""" 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 pydot except 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 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' 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_label def get_layer_lr_mult(layer): """Get the learning rate multipliers. Get the learning rate multipliers for the given layer. Assumes a Convolution/Deconvolution/InnerProduct layer. Parameters ---------- layer : caffe_pb2.LayerParameter A Convolution, Deconvolution, or InnerProduct layer. Returns ------- learning_rates : tuple of floats the learning rate multipliers for the weights and biases. """ if layer.type not in ['Convolution', 'Deconvolution', 'InnerProduct']: raise ValueError("%s layers do not have a " "learning rate multiplier" % layer.type) if not hasattr(layer, 'param'): return (1.0, 1.0) params = getattr(layer, 'param') if len(params) == 0: return (1.0, 1.0) if len(params) == 1: lrm0 = getattr(params[0],'lr_mult', 1.0) return (lrm0, 1.0) if len(params) == 2: lrm0, lrm1 = [getattr(p,'lr_mult', 1.0) for p in params] return (lrm0, lrm1) raise ValueError("Could not parse the learning rate multiplier") def get_layer_label(layer, rankdir, display_lrm=False): """Define node label based on layer type. Parameters ---------- layer : caffe_pb2.LayerParameter rankdir : {'LR', 'TB', 'BT'} Direction of graph layout. display_lrm : boolean, optional If True include the learning rate multipliers in the label (default is False). Returns ------- node_label : 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 = r'\n' # Initializes a list of descriptors that will be concatenated into the # `node_label` descriptors_list = [] # Add the layer's name descriptors_list.append(layer.name) # Add layer's type if layer.type == 'Pooling': pooling_types_dict = get_pooling_types_dict() layer_type = '(%s %s)' % (layer.type, pooling_types_dict[layer.pooling_param.pool]) else: layer_type = '(%s)' % layer.type descriptors_list.append(layer_type) # Describe parameters for spatial operation layers if layer.type in ['Convolution', 'Deconvolution', 'Pooling']: if layer.type == 'Pooling': kernel_size = layer.pooling_param.kernel_size stride = layer.pooling_param.stride padding = layer.pooling_param.pad else: kernel_size = layer.convolution_param.kernel_size[0] if \ len(layer.convolution_param.kernel_size) else 1 stride = layer.convolution_param.stride[0] if \ len(layer.convolution_param.stride) else 1 padding = layer.convolution_param.pad[0] if \ len(layer.convolution_param.pad) else 0 spatial_descriptor = separator.join([ "kernel size: %d" % kernel_size, "stride: %d" % stride, "pad: %d" % padding, ]) descriptors_list.append(spatial_descriptor) # Add LR multiplier for learning layers if display_lrm and layer.type in ['Convolution', 'Deconvolution', 'InnerProduct']: lrm0, lrm1 = get_layer_lr_mult(layer) if any([lrm0, lrm1]): lr_mult = "lr mult: %.1f, %.1f" % (lrm0, lrm1) descriptors_list.append(lr_mult) # Concatenate the descriptors into one label node_label = separator.join(descriptors_list) # Outer double quotes needed or else colon characters don't parse # properly node_label = '"%s"' % node_label return node_label def 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 color def get_pydot_graph(caffe_net, rankdir, label_edges=True, phase=None, display_lrm=False): """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) display_lrm : boolean, optional If True display the learning rate multipliers when relevant (default is False). 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, display_lrm=display_lrm) 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', phase=None, display_lrm=False): """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) display_lrm : boolean, optional If True display the learning rate multipliers for the learning layers (default is False). Returns ------- string : Postscript representation of the graph. """ return get_pydot_graph(caffe_net, rankdir, phase=phase, display_lrm=display_lrm).create(format=ext) def draw_net_to_file(caffe_net, filename, rankdir='LR', phase=None, display_lrm=False): """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) display_lrm : boolean, optional If True display the learning rate multipliers for the learning layers (default is False). """ ext = filename[filename.rfind('.')+1:] with open(filename, 'wb') as fid: fid.write(draw_net(caffe_net, rankdir, ext, phase, display_lrm))