y_ax = [] l_test = [] l_mean = [] l_std = [] y_mean = [] y_std = [] h_rounds = [] h_eps = [] h_eta_phi = [] h_models = [] h_grads = [] # non_zero_grad for getting a true_grad in fog_train # grad is not used in fcn case print('Pre-Training') best, _ = test(args, model, device, test_loader, best, 1, loss_type) best_iter = -1 print('Acc: {:.3f}'.format(best)) print('EUT Schedule') eut_schedule = get_eut_schedule(args) lut_schedule = get_lut_schedule(args) print('EUT: ', eut_schedule) print('LUT: ', lut_schedule) print('Rounds: ', args.rounds) print('+' * 80) print('Training') print('epoch \t tr loss (acc) (mean+-std) \t test loss (acc) \t EUT') worker_models = {} worker_memory = {}
print('+' * 80) h_epoch = [] h_acc_test = [] h_acc_train = [] h_acc_train_std = [] h_loss_test = [] h_loss_train = [] h_loss_train_std = [] h_uplink = [] h_grad_agg = [] h_error = [] print('Pre-Training') # tb_model_summary(model, test_loader, tb, device) best, i = test(model, device, test_loader, loss_type) ii, iii = test(model, device, test_loader, loss_type) print('Acc: {:.4f}'.format(best)) tb.add_scalar('Train_Loss', iii, 0) tb.add_scalar('Val_Loss', i, 0) tb.add_scalar('Train_Acc', ii, 0) tb.add_scalar('Val_Acc', best, 0) # worker_models: actual models to train # worker_mbufs: momentum buffer for sgd # model mbuf: moementum buffer for model # worker_residuals: for error-feedback during TopK, LBGM, etc. # worker_sdirs: directions used for approximations worker_models = {} worker_mbufs = {} model_mbuf = []
import torch from models.train import test, make_dataloader from models.model import Net if __name__=='__main__': import sys, os model_file = sys.argv[1] report_filename = sys.argv[2] loader = make_dataloader(os.path.join('s3data', 'protocol_V2/ASVspoof2017_V2_dev.trl.txt'), os.path.join('s3data/wideband-768', 'dev-files/'), 10) # load a saved model device = torch.device('cpu') model = Net() #model.load_state_dict() model = torch.load(model_file, map_location=device) print("model", model_file, model) # test it test(model, loader, report_filename)