[MRG] Domain adaptation and unbalanced solvers with backend support (#343)

* First draft

* Add matrix inverse and square root to backend

* Eigen decomposition for older versions of pytorch (1.8.1 and older)

* Corrected eigen decomposition for pytorch 1.8.1 and older

* Spectral theorem is a thing

* Optimization

* small optimization

* More functions converted

* pep8

* remove a warning and prepare torch meshgrid for future torch release (which will change default indexing)

* dots and pep8

* Meshgrid corrected for older version and prepared for future versions changes

* New backend functions

* Base transport

* LinearTransport

* All transport classes + pep8

* PR added to release file

* Jcpot barycenter test

* unbalanced with backend

* pep8

* bug solve

* test of domain adaptation with backends

* solve bug for tic toc & macos

* solving scipy deprecation warning

* solving scipy deprecation warning attempt2

* solving scipy deprecation warning attempt3

* A warning is triggered when a float->int conversion is detected

* bug solve

* docs

* release file updated

* Better handling of float->int conversion in EMD

* Corrected test for is_floating_point

* docs

* release file updated

* cupy does not allow implicit cast

* fromnumpy

* added test

* test da tf jax

* test unbalanced with no provided histogram

* using type_as argument in unif function correctly

* pep8

* transport plan cast in emd changed behaviour, now trying to cast as histogram's dtype, defaulting to cost matrix

Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>

1 files changed, 8 insertions, 24 deletions

diff --git a/test/test_sliced.py b/test/test_sliced.py
index 91e0961..08ab4fb 100644
--- a/test/test_sliced.py
+++ b/test/test_sliced.py
@@ -123,9 +123,7 @@ def test_sliced_backend(nx):
 
     n_projections = 20
 
-    xb = nx.from_numpy(x)
-    yb = nx.from_numpy(y)
-    Pb = nx.from_numpy(P)
+    xb, yb, Pb = nx.from_numpy(x, y, P)
 
     val0 = ot.sliced_wasserstein_distance(x, y, projections=P)
 
@@ -153,9 +151,7 @@ def test_sliced_backend_type_devices(nx):
     for tp in nx.__type_list__:
         print(nx.dtype_device(tp))
 
-        xb = nx.from_numpy(x, type_as=tp)
-        yb = nx.from_numpy(y, type_as=tp)
-        Pb = nx.from_numpy(P, type_as=tp)
+        xb, yb, Pb = nx.from_numpy(x, y, P, type_as=tp)
 
         valb = ot.sliced_wasserstein_distance(xb, yb, projections=Pb)
 
@@ -174,17 +170,13 @@ def test_sliced_backend_device_tf():
 
     # Check that everything stays on the CPU
     with tf.device("/CPU:0"):
-        xb = nx.from_numpy(x)
-        yb = nx.from_numpy(y)
-        Pb = nx.from_numpy(P)
+        xb, yb, Pb = nx.from_numpy(x, y, P)
         valb = ot.sliced_wasserstein_distance(xb, yb, projections=Pb)
         nx.assert_same_dtype_device(xb, valb)
 
     if len(tf.config.list_physical_devices('GPU')) > 0:
         # Check that everything happens on the GPU
-        xb = nx.from_numpy(x)
-        yb = nx.from_numpy(y)
-        Pb = nx.from_numpy(P)
+        xb, yb, Pb = nx.from_numpy(x, y, P)
         valb = ot.sliced_wasserstein_distance(xb, yb, projections=Pb)
         nx.assert_same_dtype_device(xb, valb)
         assert nx.dtype_device(valb)[1].startswith("GPU")
@@ -203,9 +195,7 @@ def test_max_sliced_backend(nx):
 
     n_projections = 20
 
-    xb = nx.from_numpy(x)
-    yb = nx.from_numpy(y)
-    Pb = nx.from_numpy(P)
+    xb, yb, Pb = nx.from_numpy(x, y, P)
 
     val0 = ot.max_sliced_wasserstein_distance(x, y, projections=P)
 
@@ -233,9 +223,7 @@ def test_max_sliced_backend_type_devices(nx):
     for tp in nx.__type_list__:
         print(nx.dtype_device(tp))
 
-        xb = nx.from_numpy(x, type_as=tp)
-        yb = nx.from_numpy(y, type_as=tp)
-        Pb = nx.from_numpy(P, type_as=tp)
+        xb, yb, Pb = nx.from_numpy(x, y, P, type_as=tp)
 
         valb = ot.max_sliced_wasserstein_distance(xb, yb, projections=Pb)
 
@@ -254,17 +242,13 @@ def test_max_sliced_backend_device_tf():
 
     # Check that everything stays on the CPU
     with tf.device("/CPU:0"):
-        xb = nx.from_numpy(x)
-        yb = nx.from_numpy(y)
-        Pb = nx.from_numpy(P)
+        xb, yb, Pb = nx.from_numpy(x, y, P)
         valb = ot.max_sliced_wasserstein_distance(xb, yb, projections=Pb)
         nx.assert_same_dtype_device(xb, valb)
 
     if len(tf.config.list_physical_devices('GPU')) > 0:
         # Check that everything happens on the GPU
-        xb = nx.from_numpy(x)
-        yb = nx.from_numpy(y)
-        Pb = nx.from_numpy(P)
+        xb, yb, Pb = nx.from_numpy(x, y, P)
         valb = ot.max_sliced_wasserstein_distance(xb, yb, projections=Pb)
         nx.assert_same_dtype_device(xb, valb)
         assert nx.dtype_device(valb)[1].startswith("GPU")