[MRG] Cupy backend (#315)

* Cupy backend

* pep8 + bug

* working even if cupy not installed

* attempt to force codecov to ignore cupy because no gpu can be used for testing on github

* docstring

* readme

1 files changed, 12 insertions, 7 deletions

diff --git a/test/test_gromov.py b/test/test_gromov.py
index c4bc04c..5c181f2 100644
--- a/test/test_gromov.py
+++ b/test/test_gromov.py
@@ -54,9 +54,12 @@ def test_gromov(nx):
 

     gw, log = ot.gromov.gromov_wasserstein2(C1, C2, p, q, 'kl_loss', log=True)

     gwb, logb = ot.gromov.gromov_wasserstein2(C1b, C2b, pb, qb, 'kl_loss', log=True)

+    gwb = nx.to_numpy(gwb)

 

     gw_val = ot.gromov.gromov_wasserstein2(C1, C2, p, q, 'kl_loss', log=False)

-    gw_valb = ot.gromov.gromov_wasserstein2(C1b, C2b, pb, qb, 'kl_loss', log=False)

+    gw_valb = nx.to_numpy(

+        ot.gromov.gromov_wasserstein2(C1b, C2b, pb, qb, 'kl_loss', log=False)

+    )

 

     G = log['T']

     Gb = nx.to_numpy(logb['T'])

@@ -188,6 +191,7 @@ def test_entropic_gromov(nx):
         C1, C2, p, q, 'kl_loss', epsilon=1e-2, log=True)

     gwb, logb = ot.gromov.entropic_gromov_wasserstein2(

         C1b, C2b, pb, qb, 'kl_loss', epsilon=1e-2, log=True)

+    gwb = nx.to_numpy(gwb)

 

     G = log['T']

     Gb = nx.to_numpy(logb['T'])

@@ -287,8 +291,8 @@ def test_pointwise_gromov(nx):
     np.testing.assert_allclose(

         q, Gb.sum(0), atol=1e-04)  # cf convergence gromov

 

-    np.testing.assert_allclose(logb['gw_dist_estimated'], 0.0, atol=1e-08)

-    np.testing.assert_allclose(logb['gw_dist_std'], 0.0, atol=1e-08)

+    np.testing.assert_allclose(float(logb['gw_dist_estimated']), 0.0, atol=1e-08)

+    np.testing.assert_allclose(float(logb['gw_dist_std']), 0.0, atol=1e-08)

 

     G, log = ot.gromov.pointwise_gromov_wasserstein(

         C1, C2, p, q, loss, max_iter=100, alpha=0.1, log=True, verbose=True, random_state=42)

@@ -298,8 +302,8 @@ def test_pointwise_gromov(nx):
     Gb = nx.to_numpy(nx.todense(Gb))

 

     np.testing.assert_allclose(G, Gb, atol=1e-06)

-    np.testing.assert_allclose(logb['gw_dist_estimated'], 0.10342276348494964, atol=1e-8)

-    np.testing.assert_allclose(logb['gw_dist_std'], 0.0015952535464736394, atol=1e-8)

+    np.testing.assert_allclose(float(logb['gw_dist_estimated']), 0.10342276348494964, atol=1e-8)

+    np.testing.assert_allclose(float(logb['gw_dist_std']), 0.0015952535464736394, atol=1e-8)

 

 

 @pytest.skip_backend("jax", reason="test very slow with jax backend")

@@ -346,8 +350,8 @@ def test_sampled_gromov(nx):
     np.testing.assert_allclose(

         q, Gb.sum(0), atol=1e-04)  # cf convergence gromov

 

-    np.testing.assert_allclose(logb['gw_dist_estimated'], 0.05679474884977278, atol=1e-08)

-    np.testing.assert_allclose(logb['gw_dist_std'], 0.0005986592106971995, atol=1e-08)

+    np.testing.assert_allclose(float(logb['gw_dist_estimated']), 0.05679474884977278, atol=1e-08)

+    np.testing.assert_allclose(float(logb['gw_dist_std']), 0.0005986592106971995, atol=1e-08)

 

 

 def test_gromov_barycenter(nx):

@@ -486,6 +490,7 @@ def test_fgw(nx):
 

     fgw, log = ot.gromov.fused_gromov_wasserstein2(M, C1, C2, p, q, 'square_loss', alpha=0.5, log=True)

     fgwb, logb = ot.gromov.fused_gromov_wasserstein2(Mb, C1b, C2b, pb, qb, 'square_loss', alpha=0.5, log=True)

+    fgwb = nx.to_numpy(fgwb)

 

     G = log['T']

     Gb = nx.to_numpy(logb['T'])