class gcn(rpn):
def __init__(
self,
dims: list[int] | tuple[int],
channel_num: int = 1,
width: int = 1,
name: str = 'rpn_gcn',
# graph structure parameters
graph: graph_class = None,
graph_file_path: str = None,
nodes: list = None,
links: list = None,
directed: bool = False,
# graph interdependence function parameters
with_multihop: bool = False, h: int = 1, accumulative: bool = False,
with_pagerank: bool = False, c: float = 0.15,
require_data: bool = False,
require_parameters: bool = False,
# adj matrix processing parameters
normalization: bool = True,
normalization_mode: str = 'column',
self_dependence: bool = True,
# parameter reconciliation and remainder functions
with_dual_lphm: bool = False,
with_lorr: bool = False, r: int = 3,
with_residual: bool = False,
enable_bias: bool = False,
# output processing parameters
with_batch_norm: bool = False,
with_relu: bool = True,
with_softmax: bool = True,
with_dropout: bool = False, p: float = 0.25,
# other parameters
device: str = 'cpu', *args, **kwargs
):
print('############# rpn-gcn model architecture ############')
if len(dims) < 2:
raise ValueError("At least two dim values is needed for defining the model...")
layers = []
for m, n in zip(dims, dims[1:]):
print('layer: {},'.format(len(layers)), 'm: {}, n: {}'.format(m, n))
layers.append(
graph_interdependence_layer(
m=m, n=n,
width=width,
channel_num=channel_num,
# ---------------
graph=graph,
graph_file_path=graph_file_path,
nodes=nodes,
links=links,
directed=directed,
# ---------------
with_multihop=with_multihop, h=h, accumulative=accumulative,
with_pagerank=with_pagerank, c=c,
require_data=require_data,
require_parameters=require_parameters,
# ---------------
normalization=normalization,
normalization_mode=normalization_mode,
self_dependence=self_dependence,
# ---------------
with_dual_lphm=with_dual_lphm,
with_lorr=with_lorr, r=r,
with_residual=with_residual,
enable_bias=enable_bias,
# ---------------
with_batch_norm=with_batch_norm and n != dims[-1],
with_relu=with_relu and n != dims[-1],
with_dropout=with_dropout and n != dims[-1], p=p,
with_softmax=with_softmax and m == dims[-2] and n == dims[-1],
# ---------------
device=device,
)
)
super().__init__(name=name, layers=layers, device=device, *args, **kwargs)