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)