Add infer_shape to abstract convolutions

theano/tensor/nnet/abstract_conv.py

@@ -322,6 +322,21 @@ class AbstractConv2d(BaseAbstractConv2d):
         d_weights = patternbroadcast(d_weights, weights.broadcastable)
         return d_bottom, d_weights
 
+    def infer_shape(self, node, input_shapes):
+        imshp = input_shapes[0]
+        kshp = input_shapes[1]
+
+        # replace symbolic shapes with known constant shapes
+        if self.imshp is not None:
+            imshp = [imshp[i] if self.imshp[i] is None else self.imshp[i]
+                     for i in range(4)]
+        if self.kshp is not None:
+            kshp = [kshp[i] if self.kshp[i] is None else self.kshp[i]
+                    for i in range(4)]
+        res = get_conv_output_shape(imshp, kshp, self.border_mode,
+                                    self.subsample)
+        return [res]
+
 
 class AbstractConv2d_gradWeights(BaseAbstractConv2d):
     """Gradient wrt. filters for `AbstractConv2d`.
@@ -387,6 +402,19 @@ class AbstractConv2d_gradWeights(BaseAbstractConv2d):
     def connection_pattern(self, node):
         return [[1], [1], [0]]  # no connection to height, width
 
+    def infer_shape(self, node, input_shapes):
+        # We use self.kshp (that was passed when creating the Op) if possible,
+        # or fall back to the `shape` input of the node.
+        # TODO: when there is no subsampling, try to infer the kernel shape
+        # from the shapes of inputs.
+        imshp = input_shapes[0]
+        topshp = input_shapes[1]
+        kshp = self.kshp[:] if self.kshp is not None else [None] * 4
+        fallback_kshp = [topshp[1], imshp[1], node.inputs[2][0], node.inputs[2][1]]
+        kshp = [fallback_kshp[i] if kshp[i] is None else kshp[i]
+                for i in range(4)]
+        return [kshp]
+
 
 class AbstractConv2d_gradInputs(BaseAbstractConv2d):
     """Gradient wrt. inputs for `AbstractConv2d`.
@@ -448,3 +476,17 @@ class AbstractConv2d_gradInputs(BaseAbstractConv2d):
 
     def connection_pattern(self, node):
         return [[1], [1], [0]]  # no connection to height, width
+
+    def infer_shape(self, node, input_shapes):
+        # We use self.imshp (that was passed when creating the Op) if possible,
+        # or fall back to the `shape` input of the node.
+        # TODO: when there is no subsampling, try to infer the image shape
+        # from the shapes of inputs.
+        kshp = input_shapes[0]
+        topshp = input_shapes[1]
+        imshp = self.imshp[:] if self.imshp is not None else [None] * 4
+        fallback_imshp = [topshp[0], kshp[1], node.inputs[2][0],
+                          node.inputs[2][1]]
+        imshp = [fallback_imshp[i] if imshp[i] is None else imshp[i]
+                 for i in range(4)]
+        return [imshp]