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authorRémi Flamary <remi.flamary@gmail.com>2018-09-24 15:05:09 +0200
committerRémi Flamary <remi.flamary@gmail.com>2018-09-24 15:05:09 +0200
commit75e78022d2df350ea220cee1b5e759ef9fc35a5b (patch)
tree81f6ac35917d23dbca1bc95b7c5296d1ac2175a0 /test
parentf45f7a68b221ec5b619b8fd8de797815a1eecf43 (diff)
update tests
Diffstat (limited to 'test')
-rw-r--r--test/test_gpu.py89
1 files changed, 48 insertions, 41 deletions
diff --git a/test/test_gpu.py b/test/test_gpu.py
index 1e97c45..51a0cff 100644
--- a/test/test_gpu.py
+++ b/test/test_gpu.py
@@ -17,63 +17,70 @@ except ImportError:
@pytest.mark.skipif(nogpu, reason="No GPU available")
-def test_gpu_sinkhorn():
+def test_gpu_dist():
rng = np.random.RandomState(0)
- def describe_res(r):
- print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format(
- np.min(r), np.max(r), np.mean(r), np.std(r)))
-
for n_samples in [50, 100, 500, 1000]:
print(n_samples)
a = rng.rand(n_samples // 4, 100)
b = rng.rand(n_samples, 100)
- time1 = time.time()
- transport = ot.da.OTDA_sinkhorn()
- transport.fit(a, b)
- G1 = transport.G
- time2 = time.time()
- transport = ot.gpu.da.OTDA_sinkhorn()
- transport.fit(a, b)
- G2 = transport.G
- time3 = time.time()
- print("Normal sinkhorn, time: {:6.2f} sec ".format(time2 - time1))
- describe_res(G1)
- print(" GPU sinkhorn, time: {:6.2f} sec ".format(time3 - time2))
- describe_res(G2)
-
- np.testing.assert_allclose(G1, G2, rtol=1e-5, atol=1e-5)
+
+ 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(), to_numpy=False)
@pytest.mark.skipif(nogpu, reason="No GPU available")
-def test_gpu_sinkhorn_lpl1():
+def test_gpu_sinkhorn():
rng = np.random.RandomState(0)
- def describe_res(r):
- print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}"
- .format(np.min(r), np.max(r), np.mean(r), np.std(r)))
+ 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)
+
+ 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)
+
+ G2 = ot.gpu.sinkhorn(wa, wb, M2, reg, to_numpy=False)
+
+
+@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)
- time1 = time.time()
- transport = ot.da.OTDA_lpl1()
- transport.fit(a, labels_a, b)
- G1 = transport.G
- time2 = time.time()
- transport = ot.gpu.da.OTDA_lpl1()
- transport.fit(a, labels_a, b)
- G2 = transport.G
- time3 = time.time()
- print("Normal sinkhorn lpl1, time: {:6.2f} sec ".format(
- time2 - time1))
- describe_res(G1)
- print(" GPU sinkhorn lpl1, time: {:6.2f} sec ".format(
- time3 - time2))
- describe_res(G2)
-
- np.testing.assert_allclose(G1, G2, rtol=1e-3, atol=1e-3)
+
+ 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)
+
+ G2 = ot.gpu.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M2, reg, to_numpy=False)