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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()
def silent_net():
n = caffe.NetSpec()
n.data, n.data2 = L.DummyData(shape=dict(dim=3), 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(mode='w+', 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)
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)
def test_type_error(self):
"""Test that a TypeError is raised when a Function input isn't a Top."""
data = L.DummyData(ntop=2) # data is a 2-tuple of Tops
r = r"^Silence input 0 is not a Top \(type is <(type|class) 'tuple'>\)$"
with self.assertRaisesRegexp(TypeError, r):
L.Silence(data, ntop=0) # should raise: data is a tuple, not a Top
L.Silence(*data, ntop=0) # shouldn't raise: each elt of data is a Top
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