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"""Tests for module gpu for gpu acceleration """
# Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
import numpy as np
import ot
import pytest
try: # test if cudamat installed
import ot.gpu
nogpu = False
except ImportError:
nogpu = True
@pytest.mark.skipif(nogpu, reason="No GPU available")
def test_gpu_old_doctests():
a = [.5, .5]
b = [.5, .5]
M = [[0., 1.], [1., 0.]]
G = ot.sinkhorn(a, b, M, 1)
np.testing.assert_allclose(G, np.array([[0.36552929, 0.13447071],
[0.13447071, 0.36552929]]))
@pytest.mark.skipif(nogpu, reason="No GPU available")
def test_gpu_dist():
rng = np.random.RandomState(0)
for n_samples in [50, 100, 500, 1000]:
print(n_samples)
a = rng.rand(n_samples // 4, 100)
b = rng.rand(n_samples, 100)
M = ot.dist(a.copy(), b.copy())
M2 = ot.gpu.dist(a.copy(), b.copy())
np.testing.assert_allclose(M, M2, rtol=1e-10)
M2 = ot.gpu.dist(a.copy(), b.copy(), metric='euclidean', to_numpy=False)
# check raise not implemented wrong metric
with pytest.raises(NotImplementedError):
M2 = ot.gpu.dist(a.copy(), b.copy(), metric='cityblock', to_numpy=False)
@pytest.mark.skipif(nogpu, reason="No GPU available")
def test_gpu_sinkhorn():
rng = np.random.RandomState(0)
for n_samples in [50, 100, 500, 1000]:
a = rng.rand(n_samples // 4, 100)
b = rng.rand(n_samples, 100)
wa = ot.unif(n_samples // 4)
wb = ot.unif(n_samples)
wb2 = np.random.rand(n_samples, 20)
wb2 /= wb2.sum(0, keepdims=True)
M = ot.dist(a.copy(), b.copy())
M2 = ot.gpu.dist(a.copy(), b.copy(), to_numpy=False)
reg = 1
G = ot.sinkhorn(wa, wb, M, reg)
G1 = ot.gpu.sinkhorn(wa, wb, M, reg)
np.testing.assert_allclose(G1, G, rtol=1e-10)
# run all on gpu
ot.gpu.sinkhorn(wa, wb, M2, reg, to_numpy=False, log=True)
# run sinkhorn for multiple targets
ot.gpu.sinkhorn(wa, wb2, M2, reg, to_numpy=False, log=True)
@pytest.mark.skipif(nogpu, reason="No GPU available")
def test_gpu_sinkhorn_lpl1():
rng = np.random.RandomState(0)
for n_samples in [50, 100, 500]:
print(n_samples)
a = rng.rand(n_samples // 4, 100)
labels_a = np.random.randint(10, size=(n_samples // 4))
b = rng.rand(n_samples, 100)
wa = ot.unif(n_samples // 4)
wb = ot.unif(n_samples)
M = ot.dist(a.copy(), b.copy())
M2 = ot.gpu.dist(a.copy(), b.copy(), to_numpy=False)
reg = 1
G = ot.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M, reg)
G1 = ot.gpu.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M, reg)
np.testing.assert_allclose(G1, G, rtol=1e-10)
ot.gpu.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M2, reg, to_numpy=False, log=True)
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