From f45f7a68b221ec5b619b8fd8de797815a1eecf43 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 14:30:44 +0200 Subject: pep8 --- ot/gpu/bregman.py | 20 ++++++++++---------- ot/gpu/da.py | 16 ++++++++-------- ot/gpu/utils.py | 34 ++++++++++++++++------------------ 3 files changed, 34 insertions(+), 36 deletions(-) (limited to 'ot/gpu') diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 912104c..6714098 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -8,12 +8,11 @@ Bregman projections for regularized OT with GPU # # License: MIT License -import cupy as np # np used for matrix computation -import cupy as cp # cp used for cupy specific operations +import cupy as np # np used for matrix computation +import cupy as cp # cp used for cupy specific operations from . import utils - def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, verbose=False, log=False, to_numpy=True, **kwargs): """ @@ -159,7 +158,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, 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.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 if log: @@ -177,24 +176,25 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, if nbb: # return only loss #res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) (explodes cupy memory) - res=np.empty(nbb) + res = np.empty(nbb) for i in range(nbb): - res[i]=np.sum(u[:,None,i]*(K*M)*v[None,:,i]) + res[i] = np.sum(u[:, None, i] * (K * M) * v[None, :, i]) if to_numpy: - res=utils.to_np(res) + res = utils.to_np(res) if log: return res, log else: return res else: # return OT matrix - res=u.reshape((-1, 1)) * K * v.reshape((1, -1)) + res = u.reshape((-1, 1)) * K * v.reshape((1, -1)) if to_numpy: - res=utils.to_np(res) + res = utils.to_np(res) if log: return res, log else: return res + # define sinkhorn as sinkhorn_knopp -sinkhorn=sinkhorn_knopp \ No newline at end of file +sinkhorn = sinkhorn_knopp diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 8bcc2aa..8c63870 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -10,15 +10,16 @@ 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 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 + def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, verbose=False, - log=False,to_numpy=True): + log=False, to_numpy=True): """ Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization @@ -101,15 +102,14 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, ot.optim.cg : General regularized OT """ - + a, labels_a, b, M = utils.to_gpu(a, labels_a, b, M) - - + p = 0.5 epsilon = 1e-3 indices_labels = [] - labels_a2=cp.asnumpy(labels_a) + labels_a2 = cp.asnumpy(labels_a) classes = npp.unique(labels_a2) for c in classes: idxc, = utils.to_gpu(npp.where(labels_a2 == c)) @@ -120,7 +120,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, for cpt in range(numItermax): Mreg = M + eta * W transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax, - stopThr=stopInnerThr,to_numpy=False) + stopThr=stopInnerThr, to_numpy=False) # the transport has been computed. Check if classes are really # separated W = np.ones(M.shape) diff --git a/ot/gpu/utils.py b/ot/gpu/utils.py index 6d0c853..d349a6d 100644 --- a/ot/gpu/utils.py +++ b/ot/gpu/utils.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -Utility functions for GPU +Utility functions for GPU """ # Author: Remi Flamary @@ -9,9 +9,8 @@ Utility functions for GPU # # License: MIT License -import cupy as np # np used for matrix computation -import cupy as cp # cp used for cupy specific operations - +import cupy as np # np used for matrix computation +import cupy as cp # cp used for cupy specific operations def euclidean_distances(a, b, squared=False, to_numpy=True): @@ -34,16 +33,16 @@ def euclidean_distances(a, b, squared=False, to_numpy=True): c : (n x m) np.ndarray or cupy.ndarray pairwise euclidean distance distance matrix """ - + a, b = to_gpu(a, b) - - a2=np.sum(np.square(a),1) - b2=np.sum(np.square(b),1) - - c=-2*np.dot(a,b.T) - c+=a2[:,None] - c+=b2[None,:] - + + a2 = np.sum(np.square(a), 1) + b2 = np.sum(np.square(b), 1) + + c = -2 * np.dot(a, b.T) + c += a2[:, None] + c += b2[None, :] + if not squared: np.sqrt(c, out=c) if to_numpy: @@ -51,6 +50,7 @@ def euclidean_distances(a, b, squared=False, to_numpy=True): else: return c + def dist(x1, x2=None, metric='sqeuclidean', to_numpy=True): """Compute distance between samples in x1 and x2 on gpu @@ -61,8 +61,8 @@ def dist(x1, x2=None, metric='sqeuclidean', to_numpy=True): matrix with n1 samples of size d x2 : np.array (n2,d), optional matrix with n2 samples of size d (if None then x2=x1) - metric : str - Metric from 'sqeuclidean', 'euclidean', + metric : str + Metric from 'sqeuclidean', 'euclidean', Returns @@ -80,7 +80,6 @@ def dist(x1, x2=None, metric='sqeuclidean', to_numpy=True): return euclidean_distances(x1, x2, squared=False, to_numpy=to_numpy) else: raise NotImplementedError - def to_gpu(*args): @@ -91,10 +90,9 @@ def to_gpu(*args): return cp.asarray(args[0]) - def to_np(*args): """ convert GPU arras to numpy and return them""" if len(args) > 1: return (cp.asnumpy(x) for x in args) else: - return cp.asnumpy(args[0]) \ No newline at end of file + return cp.asnumpy(args[0]) -- cgit v1.2.3