def replace_identityN(node: Node):
graph = node.graph
name = node.soft_get('name', node.id)
assert node.has_valid(
'data_types'), 'IdentityN {} has no `data_types` attribute'.format(
name)
dtypes = node.data_types
for idx, port in node.in_ports().items():
if not node.is_in_port_connected(
idx) or not node.is_out_port_connected(idx):
# ATTENTION section in the description above
continue
assert idx < len(
dtypes
), 'IdentityN {} has inconsistent `data_types` attribute {}'.format(
name, dtypes)
identity = Identity(graph, {
'name': '{}/{}_port'.format(name, idx),
'data_type': dtypes[idx]
}).create_node()
port.get_connection().set_destination(identity.in_port(0))
node.out_port(idx).get_connection().set_source(
identity.out_port(0))
# ATTENTION section in the description above
for in_port in node.in_ports().values():
in_port.disconnect()
for out_port in node.out_ports().values():
out_port.disconnect()
def replace_sub_graph(self, graph: Graph, match: dict):
node = match['op']
identity = Identity(graph, {'name': node.soft_get('name', node.id)}).create_node()
node.in_port(0).get_connection().set_destination(identity.in_port(0))
for idx, port in node.out_ports().items():
port.get_connection().set_source(identity.out_port(0))
def extract(cls, node):
# some Dropout flavors doesn't have is_test attribute; when it is missing, interpret it as 1
is_test = onnx_attr(node, 'is_test', 'i', 1)
if len(node.out_nodes()) > 1:
raise Error('Dropout node {} has more than one consumer. Unsupported.', node.name)
if not is_test:
raise Error('Dropout node {} has is_test: 0. This means training mode which is not supported.', node.name)
Identity.update_node_stat(node)
return cls.enabled
def extract(cls, node):
pb = node.parameters
collect_until_token(pb, b'<Dim>')
dim = read_binary_integer32_token(pb)
collect_until_token(pb, b'<BlockDim>')
block_dim = read_binary_integer32_token(pb)
collect_until_token(pb, b'<TimePeriod>')
time_period = read_binary_integer32_token(pb)
collect_until_token(pb, b'<DropoutProportion>')
dropout_proporion = read_binary_float_token(pb)
# collect_until_token(pb, b'<Continuous>')
Identity.update_node_stat(node, {})
return cls.enabled
def extract(cls, node):
Identity.update_node_stat(node)
return cls.enabled
def replace_pattern(graph: Graph, match: dict):
log.debug('================== SimpleConditionFind ===============')
# init_1
init_1 = match['init_1_data'].value
assert init_1 is not None
init_1 = int(init_1)
# step_1
assert match['add_1_y_data'].value is not None
step_1 = int(match['add_1_y_data'].value)
match['loop_cond_data'].value = None
# compute destination (or consumer) ports for time node
identity_node_name = match['Identity_1'].soft_get(
'name', match['Identity_1'].id)
time_dsts = match['Identity_1'].out_port(0).get_destinations()
# Create condition node and delete all useless nodes from condition pattern
condition_attrs = dict(iter=dict(init=init_1, step=step_1),
name=match['loop_cond'].name +
'/TensorIteratorCondition_')
condition = TensorIteratorCondition(graph, attrs=condition_attrs)
condition.create_node_with_data(
inputs=[match['Strided_slice_data']],
data_nodes=[match['loop_cond_data'], match['Identity_1_data']])
safe_nodes = [
'loop_cond_data', 'Identity_1_data', 'Strided_slice',
'Strided_slice_data'
]
# check if time node has other consumers different from increment node,
# input slicing and output concatenation nodes
other_time_consumers = False
for time_consumer in time_dsts:
if time_consumer.node.soft_get('op') not in ['TensorIteratorInput', 'TensorIteratorOutput'] and \
time_consumer.node.id != match['add_1'].id:
other_time_consumers = True
break
if other_time_consumers:
# save time related nodes since they have other consumers different from
# input slicing and output concatenation nodes
safe_nodes += [
'init_1', 'init_1_data', 'Enter_1', 'Enter_1_data', 'Merge_1',
'Merge_1_data', 'Switch_1', 'Switch_1_data', 'add_1',
'add_1_y', 'add_1_y_data', 'add_1_data', 'NextIteration_1'
]
switch_node = match['Switch_1']
new_identity_node = Identity(
graph, dict(name=identity_node_name)).create_node()
switch_node.out_port(1).connect(new_identity_node.in_port(0))
# make the graph consistent to avoid multiple producers by the same input port
graph.remove_nodes_from([match['Identity_1'].id])
rename_nodes([(new_identity_node, identity_node_name)])
for time_consumer in time_dsts:
if time_consumer.node.soft_get('op') not in [
'TensorIteratorInput', 'TensorIteratorOutput'
]:
time_consumer.get_connection().set_source(
new_identity_node.out_port(0))
# Delete useless nodes
nodes_for_remove = []
for node in match.keys():
if node not in safe_nodes:
nodes_for_remove.append(match[node].id)
graph.remove_nodes_from(nodes_for_remove)
def extract(cls, node: Node):
Identity.update_node_stat(node, {'op': 'StopGradient'})
return cls.enabled
def extract(cls, node: Node):
Identity.update_node_stat(
node, {
'data_type': tf_dtype_extractor(node.pb.attr["T"].type),
})
return cls.enabled