def _intermediate_stats_logging(self, outs, y, loss, step, epoch, N, len_loader, val_or_train):
prec1, prec3 = accuracy(outs, y, topk=(1, 3))
self.losses.update(loss.item(), N)
self.top1.update(prec1.item(), N)
self.top3.update(prec3.item(), N)
if (step > 1 and step % self.print_freq == 0) or step == len_loader - 1:
self.logger.info(val_or_train+
": [{:3d}/{}] Step {:03d}/{:03d} Loss {:.3f} "
"Prec@(1,3) ({:.1%}, {:.1%})".format(
epoch + 1, self.cnt_epochs, step, len_loader - 1, self.losses.get_avg(),
self.top1.get_avg(), self.top3.get_avg()))
def forward(self, outs, targets, latency, energy, losses_ce, losses_lat, losses_energy, N): #energy add
ce = self.weight_criterion(outs, targets)
cal_loss = ce.to('cpu').detach().numpy().copy()
prec1, _ = accuracy(outs, targets, topk=(1, 3)) #追加
prec1 = prec1.to('cpu').detach().numpy().copy()
global device
'''
rate = prec1 / 0.80
#print("ce")
#print(ce)
if prec1 >= 0.80:
ce = torch.sub(ce, ce)
else:
ce = torch.sub(ce, cal_loss * rate)
#print(ce)
'''
#ce = torch.add(ce, 1.0)
if torch.isnan(ce) or ce < 0.0: #改良
ce = torch.tensor(0.0, requires_grad=True)
cal_lat = torch.log(latency ** self.beta) #出力用, 改良
#energy_cal = torch.log(energy ** self.beta) #出力用, 改良
energy_cal = torch.log(energy ** self.delta)
energy_cal = energy_cal.to('cpu').detach().numpy().copy() #計算用
cal_lat = cal_lat.to('cpu').detach().numpy().copy() #計算用
if energy_cal < 0 or np.isnan(energy_cal): #改良
energy_cal = np.zeros(1)
if cal_lat < 0 or np.isnan(cal_lat): #改良
cal_lat = np.zeros(1)
lat = torch.log(latency ** self.beta) #original
#energy = torch.log(energy ** self.beta)
energy = torch.log(energy ** self.delta)
'''
#lat = torch.div(lat, 1.7)
rate = 5 / cal_lat
rate = torch.from_numpy(rate).clone()
rate = rate.to(device)
tmp = torch.mul(lat, rate)
#device = torch.device("cuda:3")
tmp = tmp.to(device)
if cal_lat > 5:
lat = torch.sub(lat, tmp)
lat = torch.add(lat, 1.0)
else:
lat = torch.div(lat, 5)
lat = torch.pow(lat, 0.1)
#lat = torch.add(lat, 1.0)
#print(lat)
'''
losses_ce.update(ce.item(), N)
losses_lat.update(cal_lat.item(), N)
losses_energy.update(energy_cal.item(), N)
if energy < 0 or torch.isnan(energy):
energy = torch.tensor(0.0)
if lat < 0 or torch.isnan(lat):
lat = torch.tensor(0.0)
#loss = self.alpha * ce * (lat + energy)
loss = ce + (self.alpha * lat) + (self.gamma * energy)
#loss = ce * (lat + energy)
#loss = self.alpha * (ce + lat + energy)
#loss = ce + lat + energy
if loss < 0 or torch.isnan(loss):
loss = torch.tensor(0.0, requires_grad=True)
return loss