def extract(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':
Activation.update_node_stat(node, {'operation': act_type})
elif act_type == 'leaky':
negative_slope = attrs.float('slope', 0.25)
ReLU.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 _convert_to_leaky_relu_action(graph: nx.MultiDiGraph, matches: dict):
"""
This function checks given patten and if pattern satisfies all requirements, converts to ReLU with negative slope
"""
power_op = matches['power_op']
power_data = matches['power_data']
input_data = matches['data']
eltwise_op = matches['eltwise_op']
eltwise_data = eltwise_op.out_node()
# Check that all nodes satisfies conversion requirements
if len(eltwise_op.in_nodes()) > 2:
log.debug(
'Eltwise layer ({}) can not participate in conversion to leaky ReLU due to it has more than two '
'inputs ({})'.format(eltwise_op.id, len(eltwise_op.in_nodes())))
return
if eltwise_op.soft_get('operation') != 'max':
log.debug(
'Eltwise layer ({}) can not participate in conversion to leaky ReLU due to it has not satisfied '
'operation type ({}) should be max'.format(
eltwise_op.id, eltwise_op.soft_get('operation')))
return
if not (power_op.has_valid('scale') and power_op.has_valid('power')
and power_op.has_valid('shift')):
log.debug(
'Power layer ({}) can not participate in conversion to leaky ReLU due to missing attribute (scale, '
'power or shift)'.format(power_op.id))
return
if power_op.scale > 1 or power_op.power != 1 or power_op.shift != 0:
log.debug(
'Power layer ({}) can not participate in conversion to leaky ReLU due to wrong parameters(Scale = {} '
'(should be < 1), Power {} (should be = 1), Shift {} (should be = 0))'
''.format(power_op.id, power_op.scale, power_op.power,
power_op.shift))
return
if len(power_data.out_nodes()) > 1:
log.debug(
'Power layer({}) can not participate in conversion to leaky ReLU due to it has more than one consumer'
''.format(power_op.id))
return
# Disconnect data nodes from ops
graph.remove_edge(eltwise_op.id, eltwise_data.id)
graph.remove_edge(input_data.id, power_op.id)
graph.remove_edge(input_data.id, eltwise_op.id)
# Create new ReLU operation
relu_op = ReLU(
graph, dict(name="LeakyReLU_",
negative_slope=np.array(power_op.scale)))
relu_op.create_node_with_data(inputs=[input_data], data_nodes=eltwise_data)
log.debug(
'Successful conversion from {} {} to ReLU with negative slope (leaky ReLU)'
''.format(eltwise_op.id, power_op.id))
def extract(node):
attrs = get_mxnet_layer_attrs(node.symbol_dict)
act_type = attrs.str('act_type', 'relu')
if act_type == 'sigmoid' or act_type == 'tanh':
Activation.update_node_stat(node, {'operation': act_type})
elif act_type == 'relu':
ReLU.update_node_stat(node)
else:
raise Error(
"Operation '{}' not supported. Please register it as custom op. "
+ refer_to_faq_msg(86), act_type)
return ActivationFrontExtractor.enabled
def extract(node):
ReLU.update_node_stat(node, {})
return __class__.enabled
def extract(node):
ReLU.update_node_stat(node)
return ReLUFrontExtractor.enabled
def extract(node):
negative_slope = onnx_attr(node, 'alpha', 'f', default=1.0)
ReLU.update_node_stat(node, {'negative_slope': negative_slope})
return LeakyReLUFrontExtractor.enabled