class transformer(rpn):
def __init__(
self,
dims: list[int] | tuple[int],
fc_dims: list[int] | tuple[int],
batch_num: int = None,
name: str = 'rpn_transformer',
channel_num: int = 1, width: int = 1,
# interdependence function parameters
with_dual_lphm_interdependence: bool = False,
with_lorr_interdependence: bool = True, r_interdependence: int = 3,
# data transformation function parameters
with_taylor: bool = False, d: int = 2,
# parameter reconciliation function parameters
with_dual_lphm: bool = False,
with_lorr: bool = False, r: int = 3,
enable_bias: bool = True,
# remainder function parameters
with_residual: bool = True,
# output processing parameters
with_batch_norm: bool = True,
with_relu: bool = True,
with_softmax: bool = True,
with_dropout: bool = True, p: float = 0.25,
# other parameters
device: str = 'cpu', *args, **kwargs
):
print('############# rpn-transformer model architecture ############')
if len(dims) < 2:
raise ValueError("At least two dim values is needed for defining the model...")
if len(fc_dims) < 1:
raise ValueError("At least one fc_dim value is needed for defining the model...")
layers = []
for m, n in zip(dims, dims[1:]):
layers.append(
bilinear_interdependence_layer(
m=m, n=n,
batch_num=batch_num,
channel_num=channel_num,
width=width,
# --------------------------
with_dual_lphm_interdependence=with_dual_lphm_interdependence,
with_lorr_interdependence=with_lorr_interdependence, r_interdependence=r_interdependence,
# --------------------------
with_taylor=with_taylor, d=d,
# --------------------------
with_dual_lphm=with_dual_lphm,
with_lorr=with_lorr, r=r,
enable_bias=enable_bias,
# --------------------------
with_residual=with_residual,
# --------------------------
with_batch_norm=with_batch_norm,
with_relu=with_relu,
with_dropout=with_dropout, p=p,
with_softmax=with_softmax,
# --------------------------
device=device,
)
)
layers.append(
perceptron_layer(
m=n, n=n,
channel_num=channel_num,
width=width,
# --------------------------
with_taylor=with_taylor, d=d,
# --------------------------
with_dual_lphm=with_dual_lphm,
with_lorr=with_lorr, r=r,
enable_bias=enable_bias,
# --------------------------
with_residual=with_residual,
# --------------------------
with_batch_norm=with_batch_norm,
with_relu=with_relu,
with_dropout=with_dropout, p=p,
with_softmax=with_softmax,
# --------------------------
device=device,
)
)
fc_dims = [dims[-1]] + list(fc_dims)
for m, n in zip(fc_dims, fc_dims[1:]):
layers.append(
perceptron_layer(
m=m, n=n,
channel_num=channel_num,
width=width,
# --------------------------
with_taylor=with_taylor, d=d,
# --------------------------
with_dual_lphm=with_dual_lphm,
with_lorr=with_lorr, r=r,
enable_bias=enable_bias,
# --------------------------
with_residual=with_residual,
# --------------------------
with_batch_norm=with_batch_norm and n != fc_dims[-1],
with_relu=with_relu and n != fc_dims[-1],
with_dropout=with_dropout and n != fc_dims[-1], p=p,
with_softmax=with_softmax and m == fc_dims[-2] and n == fc_dims[-1],
# --------------------------
device=device,
)
)
super().__init__(name=name, layers=layers, device=device, *args, **kwargs)