def train(self, train_set, valid_set=None, ref_set=None):
print('==> Start training {}'.format(self.reconstruction_path))
self.train_flag = True
if self.early_stop:
valid_loader = DataLoader(valid_set,
batch_size=self.test_batch_size,
shuffle=True,
num_workers=2)
for epoch in range(self.start_epoch, self.start_epoch + self.epochs):
permutated_idx = np.random.permutation(ref_set.__len__())
ref_set = Subset(ref_set, permutated_idx)
train_ref_set = data.DoubleDataset(train_set, ref_set)
train_ref_loader = DataLoader(train_ref_set,
batch_size=self.train_batch_size,
shuffle=True,
num_workers=2)
if self.train_flag:
self.train_epoch(train_ref_loader, epoch)
if self.use_scheduler:
self.scheduler_enc.step()
self.scheduler_dec.step()
if self.early_stop:
self.inference(valid_loader, epoch, type='valid')
else:
break
def train(self, train_set, valid_set=None, ref_set=None):
print('==> Start training {}'.format(self.reconstruction_path))
if self.resume:
print('==> Resuming from checkpoint..')
try:
last_epoch = np.load(os.path.join(self.reconstruction_path, 'last_epoch.npy'))
# print(last_epoch)
self.load(last_epoch)
except FileNotFoundError:
print('There is no pre-trained model; Train model from scratch')
self.train_flag = True
if self.early_stop:
valid_loader = DataLoader(valid_set, batch_size=self.test_batch_size, shuffle=True, num_workers=2)
# if ref_set is not None:
# ref_set = self.transform(ref_set)
for epoch in range(self.start_epoch, self.start_epoch + self.epochs):
permutated_idx = np.random.permutation(ref_set.__len__())
ref_set = Subset(ref_set, permutated_idx)
train_ref_set = data.DoubleDataset(train_set, ref_set)
train_ref_loader = DataLoader(train_ref_set, batch_size=self.train_batch_size, shuffle=True, num_workers=2)
if self.train_flag:
self.train_epoch(train_ref_loader, epoch)
if self.early_stop:
self.inference(valid_loader, epoch, inference_type='valid')
else:
if epoch > self.disentanglement_start_epoch - 1:
for encoder_name in self.encoder_name_list:
self.encoders_opt_scheduler[encoder_name].step()
self.class_discs_opt_scheduler[encoder_name].step()
self.membership_discs_opt_scheduler[encoder_name].step()
self.decoder_opt_scheduler.step()
if self.weights['real_fake'] > 0:
self.rf_disc_opt_scheduler.step()
if (epoch+1) % self.save_step_size == 0:
print('Save at {}'.format(epoch+1))
state = {
# 'best_valid_loss': loss,
'epoch': epoch,
}
for encoder_name in self.encoder_name_list:
state['enc_' + encoder_name] = self.encoders[encoder_name].state_dict()
state['dec'] = self.decoder.state_dict()
state['class_disc_' + encoder_name] = self.class_discs[encoder_name].state_dict()
state['membership_disc_' + encoder_name] = self.membership_discs[encoder_name].state_dict()
torch.save(state, os.path.join(self.reconstruction_path, 'ckpt{:03d}.pth'.format(epoch+1)))
# self.best_valid_loss = loss
# self.early_stop_count = 0
# self.best_class_acc_dict = self.class_acc_dict
# self.best_membership_acc_dict = self.membership_acc_dict
np.save(os.path.join(self.reconstruction_path, 'class_acc{:03d}.npy'.format(epoch+1)), self.class_acc_dict)
np.save(os.path.join(self.reconstruction_path, 'membership_acc{:03d}.npy'.format(epoch+1)), self.membership_acc_dict)
np.save(os.path.join(self.reconstruction_path, 'last_epoch.npy'), epoch)
# vutils.save_image(recons, os.path.join(self.reconstruction_path, '{}.png'.format(epoch)), nrow=10)
# if self.early_stop:
# val_loss = self.inference(valid_loader, epoch, inference_type='valid')
# for encoder_name in self.encoder_name_list:
# self.encoders_opt_scheduler[encoder_name].step(val_loss)
# self.class_discs_opt_scheduler[encoder_name].step(val_loss)
# self.membership_discs_opt_scheduler[encoder_name].step(val_loss)
# self.decoder_opt_scheduler.step(val_loss)
# self.rf_disc_opt_scheduler.step(val_loss)
# else:
# if (epoch+1) % self.save_step_size == 0:
# print('Save at {}'.format(epoch+1))
# state = {
# # 'best_valid_loss': loss,
# 'epoch': epoch,
# }
# for encoder_name in self.encoder_name_list:
# state['enc_' + encoder_name] = self.encoders[encoder_name].state_dict()
# state['dec'] = self.decoder.state_dict()
# state['class_disc_' + encoder_name] = self.class_discs[encoder_name].state_dict()
# state['membership_disc_' + encoder_name] = self.membership_discs[encoder_name].state_dict()
# torch.save(state, os.path.join(self.reconstruction_path, 'ckpt{:03d}.pth'.format(epoch+1)))
# # self.best_valid_loss = loss
# # self.early_stop_count = 0
# # self.best_class_acc_dict = self.class_acc_dict
# # self.best_membership_acc_dict = self.membership_acc_dict
# np.save(os.path.join(self.reconstruction_path, 'class_acc{:03d}.npy'.format(epoch+1)), self.class_acc_dict)
# np.save(os.path.join(self.reconstruction_path, 'membership_acc{:03d}.npy'.format(epoch+1)), self.membership_acc_dict)
# np.save(os.path.join(self.reconstruction_path, 'last_epoch.npy'), epoch)
# # vutils.save_image(recons, os.path.join(self.reconstruction_path, '{}.png'.format(epoch)), nrow=10)
else:
break