[WIP] Fix gradient scaling bug in emd

.circleci/config.yml

@@ -134,24 +134,21 @@ jobs:
             name: Deploy docs
             command: |
               set -e;
-              if [ "${CIRCLE_BRANCH}" == "master" ]; then
-                git config --global user.email "circle@PythonOT.com";
-                git config --global user.name "Circle CI";
-                cd ~/PythonOT.github.io;
-                git checkout master
-                git remote -v
-                git fetch origin
-                git reset --hard origin/master
-                git clean -xdf
-                echo "Deploying dev docs for ${CIRCLE_BRANCH}.";
-                cp -a /tmp/build/html/* .;
-                touch .nojekyll;
-                git add -A;
-                git commit -m "CircleCI update of dev docs (${CIRCLE_BUILD_NUM}).";
-                git push origin master;
-              else
-                echo "No deployment (build: ${CIRCLE_BRANCH}).";
-              fi
+              git config --global user.email "circle@PythonOT.com";
+              git config --global user.name "Circle CI";
+              cd ~/PythonOT.github.io;
+              git checkout master
+              git remote -v
+              git fetch origin
+              git reset --hard origin/master
+              git clean -xdf
+              echo "Deploying dev docs for ${CIRCLE_BRANCH}.";
+              cp -a /tmp/build/html/* .;
+              touch .nojekyll;
+              git add -A;
+              git commit -m "CircleCI update of dev docs (${CIRCLE_BUILD_NUM}).";
+              git push origin master;
+
 
 
 workflows:

examples/plot_Intro_OT.py

@@ -327,7 +327,7 @@
     time_sinkhorn_reg[k] = time.time() - start
 
     if k % 4 == 0 and k > 0:  # we only plot a few
-        ax = pl.subplot(1, 5, k / 4)
+        ax = pl.subplot(1, 5, k // 4)
         im = pl.imshow(ot_sinkhorn, vmin=0, vmax=max_ot)
         pl.title('reg={0:.2g}'.format(reg_parameter[k]))
         pl.xlabel('Cafés')

ot/backend.py

@@ -1203,7 +1203,7 @@ def forward(ctx, val, grads, *inputs):
             @staticmethod
             def backward(ctx, grad_output):
                 # the gradients are grad
-                return (None, None) + ctx.grads
+                return (None, None) + tuple(g * grad_output for g in ctx.grads)
 
         self.ValFunction = ValFunction
 

test/test_ot.py

@@ -126,6 +126,22 @@ def test_emd2_gradients():
         assert b1.shape == b1.grad.shape
         assert M1.shape == M1.grad.shape
 
+        # Testing for bug #309, checking for scaling of gradient
+        a2 = torch.tensor(a, requires_grad=True)
+        b2 = torch.tensor(a, requires_grad=True)
+        M2 = torch.tensor(M, requires_grad=True)
+
+        val = 10.0 * ot.emd2(a2, b2, M2)
+
+        val.backward()
+
+        assert np.allclose(10.0 * a1.grad.cpu().detach().numpy(),
+                           a2.grad.cpu().detach().numpy())
+        assert np.allclose(10.0 * b1.grad.cpu().detach().numpy(),
+                           b2.grad.cpu().detach().numpy())
+        assert np.allclose(10.0 * M1.grad.cpu().detach().numpy(),
+                           M2.grad.cpu().detach().numpy())
+
 
 def test_emd_emd2():
     # test emd and emd2 for simple identity