diff options
| author | ncassereau-idris <84033440+ncassereau-idris@users.noreply.github.com> | 2021-09-30 08:36:24 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2021-09-30 08:36:24 +0200 |
| commit | 14c30d4cfac060ff0bf8c64d4c88c77df32aad86 (patch) | |
| tree | 082e4b920a3cd34a29ebb3c6e6500af9f0a7fdcc /ot/gpu | |
| parent | 1c7e7ce2da8bb362c184fb6eae71fe7e36356494 (diff) | |
[MRG] GPU bugs solve (#288)
* gpus tests now passing
* pep8 compliance
* GPU tests succeeding even if b has rank higher than 1
Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>
Diffstat (limited to 'ot/gpu')
| -rw-r--r-- | ot/gpu/bregman.py | 10 | ||||
| -rw-r--r-- | ot/gpu/da.py | 2 |
2 files changed, 7 insertions, 5 deletions
diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 82f34f3..76af00e 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -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 @@ -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) diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 4a98038..7adb830 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -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) |