def find_and_replace_pattern(self, graph: Graph):
add_output_ops(graph,
graph.graph['packed_outputs'],
inputs=graph.graph['user_shapes'])
# For keeping tensor names information for output nodes fake outputs are added
# to graph during the model loading. In the following code fake outputs are removed
# and tensor names information is moved to output->Result edge.
for node in graph.get_op_nodes(needs_removal=True):
fw_info = None
in_node = None
for in_port_idx in node.in_edges():
node_idx = node.in_edge(in_port_idx)['in']
if node_idx in node.in_nodes():
in_node = node.in_node(node_idx)
fw_info_value = get_edge_attribute_between_nodes(
in_node, node, 'fw_tensor_debug_info')
if fw_info_value:
fw_info = fw_info_value
break
graph.erase_node(node)
if fw_info is not None and in_node is not None:
for out_idx in in_node.out_nodes():
set_edge_attribute_between_nodes(in_node,
in_node.out_node(out_idx),
'fw_tensor_debug_info',
fw_info)
def replace_sub_graph(self, graph: Graph, match: dict):
"""
Need to find each occurrence of pattern: _contrib_MultiBoxPrior -> Flatten
remove Flatten layer - IE does not expect outputs to be flattened
Parameters
----------
graph : Graph
Graph with loaded model.
match : dict
Patterns which were found in graph structure.
"""
graph.erase_node(match['flatten'])
def replace_sub_graph(graph: Graph, match: dict, **kwargs):
r"""
Usually graph looks like:
main_graph
... Result
| |
image_batch label_batch
\ /
batch_join
/ \
placeholder fifo_queue
Replacer works for both cases (that's why we have loop - 68 line):
label_batch was marked as output
there is no label_batch node
"""
true_placeholder_shape = match['placeholder'].shape
placeholder_shape = match['fifo_queue'].shapes[0]
placeholder_data_type = match['fifo_queue'].types[0]
# in case OOB conversion batch_size placeholder shape is not required
# so use a shape specified in FIFOQueueV2 shapes list attribute
assert true_placeholder_shape is None or true_placeholder_shape.ndim <= 1
if true_placeholder_shape is not None and true_placeholder_shape.ndim == 1 and len(
true_placeholder_shape) > 1:
log.warning(
'Placeholder \'{}\' got non 0-dimensional shape {} in FIFOQueue pattern. Placeholder will have the '
'same shape after folding the pattern instead of {} shape which is original for the network.'
''.format(match['placeholder'].id, true_placeholder_shape,
placeholder_shape))
placeholder_shape = true_placeholder_shape
placeholder_name = match['fifo_queue'].name
graph.erase_node(match['fifo_queue'])
graph.erase_node(match['placeholder'])
for _, out in match['batch_join'].out_nodes().items():
if out.id != match['image_batch'].id:
if out.out_node().op == 'Result':
graph.remove_node(out.out_node().id)
graph.remove_node(out.id)
graph.remove_node(match['batch_join'].id)
placeholder = Parameter(
graph, {
'name': placeholder_name,
'shape': placeholder_shape,
'data_type': placeholder_data_type
}).create_node()
graph.create_edge(placeholder, match['image_batch'])
log.info(
"FIFOQueueV2 pattern was detected. New shape of placeholder {} is {}. Use -b to set batch size if "
"needed".format(placeholder.id, placeholder['shape']))
def replace_sub_graph(self, graph: Graph, match: dict):
ph = match['placeholder']
if ph.name in graph.graph['freeze_placeholder']:
name = ph.name
if ph.has_and_set('data_type'):
data_type = ph.data_type
else:
data_type = SUPPORTED_DATA_TYPES[
graph.graph['cmd_params'].data_type][0]
string_value = graph.graph['freeze_placeholder'][name]
try:
if data_type != np.bool:
value = mo_array(string_value, dtype=data_type)
# TODO: investigate why boolean type is allowed only for TensorFlow
elif data_type == np.bool and graph.graph['fw'] == 'tf':
from openvino.tools.mo.front.tf.common import tf_data_type_cast
if isinstance(string_value, list):
casted_list = list()
for v in mo_array(string_value):
casted_list.append(
tf_data_type_cast[ph.data_type](v))
value = mo_array(string_value, dtype=data_type)
else:
value = tf_data_type_cast[ph.data_type](string_value)
else:
raise Error("Cannot cast value {} to {} data_type".format(
string_value, data_type))
except:
raise Error("Cannot cast value {} to {} data_type".format(
string_value, data_type))
try:
value = np.reshape(a=value, newshape=ph.shape)
except:
raise Error("Can not reshape value {} to shape {}".format(
value, ph.shape))
out_edges = list(graph.out_edges(ph.id, data=True))
new_node = Const(graph).create_node(
attrs={
'value': value,
'data_type': type(value),
'name': name + '/const_placeholder',
'shape': ph.shape
})
graph.erase_node(ph)
graph.add_edges_from([(new_node.id, v, attrs)
for u, v, attrs in out_edges])
log.info(
"Placeholder node \"{}\" was replaced with Const node \"{}\" with value \"{}\""
.format(name, new_node.name, value))
def replace_pattern(graph: Graph, match: dict):
node = match['result']
if len(node.in_nodes()) == 0:
graph.erase_node(node)
def replace_sub_graph(graph: Graph, match: dict):
graph.erase_node(match['output'])
graph.erase_node(match['noop'])
log.info("NoOp node \"{}\" was removed from the graph".format(
match['noop'].id))
