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

1 files changed, 24 insertions, 2 deletions

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))