Fix bad shape inference due to wrong broadcast in DownsampleFactorMax

theano/tensor/signal/downsample.py

@@ -256,7 +256,10 @@ class DownsampleFactorMax(Op):
             raise TypeError()
         # TODO: consider restricting the dtype?
         x = tensor.as_tensor_variable(x)
-        return gof.Apply(self, [x], [x.type()])
+        # If the input shape are broadcastable we can have 0 in the output shape
+        broad = x.broadcastable[:2] + (False, False)
+        out = tensor.TensorType(x.dtype, broad)
+        return gof.Apply(self, [x], [out()])
 
     def perform(self, node, inp, out):
         x, = inp

theano/tensor/signal/tests/test_downsample.py

@@ -801,6 +801,16 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                                             [image_val, maxout_val, gz_val],
                                             MaxPoolGrad,
                                             warn=False)
+        # checking with broadcastable input
+        image = tensor.tensor(dtype=theano.config.floatX,
+                              broadcastable=(False, False, True, True))
+        image_val = rng.rand(4, 6, 1, 1)
+        self._compile_and_check(
+            [image],
+            [DownsampleFactorMax((2, 2),
+                                 ignore_border=True,
+                                 padding=(0, 0))(image)],
+            [image_val], DownsampleFactorMax)
 
     def test_opt_max_to_average(self):
         im = theano.tensor.tensor4()