[pytest] minimal testing of net specification

python/caffe/test/test_net_spec.py

@@ -0,0 +1,67 @@
+import unittest
+import tempfile
+import caffe
+from caffe import layers as L
+from caffe import params as P
+
+def lenet(batch_size):
+    n = caffe.NetSpec()
+    n.data, n.label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
+                                         dict(dim=[batch_size, 1, 1, 1])],
+                                  transform_param=dict(scale=1./255), ntop=2)
+    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20,
+        weight_filler=dict(type='xavier'))
+    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50,
+        weight_filler=dict(type='xavier'))
+    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    n.ip1 = L.InnerProduct(n.pool2, num_output=500,
+        weight_filler=dict(type='xavier'))
+    n.relu1 = L.ReLU(n.ip1, in_place=True)
+    n.ip2 = L.InnerProduct(n.relu1, num_output=10,
+        weight_filler=dict(type='xavier'))
+    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)
+    return n.to_proto()
+
+def anon_lenet(batch_size):
+    data, label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
+                                     dict(dim=[batch_size, 1, 1, 1])],
+                              transform_param=dict(scale=1./255), ntop=2)
+    conv1 = L.Convolution(data, kernel_size=5, num_output=20,
+        weight_filler=dict(type='xavier'))
+    pool1 = L.Pooling(conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    conv2 = L.Convolution(pool1, kernel_size=5, num_output=50,
+        weight_filler=dict(type='xavier'))
+    pool2 = L.Pooling(conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    ip1 = L.InnerProduct(pool2, num_output=500,
+        weight_filler=dict(type='xavier'))
+    relu1 = L.ReLU(ip1, in_place=True)
+    ip2 = L.InnerProduct(relu1, num_output=10,
+        weight_filler=dict(type='xavier'))
+    loss = L.SoftmaxWithLoss(ip2, label)
+    return loss.to_proto()
+
+class TestNetSpec(unittest.TestCase):
+    def load_net(self, net_proto):
+        f = tempfile.NamedTemporaryFile(delete=False)
+        f.write(str(net_proto))
+        f.close()
+        return caffe.Net(f.name, caffe.TEST)
+
+    def test_lenet(self):
+        """Construct and build the Caffe version of LeNet."""
+
+        net_proto = lenet(50)
+        # check that relu is in-place
+        self.assertEqual(net_proto.layer[6].bottom,
+                net_proto.layer[6].top)
+        net = self.load_net(net_proto)
+        # check that all layers are present
+        self.assertEqual(len(net.layers), 9)
+
+        # now the check the version with automatically-generated layer names
+        net_proto = anon_lenet(50)
+        self.assertEqual(net_proto.layer[6].bottom,
+                net_proto.layer[6].top)
+        net = self.load_net(net_proto)
+        self.assertEqual(len(net.layers), 9)