def replace_pattern(self, graph: Graph, match: dict):
y = match['maximum'].in_port(0).data.get_value()
if y is None:
y = match['maximum'].in_port(1).data.get_value()
if y is None or y.shape != ():
log.debug(
'The value of the "maximum_y_data" is not defined or is not constant'
)
return
normalize_input_node = match['square'].in_port(0).get_source().node
normalize_node = NormalizeOp(
graph, {
'name': normalize_input_node.soft_get('name') + '/Normalize',
'eps': y,
'across_spatial': 0,
'channel_shared': 0
}).create_node()
weights_node = Const(
graph, {
'value':
np.ones(shape=int64_array([match['input'].shape[-1]]),
dtype=match['input'].data_type)
}).create_node()
# the normalize_input_node has 2 consumers so it is necessary to disconnect output port first
normalize_input_node.out_port(0).disconnect()
normalize_input_node.out_port(0).get_connection().set_destination(
normalize_node.in_port(0))
weights_node.out_port(0).get_connection().set_destination(
normalize_node.in_port(1))
match['l2_normalize'].out_port(0).get_connection().set_source(
normalize_node.out_port(0))
def extract(cls, node):
proto_layer = node.pb
param = proto_layer.norm_param
attrs = collect_attributes(param, enable_flattening_nested_params=True)
attrs.update(weights_biases(False, node.model_pb))
# update the attributes of the node
NormalizeOp.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
across_spatial = onnx_attr(node, 'across_spatial', 'i', default=0)
channel_shared = onnx_attr(node, 'channel_shared', 'i', default=0)
eps = onnx_attr(node, 'eps', 'f', default=0)
attrs = {
'across_spatial': bool(across_spatial),
'channel_shared': bool(channel_shared),
'eps': eps,
'layout': 'NCHW'
}
# update the attributes of the node
NormalizeOp.update_node_stat(node, attrs)
return cls.enabled
def replace_pattern(self, graph: Graph, match: dict):
bias_add = match['bias_add']
merge = match['merge']
normalize_node = NormalizeOp(
graph, {
'name': merge.name + '/Normalize',
'eps': 1e-6,
'across_spatial': 0,
'channel_shared': 0
}).create_node()
# the normalize_input_node has 2 consumers so it is necessary to disconnect output port first
bias_add.out_port(0).connect(normalize_node.in_port(0))
merge.in_port(0).disconnect()
normalize_node.out_port(0).get_connection().set_destination(
merge.in_port(0))
def replace_pattern(self, graph: Graph, match: dict):
y = match['maximum'].in_port(0).data.get_value()
if y is None:
y = match['maximum'].in_port(1).data.get_value()
if y is None or y.shape != ():
log.debug(
'The value of the "maximum_y_data" is not defined or is not constant'
)
return
# rename l2_normalize node since it will be no longer output after the transformation
output_name = match['l2_normalize'].soft_get('name',
match['l2_normalize'].id)
normalizel2_name = output_name + '/normalizel2'
rename_node(match['l2_normalize'], normalizel2_name)
normalize_node = NormalizeOp(
graph, {
'name': output_name,
'eps': y,
'across_spatial': 0,
'channel_shared': 0
}).create_node()
rename_node(normalize_node, output_name)
weights_node = Const(
graph, {
'value':
np.ones(shape=int64_array([match['input'].shape[-1]]),
dtype=match['input'].data_type)
}).create_node()
match['square'].in_port(0).get_source().connect(
normalize_node.in_port(0))
match['square'].in_port(0).disconnect()
if match['l2_normalize'].in_port(
0).get_source().node.id == match['rsqrt'].id:
match['l2_normalize'].in_port(1).disconnect()
else:
match['l2_normalize'].in_port(0).disconnect()
weights_node.out_port(0).get_connection().set_destination(
normalize_node.in_port(1))
match['l2_normalize'].out_port(0).get_connection().set_source(
normalize_node.out_port(0))
def extract(cls, node):
pb = node.parameters
try:
collect_until_token(pb, b'<Dim>')
except Error:
try:
pb.seek(0)
collect_until_token(pb, b'<InputDim>')
except Error:
raise Error("Neither <Dim> nor <InputDim> were found")
in_dim = read_binary_integer32_token(pb)
try:
collect_until_token(pb, b'<TargetRms>')
target_rms = read_binary_float_token(pb)
except Error:
# model does not contain TargetRms
target_rms = 1.0
try:
collect_until_token(pb, b'<AddLogStddev>')
add_log = read_binary_bool_token(pb)
except Error:
# model does not contain AddLogStddev
add_log = False
if add_log is not False:
raise Error(
"AddLogStddev True in Normalize component is not supported")
scale = target_rms * np.sqrt(in_dim)
attrs = {
'eps': 0.00000001,
'across_spatial': 0,
'channel_shared': 1,
'in_dim': in_dim,
}
embed_input(attrs, 1, 'weights', [scale])
NormalizeOp.update_node_stat(node, attrs)
return cls.enabled