[MRG] Solve example throwing an error when executed on a GPU (#391)

* Solve example throwing an error when executed on a GPU

* add PR to releases.md

* update pep8 command

* pep8

Author: ncassereau

Makefile

@@ -42,7 +42,7 @@ clean : FORCE
 	$(PYTHON) setup.py clean
 
 pep8 :
-	flake8 examples/ ot/ test/
+	flake8 examples/ ot/ test/ --count --max-line-length=127 --statistics --show-source
 
 test : FORCE pep8
 	$(PYTHON) -m pytest --durations=20 -v test/ --doctest-modules --ignore ot/gpu/  

RELEASES.md

@@ -19,6 +19,7 @@
 incomplete transport plan above a certain size (slightly above 46k, its square being
 roughly 2^31) (PR #381)
 - Error raised when mass mismatch in emd2 (PR #386)
+- Fixed an issue where a pytorch example would throw an error if executed on a GPU (Issue #389, PR #391)
 
 
 ## 0.8.2

examples/backends/plot_sliced_wass_grad_flow_pytorch.py

@@ -74,7 +74,7 @@
 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):
@@ -136,7 +136,7 @@ def _update_plot(i):
 loss_iter = []
 
 # generator for random permutations
-gen = torch.Generator()
+gen = torch.Generator(device=device)
 gen.manual_seed(42)
 
 alpha = 0.5

ot/gromov.py

@@ -1581,7 +1581,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun,
 
     error = []
 
-    while(err > tol and cpt < max_iter):
+    while (err > tol and cpt < max_iter):
         Cprev = C
 
         T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun,
@@ -1725,7 +1725,7 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_
         log_['err_structure'] = []
         log_['Ts_iter'] = []
 
-    while((err_feature > tol or err_structure > tol) and cpt < max_iter):
+    while ((err_feature > tol or err_structure > tol) and cpt < max_iter):
         Cprev = C
         Xprev = X
 

ot/lp/cvx.py

@@ -80,7 +80,7 @@ def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-po
     if weights is None:
         weights = np.ones(A.shape[1]) / A.shape[1]
     else:
-        assert(len(weights) == A.shape[1])
+        assert len(weights) == A.shape[1]
 
     n_distributions = A.shape[1]
     n = A.shape[0]

ot/unbalanced.py

@@ -734,7 +734,7 @@ def barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3,
     if weights is None:
         weights = nx.ones(n_hists, type_as=A) / n_hists
     else:
-        assert(len(weights) == A.shape[1])
+        assert len(weights) == A.shape[1]
 
     if log:
         log = {'err': []}
@@ -882,7 +882,7 @@ def barycenter_unbalanced_sinkhorn(A, M, reg, reg_m, weights=None,
     if weights is None:
         weights = nx.ones(n_hists, type_as=A) / n_hists
     else:
-        assert(len(weights) == A.shape[1])
+        assert len(weights) == A.shape[1]
 
     if log:
         log = {'err': []}