def load_model(group, rank):
if MODEL == 'CNN' and DATA_SET == 'Mnist':
model = CNNMnist()
if MODEL == 'CNN' and DATA_SET == 'Cifar10':
model = CNNCifar()
if MODEL == 'ResNet18' and DATA_SET == 'Cifar10':
model = ResNet18()
if SAVE and os.path.exists('autoencoder'+str(rank)+'.t7'):
logging('===> Try resume from checkpoint')
checkpoint = torch.load('autoencoder'+str(rank)+'.t7')
model.load_state_dict(checkpoint['state'])
round = checkpoint['round']
print('===> Load last checkpoint data')
else:
round = 0
init_param(model, 0, group)
return model, round
def load_model(group, rank):
if MODEL == 'CNN' and DATASET == 'Mnist':
model = CNNMnist()
if MODEL == 'CNN' and DATASET == 'Cifar10':
model = CNNCifar()
if MODEL == 'ResNet18' and DATASET == 'Cifar10':
model = ResNet18()
if CUDA:
model.cuda()
if False and SAVE and os.path.exists('autoencoder' + str(rank) + '.t7'):
logging('===> Try resume from checkpoint')
checkpoint = torch.load('autoencoder' + str(rank) + '.t7')
model.load_state_dict(checkpoint['state'])
logging('model loaded')
else:
init_param(model, 0, group)
logging('model created')
return model
