[WIP] Add Wasserstein GAN and debug memory leak (#254)

* add example and debug memory leak

* print stuff

* speedup gallery

* Apply suggestions from code review

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

* test cells

* proper header gan exmaple

* cleanup sections

* last changes ?

Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>

1 files changed, 23 insertions, 11 deletions

diff --git a/ot/backend.py b/ot/backend.py
index d68f5cf..8f46900 100644
--- a/ot/backend.py
+++ b/ot/backend.py
@@ -389,6 +389,26 @@ class TorchBackend(Backend):
     __name__ = 'torch'
     __type__ = torch_type
 
+    def __init__(self):
+
+        from torch.autograd import Function
+
+        # define a function that takes inputs val and grads
+        # ad returns a val tensor with proper gradients
+        class ValFunction(Function):
+
+            @staticmethod
+            def forward(ctx, val, grads, *inputs):
+                ctx.grads = grads
+                return val
+
+            @staticmethod
+            def backward(ctx, grad_output):
+                # the gradients are grad
+                return (None, None) + ctx.grads
+
+        self.ValFunction = ValFunction
+
     def to_numpy(self, a):
         return a.cpu().detach().numpy()
 
@@ -399,20 +419,12 @@ class TorchBackend(Backend):
             return torch.as_tensor(a, dtype=type_as.dtype, device=type_as.device)
 
     def set_gradients(self, val, inputs, grads):
-        from torch.autograd import Function
 
-        # define a function that takes inputs and return val
-        class ValFunction(Function):
-            @staticmethod
-            def forward(ctx, *inputs):
-                return val
+        Func = self.ValFunction()
 
-            @staticmethod
-            def backward(ctx, grad_output):
-                # the gradients are grad
-                return grads
+        res = Func.apply(val, grads, *inputs)
 
-        return ValFunction.apply(*inputs)
+        return res
 
     def zeros(self, shape, type_as=None):
         if type_as is None: