Python net specification

examples/pycaffe/caffenet.py

@@ -0,0 +1,54 @@
+from caffe import layers as L, params as P, to_proto
+from caffe.proto import caffe_pb2
+
+# helper function for common structures
+
+def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
+    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
+                                num_output=nout, pad=pad, group=group)
+    return conv, L.ReLU(conv, in_place=True)
+
+def fc_relu(bottom, nout):
+    fc = L.InnerProduct(bottom, num_output=nout)
+    return fc, L.ReLU(fc, in_place=True)
+
+def max_pool(bottom, ks, stride=1):
+    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
+
+def caffenet(lmdb, batch_size=256, include_acc=False):
+    data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
+        transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=True))
+
+    # the net itself
+    conv1, relu1 = conv_relu(data, 11, 96, stride=4)
+    pool1 = max_pool(relu1, 3, stride=2)
+    norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75)
+    conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2)
+    pool2 = max_pool(relu2, 3, stride=2)
+    norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75)
+    conv3, relu3 = conv_relu(norm2, 3, 384, pad=1)
+    conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2)
+    conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2)
+    pool5 = max_pool(relu5, 3, stride=2)
+    fc6, relu6 = fc_relu(pool5, 4096)
+    drop6 = L.Dropout(relu6, in_place=True)
+    fc7, relu7 = fc_relu(drop6, 4096)
+    drop7 = L.Dropout(relu7, in_place=True)
+    fc8 = L.InnerProduct(drop7, num_output=1000)
+    loss = L.SoftmaxWithLoss(fc8, label)
+
+    if include_acc:
+        acc = L.Accuracy(fc8, label)
+        return to_proto(loss, acc)
+    else:
+        return to_proto(loss)
+
+def make_net():
+    with open('train.prototxt', 'w') as f:
+        print >>f, caffenet('/path/to/caffe-train-lmdb')
+
+    with open('test.prototxt', 'w') as f:
+        print >>f, caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True)
+
+if __name__ == '__main__':
+    make_net()

python/caffe/__init__.py

@@ -4,3 +4,4 @@
 from .classifier import Classifier
 from .detector import Detector
 from . import io
+from .net_spec import layers, params, NetSpec, to_proto

python/caffe/net_spec.py

@@ -0,0 +1,203 @@
+"""Python net specification.
+
+This module provides a way to write nets directly in Python, using a natural,
+functional style. See examples/python_nets/caffenet.py for an example.
+
+Currently this works as a thin wrapper around the Python protobuf interface,
+with layers and parameters automatically generated for the "layers" and
+"params" pseudo-modules, which are actually objects using __getattr__ magic
+to generate protobuf messages.
+
+Note that when using to_proto or Top.to_proto, names of intermediate blobs will
+be automatically generated. To explicitly specify blob names, use the NetSpec
+class -- assign to its attributes directly to name layers, and call
+NetSpec.to_proto to serialize all assigned layers.
+
+This interface is expected to continue to evolve as Caffe gains new capabilities
+for specifying nets. In particular, the automatically generated layer names
+are not guaranteed to be forward-compatible.
+"""
+
+from collections import OrderedDict
+
+from .proto import caffe_pb2
+from google import protobuf
+
+
+def param_name_dict():
+    """Find out the correspondence between layer names and parameter names."""
+
+    layer = caffe_pb2.LayerParameter()
+    # get all parameter names (typically underscore case) and corresponding
+    # type names (typically camel case), which contain the layer names
+    # (note that not all parameters correspond to layers, but we'll ignore that)
+    param_names = [s for s in dir(layer) if s.endswith('_param')]
+    param_type_names = [type(getattr(layer, s)).__name__ for s in param_names]
+    # strip the final '_param' or 'Parameter'
+    param_names = [s[:-len('_param')] for s in param_names]
+    param_type_names = [s[:-len('Parameter')] for s in param_type_names]
+    return dict(zip(param_type_names, param_names))
+
+
+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 = {}
+    for top in tops:
+        top.fn._to_proto(layers, {}, autonames)
+    net = caffe_pb2.NetParameter()
+    net.layer.extend(layers.values())
+    return net
+
+
+def assign_proto(proto, name, val):
+    """Assign a Python object to a protobuf message, based on the Python
+    type (in recursive fashion). Lists become repeated fields/messages, dicts
+    become messages, and other types are assigned directly."""
+
+    if isinstance(val, list):
+        if isinstance(val[0], dict):
+            for item in val:
+                proto_item = getattr(proto, name).add()
+                for k, v in item.iteritems():
+                    assign_proto(proto_item, k, v)
+        else:
+            getattr(proto, name).extend(val)
+    elif isinstance(val, dict):
+        for k, v in val.iteritems():
+            assign_proto(getattr(proto, name), k, v)
+    else:
+        setattr(proto, name, val)
+
+
+class Top(object):
+    """A Top specifies a single output blob (which could be one of several
+    produced by a layer.)"""
+
+    def __init__(self, fn, n):
+        self.fn = fn
+        self.n = n
+
+    def to_proto(self):
+        """Generate a NetParameter that contains all layers needed to compute
+        this top."""
+
+        return to_proto(self)
+
+
+class Function(object):
+    """A Function specifies a layer, its parameters, and its inputs (which
+    are Tops from other layers)."""
+
+    def __init__(self, type_name, inputs, params):
+        self.type_name = type_name
+        self.inputs = inputs
+        self.params = params
+        self.ntop = self.params.get('ntop', 1)
+        # use del to make sure kwargs are not double-processed as layer params
+        if 'ntop' in self.params:
+            del self.params['ntop']
+        self.in_place = self.params.get('in_place', False)
+        if 'in_place' in self.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):
+        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)
+        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)
+            bottom_names.append(layers[inp.fn].top[inp.n])
+        layer = caffe_pb2.LayerParameter()
+        layer.type = self.type_name
+        layer.bottom.extend(bottom_names)
+
+        if self.in_place:
+            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)
+
+        for k, v in self.params.iteritems():
+            # special case to handle generic *params
+            if k.endswith('param'):
+                assign_proto(layer, k, v)
+            else:
+                try:
+                    assign_proto(getattr(layer,
+                        _param_names[self.type_name] + '_param'), k, v)
+                except (AttributeError, KeyError):
+                    assign_proto(layer, k, v)
+
+        layers[self] = layer
+
+
+class NetSpec(object):
+    """A NetSpec contains a set of Tops (assigned directly as attributes).
+    Calling NetSpec.to_proto generates a NetParameter containing all of the
+    layers needed to produce all of the assigned Tops, using the assigned
+    names."""
+
+    def __init__(self):
+        super(NetSpec, self).__setattr__('tops', OrderedDict())
+
+    def __setattr__(self, name, value):
+        self.tops[name] = value
+
+    def __getattr__(self, name):
+        return self.tops[name]
+
+    def to_proto(self):
+        names = {v: k for k, v in self.tops.iteritems()}
+        autonames = {}
+        layers = OrderedDict()
+        for name, top in self.tops.iteritems():
+            top.fn._to_proto(layers, names, autonames)
+        net = caffe_pb2.NetParameter()
+        net.layer.extend(layers.values())
+        return net
+
+
+class Layers(object):
+    """A Layers object is a pseudo-module which generates functions that specify
+    layers; e.g., Layers().Convolution(bottom, kernel_size=3) will produce a Top
+    specifying a 3x3 convolution applied to bottom."""
+
+    def __getattr__(self, name):
+        def layer_fn(*args, **kwargs):
+            fn = Function(name, args, kwargs)
+            if fn.ntop == 1:
+                return fn.tops[0]
+            else:
+                return fn.tops
+        return layer_fn
+
+
+class Parameters(object):
+    """A Parameters object is a pseudo-module which generates constants used
+    in layer parameters; e.g., Parameters().Pooling.MAX is the value used
+    to specify max pooling."""
+
+    def __getattr__(self, name):
+       class Param:
+            def __getattr__(self, param_name):
+                return getattr(getattr(caffe_pb2, name + 'Parameter'), param_name)
+       return Param()
+
+
+_param_names = param_name_dict()
+layers = Layers()
+params = Parameters()

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)