def load_networks(self, verbose=True):
super(ResnetSupernet, self).load_networks()
if hasattr(self, 'netG_student_tmp'):
load_pretrained_weight(self.opt.student_netG.replace('super_', ''), self.opt.student_netG,
self.netG_student_tmp, self.netG_student,
self.opt.student_ngf, self.opt.student_ngf)
del self.netG_student_tmp
def load_networks(self, verbose=True):
if self.opt.restore_pretrained_G_path is not None:
util.load_network(self.netG_pretrained, self.opt.restore_pretrained_G_path, verbose)
load_pretrained_weight(self.opt.pretrained_netG, self.opt.student_netG,
self.netG_pretrained, self.netG_student,
self.opt.pretrained_ngf, self.opt.student_ngf)
del self.netG_pretrained
super(ResnetDistiller, self).load_networks()
def load_networks(self, model_weight=None):
if self.cfgs.restore_pretrained_G_path != False:
if self.cfgs.restore_pretrained_G_path != None:
pretrained_G_path = self.cfgs.restore_pretrained_G_path
util.load_network(self.netG_pretrained, pretrained_G_path)
else:
assert len(
model_weight
) != 0, "restore_pretrained_G_path and model_weight can not be None at the same time"
if self.cfgs.direction == 'AtoB':
self.netG_pretrained.set_dict(
model_weight['netG_A'] or model_weight['netG_teacher'])
else:
self.netG_pretrained.set_dict(
model_weight['netG_B'] or model_weight['netG_teacher'])
load_pretrained_weight(self.cfgs.pretrained_netG,
self.cfgs.distiller_student_netG,
self.netG_pretrained, self.netG_student,
self.cfgs.pretrained_ngf,
self.cfgs.student_ngf)
del self.netG_pretrained
super(ResnetDistiller, self).load_networks(model_weight)