From 767171593f2a98a26b9a39bf110a45085e3b982e Mon Sep 17 00:00:00 2001 From: Nathan Cassereau <84033440+ncassereau-idris@users.noreply.github.com> Date: Thu, 24 Mar 2022 10:53:47 +0100 Subject: [MRG] Domain adaptation and unbalanced solvers with backend support (#343) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 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 --- test/test_sliced.py | 32 ++++++++------------------------ 1 file changed, 8 insertions(+), 24 deletions(-) (limited to 'test/test_sliced.py') 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") -- cgit v1.2.3