Fix ConvOp shape inference and propagation for imshp=None

theano/tensor/nnet/conv.py

@@ -373,7 +373,7 @@ class ConvOp(OpenMPOp):
 
         # Expand unknown image / kernel shapes into tuples of Nones
         if imshp is None:
-            imshp = (None, None)
+            imshp = (None, None, None)
         else:
             imshp = tuple(imshp)
         if kshp is None:
@@ -438,7 +438,7 @@ class ConvOp(OpenMPOp):
         else:
             kshp_logical = tuple(kshp_logical)
             if len(kshp_logical) != 2:
-                raise ValueError("len(kshp_logical) must be k, got %d" % len(kshp_logical))
+                raise ValueError("len(kshp_logical) must be 2, got %d" % len(kshp_logical))
             self.kshp_logical = kshp_logical
 
         # a bool
@@ -642,15 +642,6 @@ class ConvOp(OpenMPOp):
         # infer output shape from what we have
         outshp = ConvOp.getOutputShape(imshp[1:], kshp, (self.dx, self.dy),
                                        self.out_mode)
-        if not self.has_all_shape(self.imshp_logical, self.kshp_logical,
-                                  self.bsize, self.nkern):
-            # FIXME: Not sure why this is needed. I think the shape is inferred
-            # correctly no matter what, but if we return a partially symbolic
-            # shape here, test_conv_cuda_ndarray:test_gemm_grads fails. (@f0k)
-            raise theano.tensor.ShapeError()
-        # FIXME: Actually, test_conv_cuda_ndarray:test_gemm_grads only passes if
-        # we completely disable shape inference. (@f0k)
-        raise theano.tensor.ShapeError()
         return [(bsize, nkern) + outshp]
 
     def perform(self, node, inp, out):
@@ -947,8 +938,8 @@ class ConvOp(OpenMPOp):
 
         din = din(gz, filters)
 
-        assert (all(shp is None for shp in din.owner.op.outshp) or
-               all(o == i for o, i in zip(din.owner.op.outshp, self.imshp[1:])))
+        assert all(o is None or o == i
+                   for o, i in zip(din.owner.op.outshp, self.imshp[1:]))
 
         # din and dw should have the same broadcasting pattern as the
         # parameters they are the gradient of (resp. inputs and kerns).
@@ -1035,8 +1026,9 @@ using namespace std;
         d = locals()
         d.update(sub)
 
-        all_shape = self.has_all_shape(self.imshp, self.kshp,
-                                       self.nkern, self.bsize)
+        all_shape = (self.has_all_shape(self.imshp, self.kshp,
+                                       self.nkern, self.bsize) and
+                     self.has_all_shape(self.imshp_logical, self.kshp_logical))
 
         d["self_out_mode"] = self.out_mode
         d["self_dx"] = self.dx