From 0eac835c70cc1a13bb998f3b6cdb0515fafc05e1 Mon Sep 17 00:00:00 2001
From: ncassereau-idris <84033440+ncassereau-idris@users.noreply.github.com>
Date: Fri, 5 Nov 2021 15:57:08 +0100
Subject: [MRG] Tests with types/device on sliced/bregman/gromov functions
 (#303)

* First draft : making pytest use gpu for torch testing

* bug solve

* Revert "bug solve"

This reverts commit 29b013abd162f8693128f26d8129186b79923609.

* Revert "First draft : making pytest use gpu for torch testing"

This reverts commit 2778175bcc338016c704efa4187d132fe5162e3a.

* sliced

* sliced

* ot 1dsolver

* bregman

* better print

* jax works with sinkhorn, sinkhorn_log and sinkhornn_stabilized, no need to skip them

* gromov & entropic gromov
---
 test/test_gromov.py | 75 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)

(limited to 'test/test_gromov.py')

diff --git a/test/test_gromov.py b/test/test_gromov.py
index bcbcc3a..c4bc04c 100644
--- a/test/test_gromov.py
+++ b/test/test_gromov.py
@@ -75,6 +75,41 @@ def test_gromov(nx):
         q, Gb.sum(0), atol=1e-04)  # cf convergence gromov
 
 
+def test_gromov_dtype_device(nx):
+    # setup
+    n_samples = 50  # nb samples
+
+    mu_s = np.array([0, 0])
+    cov_s = np.array([[1, 0], [0, 1]])
+
+    xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=4)
+
+    xt = xs[::-1].copy()
+
+    p = ot.unif(n_samples)
+    q = ot.unif(n_samples)
+
+    C1 = ot.dist(xs, xs)
+    C2 = ot.dist(xt, xt)
+
+    C1 /= C1.max()
+    C2 /= C2.max()
+
+    for tp in nx.__type_list__:
+        print(nx.dtype_device(tp))
+
+        C1b = nx.from_numpy(C1, type_as=tp)
+        C2b = nx.from_numpy(C2, type_as=tp)
+        pb = nx.from_numpy(p, type_as=tp)
+        qb = nx.from_numpy(q, type_as=tp)
+
+        Gb = ot.gromov.gromov_wasserstein(C1b, C2b, pb, qb, 'square_loss', verbose=True)
+        gw_valb = ot.gromov.gromov_wasserstein2(C1b, C2b, pb, qb, 'kl_loss', log=False)
+
+        nx.assert_same_dtype_device(C1b, Gb)
+        nx.assert_same_dtype_device(C1b, gw_valb)
+
+
 def test_gromov2_gradients():
     n_samples = 50  # nb samples
 
@@ -168,6 +203,46 @@ def test_entropic_gromov(nx):
         q, Gb.sum(0), atol=1e-04)  # cf convergence gromov
 
 
+@pytest.skip_backend("jax", reason="test very slow with jax backend")
+def test_entropic_gromov_dtype_device(nx):
+    # setup
+    n_samples = 50  # nb samples
+
+    mu_s = np.array([0, 0])
+    cov_s = np.array([[1, 0], [0, 1]])
+
+    xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=42)
+
+    xt = xs[::-1].copy()
+
+    p = ot.unif(n_samples)
+    q = ot.unif(n_samples)
+
+    C1 = ot.dist(xs, xs)
+    C2 = ot.dist(xt, xt)
+
+    C1 /= C1.max()
+    C2 /= C2.max()
+
+    for tp in nx.__type_list__:
+        print(nx.dtype_device(tp))
+
+        C1b = nx.from_numpy(C1, type_as=tp)
+        C2b = nx.from_numpy(C2, type_as=tp)
+        pb = nx.from_numpy(p, type_as=tp)
+        qb = nx.from_numpy(q, type_as=tp)
+
+        Gb = ot.gromov.entropic_gromov_wasserstein(
+            C1b, C2b, pb, qb, 'square_loss', epsilon=5e-4, verbose=True
+        )
+        gw_valb = ot.gromov.entropic_gromov_wasserstein2(
+            C1b, C2b, pb, qb, 'square_loss', epsilon=5e-4, verbose=True
+        )
+
+        nx.assert_same_dtype_device(C1b, Gb)
+        nx.assert_same_dtype_device(C1b, gw_valb)
+
+
 def test_pointwise_gromov(nx):
     n_samples = 50  # nb samples
 
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