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