def extract(cls, node):
attrs = {
'alpha': onnx_attr(node, 'alpha', 'f', 1e-4),
'beta': onnx_attr(node, 'beta', 'f', 0.75),
'bias': onnx_attr(node, 'bias', 'f', 1.0),
'local_size': onnx_attr(node, 'size', 'i', None),
}
AttributedLRN.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
pb = node.pb
AttributedLRN.update_node_stat(
node, {
'alpha': pb.attr['alpha'].f *
(2. * pb.attr['depth_radius'].i + 1.),
'beta': pb.attr['beta'].f,
'bias': pb.attr['bias'].f,
'local_size': (2 * pb.attr['depth_radius'].i + 1),
})
return cls.enabled
def extract(cls, node):
param = node.pb.lrn_param
region = 'same' if param.norm_region == 1 else 'across'
AttributedLRN.update_node_stat(node, {
'alpha': param.alpha,
'beta': param.beta,
'bias': 1,
'local_size': param.local_size,
'region': region,
})
return cls.enabled
def extract(cls, node):
attrs = get_mxnet_layer_attrs(node.symbol_dict)
alpha = attrs.float("alpha", 0.0001)
beta = attrs.float("beta", 0.75)
knorm = attrs.float("knorm", 2.0)
nsize = attrs.int("nsize", None)
AttributedLRN.update_node_stat(node, {
'alpha': alpha,
'beta': beta,
'bias': knorm,
'local_size': nsize,
})
return cls.enabled