diff options
Diffstat (limited to 'ot/gpu')
| -rw-r--r-- | ot/gpu/__init__.py | 15 | ||||
| -rw-r--r-- | ot/gpu/da.py | 19 |
2 files changed, 27 insertions, 7 deletions
diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index 0187a4f..213578c 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -13,11 +13,13 @@ In order to get the best erformances, we recommend to given 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..f5e7daa 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 +133,22 @@ 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): +<<<<<<< HEAD + (_, nbRow) = indices_labels[i].shape + tmpC_GPU = cudamat.empty((Nfin, nbRow)).assign(0) + transp_GPU.transpose().select_columns(indices_labels[i], tmpC_GPU) + majs_GPU = tmpC_GPU.sum(axis=1).add(epsilon) + cudamat.pow(majs_GPU, (p - 1)) + majs_GPU.mult(p) + + tmpC_GPU.assign(0) + tmpC_GPU.add_col_vec(majs_GPU) + W_GPU.set_selected_columns(indices_labels[i], tmpC_GPU) + + W_GPU = W_GPU.transpose() + + return transp_GPU.asarray() +======= majs = np.sum(transp[indices_labels[i]], axis=0) majs = p * ((majs + epsilon)**(p - 1)) W[indices_labels[i]] = majs @@ -139,3 +157,4 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, return utils.to_np(transp) else: return transp +>>>>>>> master |