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| author | Rémi Flamary <remi.flamary@gmail.com> | 2018-10-03 08:38:11 +0200 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2018-10-03 08:38:11 +0200 |
| commit | feaecbe2965fa22cdf964c2e869fe56d6ce392e9 (patch) | |
| tree | e591f016a12b00c17a065b4e5881b0d5c24135fd /ot/gpu | |
| parent | 8f6c45534dcdd6e8f80a31205be28481fc79c533 (diff) | |
| parent | 27f0a9fe625f6fa8123e0e7364a4c0c0e10d5702 (diff) | |
Merge pull request #63 from rflamary/prV0.5
[MRG] Release 0.5
Diffstat (limited to 'ot/gpu')
| -rw-r--r-- | ot/gpu/__init__.py | 17 | ||||
| -rw-r--r-- | ot/gpu/da.py | 3 |
2 files changed, 12 insertions, 8 deletions
diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index 0187a4f..deda6b1 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -9,15 +9,17 @@ By default, the functions in this module accept and return numpy arrays in order to proide drop-in replacement for the other POT function but the transfer between CPU en GPU comes with a significant overhead. -In order to get the best erformances, we recommend to given only cupy +In order to get the best erformances, we recommend to give only cupy arrays to the functions and desactivate the conversion to numpy of the result of the function with parameter ``to_numpy=False``. - - - """ +# Author: Remi Flamary <remi.flamary@unice.fr> +# Leo Gautheron <https://github.com/aje> +# +# License: MIT License + from . import bregman from . import da from .bregman import sinkhorn @@ -27,10 +29,9 @@ from . import utils from .utils import dist, to_gpu, to_np -# Author: Remi Flamary <remi.flamary@unice.fr> -# Leo Gautheron <https://github.com/aje> -# -# License: MIT License + + __all__ = ["utils", "dist", "sinkhorn", "sinkhorn_lpl1_mm", 'bregman', 'da', 'to_gpu', 'to_np'] + diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 6aba29c..4a98038 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -10,10 +10,12 @@ Domain adaptation with optimal transport with GPU implementation # # License: MIT License + import cupy as np # np used for matrix computation import cupy as cp # cp used for cupy specific operations import numpy as npp from . import utils + from .bregman import sinkhorn @@ -131,6 +133,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, # separated W = np.ones(M.shape) for (i, c) in enumerate(classes): + majs = np.sum(transp[indices_labels[i]], axis=0) majs = p * ((majs + epsilon)**(p - 1)) W[indices_labels[i]] = majs |