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Python files are cleaned #5

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34 changes: 16 additions & 18 deletions onnxruntime/core/providers/openvino/openvino_mo/openvino_emitter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -56,7 +56,7 @@ def create_const_nodes(graph: nx.MultiDiGraph, start_data_nodes_are_not_allowed:
[(copy_data_node_name, const_node_name, {'in': 0, 'bin': 'custom'})])
elif start_data_nodes_are_not_allowed:
log.debug('node = {}'.format(node.graph.node[node.id]))
# TODO for body sub-graph it shouldn't be reported as an error
#TODO for body sub - graph it shouldn't be reported as an error
raise Error(
'Discovered data node without inputs and value, node.name = {}, consumer.name = {}. ' +
refer_to_faq_msg(23),
Expand Down Expand Up @@ -119,7 +119,7 @@ def serialize_constants_recursively(weights, graph: nx.MultiDiGraph, data_type,
node = NodeWrap(graph, node)
else:
node = Node(graph, node)
# Dump blobs recursively if sub-graphs are present in the node
#Dump blobs recursively if sub - graphs are present in the node
if node.has_valid('sub_graphs'):
for sub_graph_attr_name in node.sub_graphs:
sub_graph = node[sub_graph_attr_name]
Expand Down Expand Up @@ -148,8 +148,8 @@ def xml_shape(shape: np.ndarray, element: xml.etree.ElementTree.Element):
dim = SubElement(element, 'dim')
if d <= 0:
d = 1
# raise Error('The value "{}" for shape is less or equal to 0. May be the input shape of the topology is '
# 'wrong.'.format(d))
#raise Error('The value "{}" for shape is less or equal to 0. May be the input shape of the topology is '
#'wrong.'.format(d))
if int(d) != d:
raise Error('The value "{}" for shape is not integer.'.format(d))
if not isinstance(d, np.int64):
Expand All @@ -174,7 +174,7 @@ def sorted_outputs(node):


def xml_ports(node: Node, element: xml.etree.ElementTree.Element, edges: xml.etree.ElementTree.Element):
# input ports
#input ports
inputs = None # will create input section only if at least one input is available
for u, d in sorted_inputs(node):
if 'bin' not in d and ('xml_skip' not in d or not d['xml_skip']):
Expand All @@ -185,7 +185,7 @@ def xml_ports(node: Node, element: xml.etree.ElementTree.Element, edges: xml.etr
assert node.graph.node[u]['shape'] is not None, 'Input shape is not calculated properly for node {}'.format(
node.id)
xml_shape(node.graph.node[u]['shape'], port)
# u is a data node that has a single producer, let's find it
#u is a data node that has a single producer, let's find it
assert (node.graph.node[u]['kind'] == 'data')
in_nodes = list(node.graph.in_edges(u, data=True))
assert (len(in_nodes) <= 1)
Expand All @@ -197,7 +197,7 @@ def xml_ports(node: Node, element: xml.etree.ElementTree.Element, edges: xml.etr
edge.set('to-layer', str(node.node))
edge.set('to-port', str(d['in']))

# output ports
#output ports
outputs = None
for v, d in sorted_outputs(node):
if 'xml_skip' not in d or not d['xml_skip']:
Expand Down Expand Up @@ -245,7 +245,7 @@ def serialize_element(
if isinstance(attr, tuple):
key = attr[0]
try:
if callable(attr[1]):
if callable(attr[1]):
value = attr[1](node)
else:
value = node[attr[1]] if attr[1] in node else None
Expand Down Expand Up @@ -296,13 +296,11 @@ def serialize_node_attributes(
unsupported):

try:
for s in schema:
if not isinstance(s, tuple):
if s == '@ports':
try:
# TODO make sure that edges are generated regardless of the existence of @ports
xml_ports(node, parent_element, edges)
except Exception as e:
for
s in schema : if not isinstance(s, tuple) : if s == '@ports' : try :
#TODO make sure that edges are generated regardless of the existence of @ports
xml_ports(node, parent_element, edges)
except Exception as e:
raise Error(('Unable to create ports for node with id {}. ' +
refer_to_faq_msg(3)).format(node.id)) from e
elif s == '@consts':
Expand Down Expand Up @@ -331,9 +329,9 @@ def serialize_node_attributes(
def create_pre_process_block_for_image(net: xml.etree.ElementTree.Element, ref_layer_names: list, mean_offset: tuple,
mean_size: tuple):
pre_process = SubElement(net, 'pre-process')
# TODO: to think about need to output FP16 mean values
#TODO : to think about need to output FP16 mean values
pre_process.set('mean-precision', 'FP32')
# TODO: extend it for several inputs
#TODO : extend it for several inputs
pre_process.set('reference-layer-name', ref_layer_names[0])
for idx in range(len(mean_size)):
channel_xml = SubElement(pre_process, 'channel')
Expand Down Expand Up @@ -402,7 +400,7 @@ def generate_ie_ir(graph: nx.MultiDiGraph, file_name: str, input_names: tuple =
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?)
#TODO substitute real batches here(is it a number or is it an index ?)
net.set('batch', '1')

if mean_size or mean_offset:
Expand Down
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