"""

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

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 d

def 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':

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_label

def 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':

# Outer double quotes needed or else colon characters don't parse

# properly

node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\

(layer.name,

separator,

layer.type,

separator,

layer.convolution_param.kernel_size,

separator,

layer.convolution_param.stride,

separator,

layer.convolution_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_label

def choose_color_by_layertype(layertype):

"""Define colors for nodes based on the layer type.

"""

color = '#6495ED' # Default

if layertype == 'Convolution':

color = '#FF5050'

elif layertype == 'Pooling':

color = '#FF9900'

elif layertype == 'InnerProduct':

color = '#CC33FF'

return color

def get_pydot_graph(caffe_net, rankdir, label_edges=True):

"""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).

Returns

-------

pydot graph object

"""

pydot_graph = pydot.Dot(caffe_net.name,

graph_type='digraph',

rankdir=rankdir)

pydot_nodes = {}

pydot_edges = []

for layer in caffe_net.layer:

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_graph

def draw_net(caffe_net, rankdir, ext='png'):

"""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').

Returns

-------

string :

Postscript representation of the graph.

"""

return get_pydot_graph(caffe_net, rankdir).create(format=ext)

def draw_net_to_file(caffe_net, filename, rankdir='LR'):

"""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.

"""

ext = filename[filename.rfind('.')+1:]

with open(filename, 'wb') as fid:

fid.write(draw_net(caffe_net, rankdir, ext))