def generate_ie_ir(graph: Graph,
file_name: str,
input_names: tuple = (),
mean_offset: tuple = (),
mean_size: tuple = (),
meta_info: dict = dict()):
"""
Extracts IE/IR attributes from kind='op' nodes in three ways:
(1) node.IE xml scheme that set correspondance from existing attributes to generated xml elements
(2) input/output edges that don't have 'bin' attributes are transformed to input/output ports
(3) input edges that has 'bin' attributes are handled in special way like weights/biases
Args:
graph: nx graph with FW-independent model
file_name: name of the resulting IR
input_names: names of input layers of the topology to add mean file to
input_name: name of the layer which is referenced from pre-processing block if any
mean_values: tuple of mean values for channels in RGB order
scale_values: tuple of mean values for channels in RGB order
mean_offset: offset in binary file, where mean file values start
mean_size: size of the mean file
"""
net = Element('net')
net.set('name', graph.name)
net.set('version', str((graph.graph['ir_version'])))
net.set(
'batch', '1'
) # TODO substitute real batches here (is it a number or is it an index?)
if mean_size or mean_offset:
create_pre_process_block_for_image(net, input_names, mean_offset,
mean_size)
if 'mean_values' in graph.graph.keys():
for input_name, values in graph.graph['mean_values'].items():
create_pre_process_block(net, input_name, values)
unsupported = UnsupportedOps(graph)
serialize_network(graph, net, unsupported)
add_quantization_statistics(graph, net)
add_meta_data(net, meta_info)
xml_string = tostring(net)
xml_doc = parseString(xml_string)
pretty_xml_as_string = xml_doc.toprettyxml()
if len(unsupported.unsupported):
log.debug('Partially correct IR XML:\n{}'.format(pretty_xml_as_string))
unsupported.report(
log.error,
"List of operations that cannot be converted to Inference Engine IR:"
)
raise Error('Part of the nodes was not converted to IR. Stopped. ' +
refer_to_faq_msg(24))
with open(file_name, 'wb') as file:
file.write(bytes(pretty_xml_as_string, "UTF-8"))
def generate_ie_ir(graph: nx.MultiDiGraph,
file_name: str,
input_names: tuple = (),
mean_offset: tuple = (),
mean_size: tuple = (),
meta_info: dict = dict()):
net = Element('net')
net.set('name', graph.name)
net.set('version', str((graph.graph['ir_version'])))
# TODO substitute real batches here (is it a number or is it an index?)
net.set('batch', '1')
if mean_size or mean_offset:
create_pre_process_block_for_image(net, input_names, mean_offset,
mean_size)
if 'mean_values' in graph.graph.keys():
for input_name, values in graph.graph['mean_values'].items():
create_pre_process_block(net, input_name, values)
unsupported = UnsupportedOps(graph)
serialize_network(graph, net, unsupported)
add_meta_data(net, meta_info)
xml_string = tostring(net)
xml_doc = xml.dom.minidom.parseString(xml_string) # ugly?
pretty_xml_as_string = xml_doc.toprettyxml()
if len(unsupported.unsupported):
log.debug('Partially correct IR XML:\n{}'.format(pretty_xml_as_string))
raise Error('Part of the nodes was not translated to IE. Stopped. ' +
refer_to_faq_msg(24))
return xml_string
