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