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-rw-r--r--examples/backends/plot_sliced_wass_grad_flow_pytorch.py10
1 files changed, 5 insertions, 5 deletions
diff --git a/examples/backends/plot_sliced_wass_grad_flow_pytorch.py b/examples/backends/plot_sliced_wass_grad_flow_pytorch.py
index cf5d64d..f00de50 100644
--- a/examples/backends/plot_sliced_wass_grad_flow_pytorch.py
+++ b/examples/backends/plot_sliced_wass_grad_flow_pytorch.py
@@ -74,7 +74,7 @@ x_all = np.zeros((nb_iter_max, x1.shape[0], 2))
loss_iter = []
# generator for random permutations
-gen = torch.Generator()
+gen = torch.Generator(device=device)
gen.manual_seed(42)
for i in range(nb_iter_max):
@@ -103,7 +103,7 @@ ax = pl.axis()
# %%
# Animate trajectories of the gradient flow along iteration
-# -------------------------------------------------------
+# ---------------------------------------------------------
pl.figure(3, (8, 4))
@@ -122,7 +122,7 @@ ani = animation.FuncAnimation(pl.gcf(), _update_plot, nb_iter_max, interval=100,
# %%
# Compute the Sliced Wasserstein Barycenter
-#
+# -----------------------------------------
x1_torch = torch.tensor(x1).to(device=device)
x3_torch = torch.tensor(x3).to(device=device)
xbinit = np.random.randn(500, 2) * 10 + 16
@@ -136,7 +136,7 @@ x_all = np.zeros((nb_iter_max, xbary_torch.shape[0], 2))
loss_iter = []
# generator for random permutations
-gen = torch.Generator()
+gen = torch.Generator(device=device)
gen.manual_seed(42)
alpha = 0.5
@@ -169,7 +169,7 @@ ax = pl.axis()
# %%
# Animate trajectories of the barycenter along gradient descent
-# -------------------------------------------------------
+# -------------------------------------------------------------
pl.figure(5, (8, 4))