def initialize_networks(self, opt):
netG = networks.define_G(opt)
netD = networks.define_D(opt) if opt.isTrain else None
netE = networks.define_E(opt) if opt.use_vae else None
if not opt.isTrain or opt.continue_train:
netG = util.load_network(netG, 'G', opt.which_epoch, opt)
if opt.isTrain:
netD = util.load_network(netD, 'D', opt.which_epoch, opt)
if opt.use_vae:
netE = util.load_network(netE, 'E', opt.which_epoch, opt)
return netG, netD, netE
def initialize_networks(self):
netD = networks.define_D2(self.opt) if self.opt.isTrain else None
if self.opt.isTrain and self.opt.which_iter_D2>0:
print_network(netD)
netD = util.load_network(netD, 'D2', self.opt.which_iter_D2 , self.opt)
return netD
def run_pretrain(self, load=False):
if not load:
global_iteration = 0
training_steps = int(self.n_samples /self.opt.batchSize)
self.num_semantics = self.progressive_model.num_semantics
self.set_data_resolution(int(self.opt.crop_size/self.opt.aspect_ratio))
print(f"Training at resolution {self.progressive_model.generator.res}")
dim_ind = 0
phase = "stabilize"
scaling = int(self.opt.crop_size / (self.opt.aspect_ratio * self.progressive_model.generator.res))
num_epochs=1
for epoch in range(num_epochs):
for iteration in range(training_steps):
seg, _, im, _ = self.next_batch()
seg, seg_mc, im = self.call_next_batch(seg,im)
D_losses = self.step_discriminator( iteration, global_iteration, dim_ind, seg_mc, seg, im, scaling, phase)
global_iteration += 1
if (iteration + 1) % 10 == 0:
print(
f"Res {self.progressive_model.generator.res:03d}, {phase.rjust(9)}: Iteration {iteration + 1:05d}/{training_steps:05d}, epoch:{epoch + 1:05d}/{num_epochs:05d}"
)
if epoch % self.opt.save_epoch_freq == 0 or \
(epoch+1) == num_epochs:
util.save_network(self.end2end_model_on_one_gpu.netD2, 'D2', global_iteration, self.opt)
else:
netD2 = self.end2end_model_on_one_gpu.netD2
netD2 = util.load_network(netD2, 'D2', self.opt.which_iter_D2, self.opt)
global_iteration = self.opt.which_iter_D2
return global_iteration