changes: 1 addition & 1 deletion ot/bregman.py
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Original file line number Diff line number Diff line change
Expand Up @@ -312,7 +312,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000,
numItermax : int, optional
Max number of iterations
stopThr : float, optional
Stop threshol on error (>0)
Stop threshold on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
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10 changes: 6 additions & 4 deletions ot/gpu/bregman.py
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Expand Up @@ -54,7 +54,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9,
numItermax : int, optional
Max number of iterations
stopThr : float, optional
Stop threshol on error (>0)
Stop threshold on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
Expand Down Expand Up @@ -148,13 +148,15 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9,
# we can speed up the process by checking for the error only all
# the 10th iterations
if nbb:
err = np.sum((u - uprev)**2) / np.sum((u)**2) + \
np.sum((v - vprev)**2) / np.sum((v)**2)
err = np.sqrt(
np.sum((u - uprev)**2) / np.sum((u)**2)
+ np.sum((v - vprev)**2) / np.sum((v)**2)
)
else:
# compute right marginal tmp2= (diag(u)Kdiag(v))^T1
tmp2 = np.sum(u[:, None] * K * v[None, :], 0)
#tmp2=np.einsum('i,ij,j->j', u, K, v)
err = np.linalg.norm(tmp2 - b)**2 # violation of marginal
err = np.linalg.norm(tmp2 - b) # violation of marginal
if log:
log['err'].append(err)

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2 changes: 1 addition & 1 deletion ot/gpu/da.py
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Expand Up @@ -120,7 +120,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
labels_a2 = cp.asnumpy(labels_a)
classes = npp.unique(labels_a2)
for c in classes:
idxc, = utils.to_gpu(npp.where(labels_a2 == c))
idxc = utils.to_gpu(*npp.where(labels_a2 == c))
indices_labels.append(idxc)

W = np.zeros(M.shape)
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