##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import os, sys, time, torch
from xautodl.log_utils import AverageMeter, time_string
from xautodl.models import change_key
from .eval_funcs import obtain_accuracy
def get_flop_loss(expected_flop, flop_cur, flop_need, flop_tolerant):
expected_flop = torch.mean(expected_flop)
if flop_cur < flop_need - flop_tolerant: # Too Small FLOP
loss = -torch.log(expected_flop)
# elif flop_cur > flop_need + flop_tolerant: # Too Large FLOP
elif flop_cur > flop_need: # Too Large FLOP
loss = torch.log(expected_flop)
else: # Required FLOP
loss = None
if loss is None:
return 0, 0
else:
return loss, loss.item()
def search_train(
search_loader,
network,
criterion,
scheduler,
base_optimizer,
arch_optimizer,
optim_config,
extra_info,
print_freq,
logger,
):
data_time, batch_time = AverageMeter(), AverageMeter()
base_losses, arch_losses, top1, top5 = (
AverageMeter(),
AverageMeter(),
AverageMeter(),
AverageMeter(),
)
arch_cls_losses, arch_flop_losses = AverageMeter(), AverageMeter()
epoch_str, flop_need, flop_weight, flop_tolerant = (
extra_info["epoch-str"],
extra_info["FLOP-exp"],
extra_info["FLOP-weight"],
extra_info["FLOP-tolerant"],
)
network.train()
logger.log(
"[Search] : {:}, FLOP-Require={:.2f} MB, FLOP-WEIGHT={:.2f}".format(
epoch_str, flop_need, flop_weight
)
)
end = time.time()
network.apply(change_key("search_mode", "search"))
for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(
search_loader
):
scheduler.update(None, 1.0 * step / len(search_loader))
# calculate prediction and loss
base_targets = base_targets.cuda(non_blocking=True)
arch_targets = arch_targets.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - end)
# update the weights
base_optimizer.zero_grad()
logits, expected_flop = network(base_inputs)
# network.apply( change_key('search_mode', 'basic') )
# features, logits = network(base_inputs)
base_loss = criterion(logits, base_targets)
base_loss.backward()
base_optimizer.step()
# record
prec1, prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
base_losses.update(base_loss.item(), base_inputs.size(0))
top1.update(prec1.item(), base_inputs.size(0))
top5.update(prec5.item(), base_inputs.size(0))
# update the architecture
arch_optimizer.zero_grad()
logits, expected_flop = network(arch_inputs)
flop_cur = network.module.get_flop("genotype", None, None)
flop_loss, flop_loss_scale = get_flop_loss(
expected_flop, flop_cur, flop_need, flop_tolerant
)
acls_loss = criterion(logits, arch_targets)
arch_loss = acls_loss + flop_loss * flop_weight
arch_loss.backward()
arch_optimizer.step()
# record
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
arch_flop_losses.update(flop_loss_scale, arch_inputs.size(0))
arch_cls_losses.update(acls_loss.item(), arch_inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if step % print_freq == 0 or (step + 1) == len(search_loader):
Sstr = (
"**TRAIN** "
+ time_string()
+ " [{:}][{:03d}/{:03d}]".format(epoch_str, step, len(search_loader))
)
Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
batch_time=batch_time, data_time=data_time
)
Lstr = "Base-Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})".format(
loss=base_losses, top1=top1, top5=top5
)
Vstr = "Acls-loss {aloss.val:.3f} ({aloss.avg:.3f}) FLOP-Loss {floss.val:.3f} ({floss.avg:.3f}) Arch-Loss {loss.val:.3f} ({loss.avg:.3f})".format(
aloss=arch_cls_losses, floss=arch_flop_losses, loss=arch_losses
)
logger.log(Sstr + " " + Tstr + " " + Lstr + " " + Vstr)
# Istr = 'Bsz={:} Asz={:}'.format(list(base_inputs.size()), list(arch_inputs.size()))
# logger.log(Sstr + ' ' + Tstr + ' ' + Lstr + ' ' + Vstr + ' ' + Istr)
# print(network.module.get_arch_info())
# print(network.module.width_attentions[0])
# print(network.module.width_attentions[1])
logger.log(
" **TRAIN** Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f} Error@1 {error1:.2f} Error@5 {error5:.2f} Base-Loss:{baseloss:.3f}, Arch-Loss={archloss:.3f}".format(
top1=top1,
top5=top5,
error1=100 - top1.avg,
error5=100 - top5.avg,
baseloss=base_losses.avg,
archloss=arch_losses.avg,
)
)
return base_losses.avg, arch_losses.avg, top1.avg, top5.avg
def search_valid(xloader, network, criterion, extra_info, print_freq, logger):
data_time, batch_time, losses, top1, top5 = (
AverageMeter(),
AverageMeter(),
AverageMeter(),
AverageMeter(),
AverageMeter(),
)
network.eval()
network.apply(change_key("search_mode", "search"))
end = time.time()
# logger.log('Starting evaluating {:}'.format(epoch_info))
with torch.no_grad():
for i, (inputs, targets) in enumerate(xloader):
# measure data loading time
data_time.update(time.time() - end)
# calculate prediction and loss
targets = targets.cuda(non_blocking=True)
logits, expected_flop = network(inputs)
loss = criterion(logits, targets)
# record
prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update(prec1.item(), inputs.size(0))
top5.update(prec5.item(), inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % print_freq == 0 or (i + 1) == len(xloader):
Sstr = (
"**VALID** "
+ time_string()
+ " [{:}][{:03d}/{:03d}]".format(extra_info, i, len(xloader))
)
Tstr = "Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})".format(
batch_time=batch_time, data_time=data_time
)
Lstr = "Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})".format(
loss=losses, top1=top1, top5=top5
)
Istr = "Size={:}".format(list(inputs.size()))
logger.log(Sstr + " " + Tstr + " " + Lstr + " " + Istr)
logger.log(
" **VALID** Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f} Error@1 {error1:.2f} Error@5 {error5:.2f} Loss:{loss:.3f}".format(
top1=top1,
top5=top5,
error1=100 - top1.avg,
error5=100 - top5.avg,
loss=losses.avg,
)
)
return losses.avg, top1.avg, top5.avg