code review: move test_wasserstein_distance_grad from test_wasserstein_distance.py to test_wasserstein_with_tensors.py

3 files changed, 27 insertions, 28 deletions

diff --git a/src/python/CMakeLists.txt b/src/python/CMakeLists.txt
index cc71503f..c09996fe 100644
--- a/src/python/CMakeLists.txt
+++ b/src/python/CMakeLists.txt
@@ -499,13 +499,14 @@ if(PYTHONINTERP_FOUND)
 
     # Wasserstein
     if(OT_FOUND AND PYBIND11_FOUND)
-      if(TORCH_FOUND AND EAGERPY_FOUND)
+      # EagerPy dependency because of enable_autodiff=True
+      if(EAGERPY_FOUND)
         add_gudhi_py_test(test_wasserstein_distance)
       endif()
       add_gudhi_py_test(test_wasserstein_barycenter)
     endif()
     if(OT_FOUND)
-      if(TENSORFLOW_FOUND AND EAGERPY_FOUND)
+      if(TORCH_FOUND AND TENSORFLOW_FOUND AND EAGERPY_FOUND)
         add_gudhi_py_test(test_wasserstein_with_tensors)
       endif()
     endif()
diff --git a/src/python/test/test_wasserstein_distance.py b/src/python/test/test_wasserstein_distance.py
index 90d26809..e3b521d6 100755
--- a/src/python/test/test_wasserstein_distance.py
+++ b/src/python/test/test_wasserstein_distance.py
@@ -97,27 +97,3 @@ def test_wasserstein_distance_pot():
 def test_wasserstein_distance_hera():
     _basic_wasserstein(hera_wrap(delta=1e-12), 1e-12, test_matching=False)
     _basic_wasserstein(hera_wrap(delta=.1), .1, test_matching=False)
-
-def test_wasserstein_distance_grad():
-    import torch
-
-    diag1 = torch.tensor([[2.7, 3.7], [9.6, 14.0], [34.2, 34.974]], requires_grad=True)
-    diag2 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True)
-    diag3 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True)
-    assert diag1.grad is None and diag2.grad is None and diag3.grad is None
-    dist12 = pot(diag1, diag2, internal_p=2, order=2, enable_autodiff=True)
-    dist30 = pot(diag3, torch.tensor([]), internal_p=2, order=2, enable_autodiff=True)
-    dist12.backward()
-    dist30.backward()
-    assert not torch.isnan(diag1.grad).any() and not torch.isnan(diag2.grad).any() and not torch.isnan(diag3.grad).any()
-    diag4 = torch.tensor([[0., 10.]], requires_grad=True)
-    diag5 = torch.tensor([[1., 11.], [3., 4.]], requires_grad=True)
-    dist45 = pot(diag4, diag5, internal_p=1, order=1, enable_autodiff=True)
-    assert dist45 == 3.
-    dist45.backward()
-    assert np.array_equal(diag4.grad, [[-1., -1.]])
-    assert np.array_equal(diag5.grad, [[1., 1.], [-1., 1.]])
-    diag6 = torch.tensor([[5., 10.]], requires_grad=True)
-    pot(diag6, diag6, internal_p=2, order=2, enable_autodiff=True).backward()
-    # https://github.com/jonasrauber/eagerpy/issues/6
-    # assert np.array_equal(diag6.grad, [[0., 0.]])
diff --git a/src/python/test/test_wasserstein_with_tensors.py b/src/python/test/test_wasserstein_with_tensors.py
index 8957705d..e3f1411a 100755
--- a/src/python/test/test_wasserstein_with_tensors.py
+++ b/src/python/test/test_wasserstein_with_tensors.py
@@ -10,10 +10,32 @@
 
 from gudhi.wasserstein import wasserstein_distance as pot
 import numpy as np
+import torch
+import tensorflow as tf
 
-def test_wasserstein_distance_grad_tensorflow():
-    import tensorflow as tf
+def test_wasserstein_distance_grad():
+    diag1 = torch.tensor([[2.7, 3.7], [9.6, 14.0], [34.2, 34.974]], requires_grad=True)
+    diag2 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True)
+    diag3 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True)
+    assert diag1.grad is None and diag2.grad is None and diag3.grad is None
+    dist12 = pot(diag1, diag2, internal_p=2, order=2, enable_autodiff=True)
+    dist30 = pot(diag3, torch.tensor([]), internal_p=2, order=2, enable_autodiff=True)
+    dist12.backward()
+    dist30.backward()
+    assert not torch.isnan(diag1.grad).any() and not torch.isnan(diag2.grad).any() and not torch.isnan(diag3.grad).any()
+    diag4 = torch.tensor([[0., 10.]], requires_grad=True)
+    diag5 = torch.tensor([[1., 11.], [3., 4.]], requires_grad=True)
+    dist45 = pot(diag4, diag5, internal_p=1, order=1, enable_autodiff=True)
+    assert dist45 == 3.
+    dist45.backward()
+    assert np.array_equal(diag4.grad, [[-1., -1.]])
+    assert np.array_equal(diag5.grad, [[1., 1.], [-1., 1.]])
+    diag6 = torch.tensor([[5., 10.]], requires_grad=True)
+    pot(diag6, diag6, internal_p=2, order=2, enable_autodiff=True).backward()
+    # https://github.com/jonasrauber/eagerpy/issues/6
+    # assert np.array_equal(diag6.grad, [[0., 0.]])
 
+def test_wasserstein_distance_grad_tensorflow():
     with tf.GradientTape() as tape:
         diag4 = tf.convert_to_tensor(tf.Variable(initial_value=np.array([[0., 10.]]),           trainable=True))
         diag5 = tf.convert_to_tensor(tf.Variable(initial_value=np.array([[1., 11.], [3., 4.]]), trainable=True))