def extract(cls, node):
attrs = get_mxnet_layer_attrs(node.symbol_dict)
act_type = attrs.str('act_type', 'leaky')
if act_type == 'prelu':
prelu_attrs = {
'channel_shared': 1,
'filler_type': 'constant',
'filler_value': 0,
'min': 0,
'max': 1,
'mean': 0,
'std': 0,
'sparse': -1,
'variance_norm': "caffe.FillerParameter.FAN_IN"
}
PreluOp.update_node_stat(node, prelu_attrs)
elif act_type == 'elu':
alpha = attrs.float('slope', 0.25)
Elu.update_node_stat(node, {'alpha': alpha})
elif act_type == 'leaky':
negative_slope = attrs.float('slope', 0.25)
if negative_slope == 0:
ReLU.update_node_stat(node)
else:
LeakyReLU.update_node_stat(node,
{'negative_slope': negative_slope})
else:
raise Error(
"Operation '{}' not supported. Please register it as custom op. "
+ refer_to_faq_msg(86), act_type)
return LeakyReLUFrontExtractor.enabled
def replace_pattern(graph: Graph, match: dict):
relu = match['leakyrelu']
if not relu.has_valid('negative_slope'):
return
# Create PReLU op and reconnect input/output from LeakyReLU to PReLU
prelu = PreluOp(graph, dict(name=relu.name)).create_node()
const = Const(
graph,
dict(name=relu.name + "/weights",
value=np.array([relu.negative_slope]))).create_node()
relu.in_port(0).get_connection().set_destination(prelu.in_port(0))
const.out_port(0).connect(prelu.in_port(1))
relu.out_port(0).get_connection().set_source(prelu.out_port(0))
def extract(cls, node):
proto_layer = node.pb
pb_model = node.model_pb
param = proto_layer.prelu_param
update_attrs = {'channel_shared': int(param.channel_shared)}
variance_norm_caffe_map = {
0: 'caffe.FillerParameter.FAN_IN',
1: 'caffe.FillerParameter.FAN_OUT',
2: 'caffe.FillerParameter.AVERAGE'
}
if hasattr(param, 'filler'):
update_attrs.update({
'filler_type':
param.filler.type,
'filler_value':
int(param.filler.value),
'min':
int(param.filler.min),
'max':
int(param.filler.max),
'mean':
int(param.filler.mean),
'std':
int(param.filler.std),
'sparse':
param.filler.sparse,
'variance_norm':
variance_norm_caffe_map[param.filler.variance_norm]
})
mapping_rule = merge_attrs(param, update_attrs)
mapping_rule.update(weights_biases(False, pb_model))
mapping_rule.update(layout_attrs())
# update the attributes of the node
PreluOp.update_node_stat(node, mapping_rule)
return cls.enabled
def replace_sub_graph(self, graph: Graph, match: dict):
consumers = [
n for n in match if n not in ['mul', 'op', 'add']
and not check_node_usages_out_of_match(match, n)
]
if consumers:
log.warning(
'PReLU pattern was detected. Non pattern consumers of nodes: "{}" were found. Won\'t replace'
''.format(', '.join([match[n].id for n in consumers])))
return
gamma = match['mul'].in_node(0) if match['mul'].in_node(
1).id == match['neg_1'].id else match['mul'].in_node(1)
prelu_node = PreluOp(graph, {
'name': '{}/PReLU'.format(match['add'].id)
}).create_node([match['op'], gamma])
match['add'].replace_node(prelu_node)
log.debug(
'PReLU pattern starting from "{}" was collapsed to "{}"'.format(
match['op'].id, prelu_node.id))
