Python net spec cleanups and top-less layers

python/caffe/net_spec.py

@@ -18,7 +18,7 @@ class -- assign to its attributes directly to name layers, and call
 are not guaranteed to be forward-compatible.
 """
 
-from collections import OrderedDict
+from collections import OrderedDict, Counter
 
 from .proto import caffe_pb2
 from google import protobuf
@@ -44,10 +44,8 @@ def to_proto(*tops):
     """Generate a NetParameter that contains all layers needed to compute
     all arguments."""
 
-    if not isinstance(tops, tuple):
-        tops = (tops,)
     layers = OrderedDict()
-    autonames = {}
+    autonames = Counter()
     for top in tops:
         top.fn._to_proto(layers, {}, autonames)
     net = caffe_pb2.NetParameter()
@@ -89,6 +87,9 @@ def to_proto(self):
 
         return to_proto(self)
 
+    def _to_proto(self, layers, names, autonames):
+        return self.fn._to_proto(layers, names, autonames)
+
 
 class Function(object):
     """A Function specifies a layer, its parameters, and its inputs (which
@@ -107,19 +108,26 @@ def __init__(self, type_name, inputs, params):
             del self.params['in_place']
         self.tops = tuple(Top(self, n) for n in range(self.ntop))
 
-    def _get_name(self, top, names, autonames):
+    def _get_name(self, names, autonames):
+        if self not in names and self.ntop > 0:
+            names[self] = self._get_top_name(self.tops[0], names, autonames)
+        elif self not in names:
+            autonames[self.type_name] += 1
+            names[self] = self.type_name + str(autonames[self.type_name])
+        return names[self]
+
+    def _get_top_name(self, top, names, autonames):
         if top not in names:
-            n = autonames.setdefault(top.fn.type_name, 1)
             autonames[top.fn.type_name] += 1
-            names[top] = top.fn.type_name + str(n)
+            names[top] = top.fn.type_name + str(autonames[top.fn.type_name])
         return names[top]
 
     def _to_proto(self, layers, names, autonames):
         if self in layers:
             return
         bottom_names = []
         for inp in self.inputs:
-            inp.fn._to_proto(layers, names, autonames)
+            inp._to_proto(layers, names, autonames)
             bottom_names.append(layers[inp.fn].top[inp.n])
         layer = caffe_pb2.LayerParameter()
         layer.type = self.type_name
@@ -129,8 +137,8 @@ def _to_proto(self, layers, names, autonames):
             layer.top.extend(layer.bottom)
         else:
             for top in self.tops:
-                layer.top.append(self._get_name(top, names, autonames))
-        layer.name = self._get_name(self.tops[0], names, autonames)
+                layer.top.append(self._get_top_name(top, names, autonames))
+        layer.name = self._get_name(names, autonames)
 
         for k, v in six.iteritems(self.params):
             # special case to handle generic *params
@@ -163,10 +171,10 @@ def __getattr__(self, name):
 
     def to_proto(self):
         names = {v: k for k, v in six.iteritems(self.tops)}
-        autonames = {}
+        autonames = Counter()
         layers = OrderedDict()
         for name, top in six.iteritems(self.tops):
-            top.fn._to_proto(layers, names, autonames)
+            top._to_proto(layers, names, autonames)
         net = caffe_pb2.NetParameter()
         net.layer.extend(layers.values())
         return net
@@ -180,7 +188,9 @@ class Layers(object):
     def __getattr__(self, name):
         def layer_fn(*args, **kwargs):
             fn = Function(name, args, kwargs)
-            if fn.ntop == 1:
+            if fn.ntop == 0:
+                return fn
+            elif fn.ntop == 1:
                 return fn.tops[0]
             else:
                 return fn.tops

python/caffe/test/test_net_spec.py

@@ -41,6 +41,14 @@ def anon_lenet(batch_size):
     loss = L.SoftmaxWithLoss(ip2, label)
     return loss.to_proto()
 
+def silent_net():
+    n = caffe.NetSpec()
+    n.data, n.data2 = L.DummyData(shape=[dict(dim=[3]), dict(dim=[4, 2])],
+                                  ntop=2)
+    n.silence_data = L.Silence(n.data, ntop=0)
+    n.silence_data2 = L.Silence(n.data2, ntop=0)
+    return n.to_proto()
+
 class TestNetSpec(unittest.TestCase):
     def load_net(self, net_proto):
         f = tempfile.NamedTemporaryFile(delete=False)
@@ -65,3 +73,10 @@ def test_lenet(self):
                 net_proto.layer[6].top)
         net = self.load_net(net_proto)
         self.assertEqual(len(net.layers), 9)
+
+    def test_zero_tops(self):
+        """Test net construction for top-less layers."""
+
+        net_proto = silent_net()
+        net = self.load_net(net_proto)
+        self.assertEqual(len(net.forward()), 0)