caffe中 cifar10案例（三）模型可视化

1. 准备工作

之前安装caffe时，确认已经执行过如下caffe安装命令：
# cmake -D CPU_ONLY=on -D CMAKE_INSTALL_PREFIX=/usr/local ..
# make all
# make install

安装必要部件
sudo apt-get update
sudo apt-get install python-pip python-dev python-numpy
sudo apt-get install gfortran graphviz
sudo pip install -r ${CAFFE_HOME}/python/erquirements.txt
//此处的{CAFFE_ROOT}为你安装caffe的位置。另外requirements文件中，用到ipython，而ipython6以后不再支持python2.7。用python2.7的朋友，可以把ipython这行写成：ipython==5.4.1

sudo pip install pydot
sudo apt-get install graphviz

2. 执行画图命令

/opt/caffe-master/python$ ./draw_net.py --rankdir BT ../examples/cifar10/cifar10_quick_train_test.prototxt ./cifar10_net.png

报错：

。。。。。。
layer.convolution_param.kernel_size[0] if len(layer.convolution_param.kernel_size._values) else 1,
AttributeError: ‘google.protobuf.pyext._message.RepeatedScalarConta’ object has no attribute

解决：
1. 备份python/caffe/draw.py，并替换成：

"""Caffe network visualization: draw the NetParameter protobuffer... note::    This requires pydot>=1.0.2, which is not included in requirements.txt since    it requires graphviz and other prerequisites outside the scope of the    Caffe."""from caffe.proto import caffe_pb2"""pydot is not supported under python 3 and pydot2 doesn't work properly.pydotplus works nicely (pip install pydotplus)"""try:    # Try to load pydotplus    import pydotplus as pydotexcept ImportError:    import pydot# Internal layer and blob styles.LAYER_STYLE_DEFAULT = {'shape': 'record',                       'fillcolor': '#6495ED',                       'style': 'filled'}NEURON_LAYER_STYLE = {'shape': 'record',                      'fillcolor': '#90EE90',                      'style': 'filled'}BLOB_STYLE = {'shape': 'octagon',              'fillcolor': '#E0E0E0',              'style': 'filled'}def get_pooling_types_dict():    """Get dictionary mapping pooling type number to type name    """    desc = caffe_pb2.PoolingParameter.PoolMethod.DESCRIPTOR    d = {}    for k, v in desc.values_by_name.items():        d[v.number] = k    return ddef get_edge_label(layer):    """Define edge label based on layer type.    """    if layer.type == 'Data':        edge_label = 'Batch ' + str(layer.data_param.batch_size)    elif layer.type == 'Convolution' or layer.type == 'Deconvolution':        edge_label = str(layer.convolution_param.num_output)    elif layer.type == 'InnerProduct':        edge_label = str(layer.inner_product_param.num_output)    else:        edge_label = '""'    return edge_labeldef get_layer_label(layer, rankdir):    """Define node label based on layer type.    Parameters    ----------    layer : ?    rankdir : {'LR', 'TB', 'BT'}        Direction of graph layout.    Returns    -------    string :        A label for the current layer    """    if rankdir in ('TB', 'BT'):        # If graph orientation is vertical, horizontal space is free and        # vertical space is not; separate words with spaces        separator = ' '    else:        # If graph orientation is horizontal, vertical space is free and        # horizontal space is not; separate words with newlines        separator = '\\n'    if layer.type == 'Convolution' or layer.type == 'Deconvolution':        # Outer double quotes needed or else colon characters don't parse        # properly        param = layer.convolution_param        node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\                    (layer.name,                     separator,                     layer.type,                     separator,        layer.pooling_param.kernel_size,                     separator,        layer.pooling_param.stride,                     separator,        layer.pooling_param.pad)    elif layer.type == 'Pooling':        pooling_types_dict = get_pooling_types_dict()        node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\                     (layer.name,                      separator,                      pooling_types_dict[layer.pooling_param.pool],                      layer.type,                      separator,                      layer.pooling_param.kernel_size,                      separator,                      layer.pooling_param.stride,                      separator,                      layer.pooling_param.pad)    else:        node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type)    return node_labeldef choose_color_by_layertype(layertype):    """Define colors for nodes based on the layer type.    """    color = '#6495ED'  # Default    if layertype == 'Convolution' or layertype == 'Deconvolution':        color = '#FF5050'    elif layertype == 'Pooling':        color = '#FF9900'    elif layertype == 'InnerProduct':        color = '#CC33FF'    return colordef get_pydot_graph(caffe_net, rankdir, label_edges=True, phase=None):    """Create a data structure which represents the `caffe_net`.    Parameters    ----------    caffe_net : object    rankdir : {'LR', 'TB', 'BT'}        Direction of graph layout.    label_edges : boolean, optional        Label the edges (default is True).    phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional        Include layers from this network phase.  If None, include all layers.        (the default is None)    Returns    -------    pydot graph object    """    pydot_graph = pydot.Dot(caffe_net.name if caffe_net.name else 'Net',                            graph_type='digraph',                            rankdir=rankdir)    pydot_nodes = {}    pydot_edges = []    for layer in caffe_net.layer:        if phase is not None:          included = False          if len(layer.include) == 0:            included = True          if len(layer.include) > 0 and len(layer.exclude) > 0:            raise ValueError('layer ' + layer.name + ' has both include '                             'and exclude specified.')          for layer_phase in layer.include:            included = included or layer_phase.phase == phase          for layer_phase in layer.exclude:            included = included and not layer_phase.phase == phase          if not included:            continue        node_label = get_layer_label(layer, rankdir)        node_name = "%s_%s" % (layer.name, layer.type)        if (len(layer.bottom) == 1 and len(layer.top) == 1 and           layer.bottom[0] == layer.top[0]):            # We have an in-place neuron layer.            pydot_nodes[node_name] = pydot.Node(node_label,                                                **NEURON_LAYER_STYLE)        else:            layer_style = LAYER_STYLE_DEFAULT            layer_style['fillcolor'] = choose_color_by_layertype(layer.type)            pydot_nodes[node_name] = pydot.Node(node_label, **layer_style)        for bottom_blob in layer.bottom:            pydot_nodes[bottom_blob + '_blob'] = pydot.Node('%s' % bottom_blob,                                                            **BLOB_STYLE)            edge_label = '""'            pydot_edges.append({'src': bottom_blob + '_blob',                                'dst': node_name,                                'label': edge_label})        for top_blob in layer.top:            pydot_nodes[top_blob + '_blob'] = pydot.Node('%s' % (top_blob))            if label_edges:                edge_label = get_edge_label(layer)            else:                edge_label = '""'            pydot_edges.append({'src': node_name,                                'dst': top_blob + '_blob',                                'label': edge_label})    # Now, add the nodes and edges to the graph.    for node in pydot_nodes.values():        pydot_graph.add_node(node)    for edge in pydot_edges:        pydot_graph.add_edge(            pydot.Edge(pydot_nodes[edge['src']],                       pydot_nodes[edge['dst']],                       label=edge['label']))    return pydot_graphdef draw_net(caffe_net, rankdir, ext='png', phase=None):    """Draws a caffe net and returns the image string encoded using the given    extension.    Parameters    ----------    caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.    ext : string, optional        The image extension (the default is 'png').    phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional        Include layers from this network phase.  If None, include all layers.        (the default is None)    Returns    -------    string :        Postscript representation of the graph.    """    return get_pydot_graph(caffe_net, rankdir, phase=phase).create(format=ext)def draw_net_to_file(caffe_net, filename, rankdir='LR', phase=None):    """Draws a caffe net, and saves it to file using the format given as the    file extension. Use '.raw' to output raw text that you can manually feed    to graphviz to draw graphs.    Parameters    ----------    caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.    filename : string        The path to a file where the networks visualization will be stored.    rankdir : {'LR', 'TB', 'BT'}        Direction of graph layout.    phase : {caffe_pb2.Phase.TRAIN, caffe_pb2.Phase.TEST, None} optional        Include layers from this network phase.  If None, include all layers.        (the default is None)    """    ext = filename[filename.rfind('.')+1:]    with open(filename, 'wb') as fid:        fid.write(draw_net(caffe_net, rankdir, ext, phase))

1. 在python/caffe/test/ 增加文件 test_draw.py

import osimport unittestfrom google import protobufimport caffe.drawfrom caffe.proto import caffe_pb2def getFilenames():    """Yields files in the source tree which are Net prototxts."""    result = []    root_dir = os.path.abspath(os.path.join(        os.path.dirname(__file__), '..', '..', '..'))    assert os.path.exists(root_dir)    for dirname in ('models', 'examples'):        dirname = os.path.join(root_dir, dirname)        assert os.path.exists(dirname)        for cwd, _, filenames in os.walk(dirname):            for filename in filenames:                filename = os.path.join(cwd, filename)                if filename.endswith('.prototxt') and 'solver' not in filename:                    yield os.path.join(dirname, filename)class TestDraw(unittest.TestCase):    def test_draw_net(self):        for filename in getFilenames():            net = caffe_pb2.NetParameter()            with open(filename) as infile:                protobuf.text_format.Merge(infile.read(), net)            caffe.draw.draw_net(net, 'LR')

得到结果：

这个图我画反了，如果正过来，把参数BT，改成TB就好了。

然 并 卵
再介绍一种办法，瞬间秒杀上面的手工画图！！！！！！

进入网站http://ethereon.github.io/netscope/#/editor

把训练好的模型结构放到左面，瞬间高大上的感觉，有没有！！！