def get_model(args):
if args.multiScale:
model = multiscale_iResNet(in_shape,
args.nBlocks, args.nStrides, args.nChannels,
args.init_ds == 2,
args.inj_pad, args.coeff, args.densityEstimation,
args.nClasses,
args.numTraceSamples, args.numSeriesTerms,
args.powerIterSpectralNorm,
actnorm=(not args.noActnorm),
learn_prior=(not args.fixedPrior),
nonlin=args.nonlin)
else:
model = iResNet(nBlocks=args.nBlocks, nStrides=args.nStrides,
nChannels=args.nChannels, nClasses=args.nClasses,
init_ds=args.init_ds,
inj_pad=args.inj_pad,
in_shape=in_shape,
coeff=args.coeff,
numTraceSamples=args.numTraceSamples,
numSeriesTerms=args.numSeriesTerms,
n_power_iter = args.powerIterSpectralNorm,
density_estimation=args.densityEstimation,
actnorm=(not args.noActnorm),
learn_prior=(not args.fixedPrior),
nonlin=args.nonlin)
return model
def get_model(args):
if args.multiScale:
model = multiscale_iResNet(in_shape,
args.nBlocks,
args.nStrides,
args.nChannels,
args.doAttention,
args.init_ds == 2,
args.coeff,
args.nClasses,
args.numTraceSamples,
args.numSeriesTerms,
args.powerIterSpectralNorm,
actnorm=(not args.noActnorm),
nonlin=args.nonlin,
use_label=args.use_label)
else:
# model = iResNet(nBlocks=args.nBlocks, nStrides=args.nStrides,
# nChannels=args.nChannels, nClasses=args.nClasses,
# init_ds=args.init_ds,
# inj_pad=args.inj_pad,
# in_shape=in_shape,
# coeff=args.coeff,
# numTraceSamples=args.numTraceSamples,
# numSeriesTerms=args.numSeriesTerms,
# n_power_iter = args.powerIterSpectralNorm,
# density_estimation=args.densityEstimation,
# actnorm=(not args.noActnorm),
# learn_prior=(not args.fixedPrior),
# nonlin=args.nonlin)
print("Only multiscale model supported.")
exit()
return model