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""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
Author(s): Mathieu Carriere
Copyright (C) 2020 Inria
Modification(s):
- YYYY/MM Author: Description of the modification
"""
from gudhi.wasserstein import wasserstein_distance as pot
import numpy as np
import torch
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))
dist45 = pot(diag4, diag5, internal_p=1, order=1, enable_autodiff=True)
assert dist45 == 3.
grads = tape.gradient(dist45, [diag4, diag5])
assert np.array_equal(grads[0].values, [[-1., -1.]])
assert np.array_equal(grads[1].values, [[1., 1.], [-1., 1.]])
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