def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, algo, epoch_str, print_freq, logger):
data_time, batch_time = AverageMeter(), AverageMeter()
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
end = time.time()
network.train()
for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):
scheduler.update(None, 1.0 * step / len(xloader))
base_inputs = base_inputs.cuda(non_blocking=True)
arch_inputs = arch_inputs.cuda(non_blocking=True)
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
network.zero_grad()
_, logits, _ = network(base_inputs)
base_loss = criterion(logits, base_targets)
base_loss.backward()
w_optimizer.step()
# record
base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
base_losses.update(base_loss.item(), base_inputs.size(0))
base_top1.update (base_prec1.item(), base_inputs.size(0))
base_top5.update (base_prec5.item(), base_inputs.size(0))
# update the architecture-weight
network.zero_grad()
_, logits, log_probs = network(arch_inputs)
arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
if algo == 'tunas':
with torch.no_grad():
RL_BASELINE_EMA.update(arch_prec1.item())
rl_advantage = arch_prec1 - RL_BASELINE_EMA.value
rl_log_prob = sum(log_probs)
arch_loss = - rl_advantage * rl_log_prob
elif algo == 'tas' or algo == 'fbv2':
arch_loss = criterion(logits, arch_targets)
else:
raise ValueError('invalid algorightm name: {:}'.format(algo))
arch_loss.backward()
a_optimizer.step()
# record
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
arch_top1.update (arch_prec1.item(), arch_inputs.size(0))
arch_top5.update (arch_prec5.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(xloader):
Sstr = '*SEARCH* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, 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)
Wstr = '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=base_top1, top5=base_top5)
Astr = 'Arch [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=arch_losses, top1=arch_top1, top5=arch_top5)
logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger):
data_time, batch_time = AverageMeter(), AverageMeter()
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
network.train()
end = time.time()
# pdb.set_trace()
for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):
scheduler.update(None, 1.0 * step / len(xloader))
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
w_optimizer.zero_grad()
_, logits, _ = network(base_inputs)
base_loss = criterion(logits, base_targets)
base_loss.backward()
torch.nn.utils.clip_grad_norm_(network.parameters(), 5)
w_optimizer.step()
base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
base_losses.update(base_loss.item(), base_inputs.size(0))
base_top1.update(base_prec1.item(), base_inputs.size(0))
base_top5.update(base_prec5.item(), base_inputs.size(0))
# update the architecture-weight
a_optimizer.zero_grad()
_, logits, activation_terms = network(inputs=arch_inputs)
arch_loss = criterion(logits, arch_targets)
arch_loss = arch_loss + activation_terms.mean()
arch_loss.backward()
a_optimizer.step()
arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
arch_top5.update(arch_prec5.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(xloader):
Sstr = '*SEARCH* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, 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)
Wstr = '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=base_top1, top5=base_top5)
Astr = 'Arch [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=arch_losses, top1=arch_top1, top5=arch_top5)
logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
# pdb.set_trace()
return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger):
data_time, batch_time = AverageMeter(), AverageMeter()
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
end = time.time()
network.train()
for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):
scheduler.update(None, 1.0 * step / len(xloader))
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
sampled_arch = network.module.dync_genotype(True)
network.module.set_cal_mode('dynamic', sampled_arch)
#network.module.set_cal_mode( 'urs' )
network.zero_grad()
_, logits = network(base_inputs)
base_loss = criterion(logits, base_targets)
base_loss.backward()
w_optimizer.step()
# record
base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))
base_losses.update(base_loss.item(), base_inputs.size(0))
base_top1.update (base_prec1.item(), base_inputs.size(0))
base_top5.update (base_prec5.item(), base_inputs.size(0))
# update the architecture-weight
network.module.set_cal_mode( 'joint' )
network.zero_grad()
_, logits = network(arch_inputs)
arch_loss = criterion(logits, arch_targets)
arch_loss.backward()
a_optimizer.step()
# record
arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5))
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
arch_top1.update (arch_prec1.item(), arch_inputs.size(0))
arch_top5.update (arch_prec5.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(xloader):
Sstr = '*SEARCH* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, 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)
Wstr = '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=base_top1, top5=base_top5)
Astr = 'Arch [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=arch_losses, top1=arch_top1, top5=arch_top5)
logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
#print (nn.functional.softmax(network.module.arch_parameters, dim=-1))
#print (network.module.arch_parameters)
return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
def valid_func(xloader, network, criterion):
data_time, batch_time = AverageMeter(), AverageMeter()
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(
), AverageMeter()
end = time.time()
with torch.no_grad():
for step, (X, Y) in enumerate(xloader):
X = X.cuda(non_blocking=True)
Y = Y.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - end)
# prediction
network.random_genotype(True)
_, logits = network(X)
arch_loss = criterion(logits, Y)
# record
arch_prec1, arch_prec5 = obtain_accuracy(logits.data,
Y.data,
topk=(1, 5))
arch_losses.update(arch_loss.item(), Y.size(0))
arch_top1.update(arch_prec1.item(), Y.size(0))
arch_top5.update(arch_prec5.item(), Y.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
return arch_losses.avg, arch_top1.avg, arch_top5.avg
def get_best_arch(controller, shared_cnn, xloader, n_samples=10):
with torch.no_grad():
controller.eval()
shared_cnn.eval()
archs, valid_accs = [], []
loader_iter = iter(xloader)
for i in range(n_samples):
try:
inputs, targets = next(loader_iter)
except:
loader_iter = iter(xloader)
inputs, targets = next(loader_iter)
_, _, sampled_arch = controller()
arch = shared_cnn.module.update_arch(sampled_arch)
_, logits = shared_cnn(inputs)
val_top1, val_top5 = obtain_accuracy(logits.cpu().data,
targets.data,
topk=(1, 5))
archs.append(arch)
valid_accs.append(val_top1.item())
best_idx = np.argmax(valid_accs)
best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
return best_arch, best_valid_acc
def search_find_best(xloader, network, n_samples):
with torch.no_grad():
network.eval()
archs, valid_accs = [], []
#print ('obtain the top-{:} architectures'.format(n_samples))
loader_iter = iter(xloader)
for i in range(n_samples):
arch = network.module.random_genotype(True)
try:
inputs, targets = next(loader_iter)
except:
loader_iter = iter(xloader)
inputs, targets = next(loader_iter)
_, logits = network(inputs)
val_top1, val_top5 = obtain_accuracy(logits.cpu().data,
targets.data,
topk=(1, 5))
archs.append(arch)
valid_accs.append(val_top1.item())
best_idx = np.argmax(valid_accs)
best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
return best_arch, best_valid_acc
def get_best_arch(xloader, network, n_samples):
with torch.no_grad():
network.eval()
archs, valid_accs = network.module.return_topK(n_samples), []
#print ('obtain the top-{:} architectures'.format(n_samples))
loader_iter = iter(xloader)
for i, sampled_arch in enumerate(archs):
network.module.set_cal_mode('dynamic', sampled_arch)
try:
inputs, targets = next(loader_iter)
except:
loader_iter = iter(xloader)
inputs, targets = next(loader_iter)
_, logits = network(inputs)
val_top1, val_top5 = obtain_accuracy(logits.cpu().data,
targets.data,
topk=(1, 5))
valid_accs.append(val_top1.item())
#print ('--- {:}/{:} : {:} : {:}'.format(i, len(archs), sampled_arch, val_top1))
best_idx = np.argmax(valid_accs)
best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
return best_arch, best_valid_acc
def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
data_time, batch_time, batch = AverageMeter(), AverageMeter(), None
losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
latencies, device = [], torch.cuda.current_device()
network.eval()
with torch.no_grad():
end = time.time()
for i, (inputs, targets) in enumerate(xloader):
targets = targets.cuda(device=device, non_blocking=True)
inputs = inputs.cuda(device=device, non_blocking=True)
data_time.update(time.time() - end)
# forward
features, logits = network(inputs)
loss = criterion(logits, targets)
batch_time.update(time.time() - end)
if batch is None or batch == inputs.size(0):
batch = inputs.size(0)
latencies.append(batch_time.val - data_time.val)
# record loss and accuracy
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))
end = time.time()
if len(latencies) > 2: latencies = latencies[1:]
return losses.avg, top1.avg, top5.avg, latencies
def train_shared_cnn(xloader, shared_cnn, criterion, scheduler, optimizer,
print_freq, logger, config, start_epoch):
# start training
start_time, epoch_time, total_epoch = time.time(), AverageMeter(
), config.epochs + config.warmup
for epoch in range(start_epoch, total_epoch):
scheduler.update(epoch, 0.0)
need_time = 'Time Left: {:}'.format(
convert_secs2time(epoch_time.val * (total_epoch - epoch), True))
epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
logger.log('\n[Traing the {:}-th epoch] {:}, LR={:}'.format(
epoch_str, need_time, min(scheduler.get_lr())))
data_time, batch_time = AverageMeter(), AverageMeter()
losses, top1s, top5s, xend = AverageMeter(), AverageMeter(
), AverageMeter(), time.time()
shared_cnn.train()
for step, (inputs, targets) in enumerate(xloader):
scheduler.update(None, 1.0 * step / len(xloader))
targets = targets.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - xend)
optimizer.zero_grad()
_, logits = shared_cnn(inputs)
loss = criterion(logits, targets)
loss.backward()
torch.nn.utils.clip_grad_norm_(shared_cnn.parameters(), 5)
optimizer.step()
# record
prec1, prec5 = obtain_accuracy(logits.data,
targets.data,
topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1s.update(prec1.item(), inputs.size(0))
top5s.update(prec5.item(), inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - xend)
xend = time.time()
if step % print_freq == 0 or step + 1 == len(xloader):
Sstr = '*Train-Shared-CNN* ' + time_string(
) + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step,
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)
Wstr = '[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=top1s, top5=top5s)
logger.log(Sstr + ' ' + Tstr + ' ' + Wstr)
cnn_loss, cnn_top1, cnn_top5 = losses.avg, top1s.avg, top5s.avg
logger.log(
'[{:}] shared-cnn : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'
.format(epoch_str, cnn_loss, cnn_top1, cnn_top5))
epoch_time.update(time.time() - start_time)
start_time = time.time()
return
def procedure(xloader, network, criterion, scheduler, optimizer, mode: str):
losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
if mode == 'train':
network.train()
elif mode == 'valid':
network.eval()
else:
raise ValueError("The mode is not right : {:}".format(mode))
device = torch.cuda.current_device()
data_time, batch_time, end = AverageMeter(), AverageMeter(), time.time()
for i, (inputs, targets) in enumerate(xloader):
if mode == 'train': scheduler.update(None, 1.0 * i / len(xloader))
targets = targets.cuda(device=device, non_blocking=True)
if mode == 'train': optimizer.zero_grad()
# forward
features, logits = network(inputs)
loss = criterion(logits, targets)
# backward
if mode == 'train':
loss.backward()
optimizer.step()
# record loss and accuracy
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))
# count time
batch_time.update(time.time() - end)
end = time.time()
return losses.avg, top1.avg, top5.avg, batch_time.sum
def get_best_arch(xloader, network, n_samples):
with torch.no_grad():
network.eval()
archs, valid_accs = [], []
loader_iter = iter(xloader)
for i in range(n_samples):
try:
inputs, targets = next(loader_iter)
except:
loader_iter = iter(xloader)
inputs, targets = next(loader_iter)
sampled_arch = network.module.dync_genotype(False)
network.module.set_cal_mode('dynamic', sampled_arch)
_, logits = network(inputs)
val_top1, val_top5 = obtain_accuracy(logits.cpu().data,
targets.data,
topk=(1, 5))
archs.append(sampled_arch)
valid_accs.append(val_top1.item())
best_idx = np.argmax(valid_accs)
best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
return best_arch, best_valid_acc
def get_best_arch(xloader, network, n_samples, algo):
with torch.no_grad():
network.eval()
if algo == 'random':
archs, valid_accs = network.return_topK(n_samples, True), []
elif algo == 'setn':
archs, valid_accs = network.return_topK(n_samples, False), []
elif algo.startswith('darts') or algo == 'gdas':
arch = network.genotype
archs, valid_accs = [arch], []
elif algo == 'enas':
archs, valid_accs = [], []
for _ in range(n_samples):
_, _, sampled_arch = network.controller()
archs.append(sampled_arch)
else:
raise ValueError('Invalid algorithm name : {:}'.format(algo))
loader_iter = iter(xloader)
for i, sampled_arch in enumerate(archs):
network.set_cal_mode('dynamic', sampled_arch)
try:
inputs, targets = next(loader_iter)
except:
loader_iter = iter(xloader)
inputs, targets = next(loader_iter)
_, logits = network(inputs.cuda(non_blocking=True))
val_top1, val_top5 = obtain_accuracy(logits.cpu().data,
targets.data,
topk=(1, 5))
valid_accs.append(val_top1.item())
best_idx = np.argmax(valid_accs)
best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
return best_arch, best_valid_acc
def valid_func(xloader, network, criterion):
data_time, batch_time = AverageMeter(), AverageMeter()
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(
), AverageMeter()
network.eval()
end = time.time()
with torch.no_grad():
for step, (arch_inputs, arch_targets) in enumerate(xloader):
arch_targets = arch_targets.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - end)
# prediction
_, logits = network(arch_inputs)
arch_loss = criterion(logits, arch_targets)
# record
arch_prec1, arch_prec5 = obtain_accuracy(logits.data,
arch_targets.data,
topk=(1, 5))
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
return arch_losses.avg, arch_top1.avg, arch_top5.avg
def train_shared_cnn(
xloader,
shared_cnn,
controller,
criterion,
scheduler,
optimizer,
epoch_str,
print_freq,
logger,
):
data_time, batch_time = AverageMeter(), AverageMeter()
losses, top1s, top5s, xend = (
AverageMeter(),
AverageMeter(),
AverageMeter(),
time.time(),
)
shared_cnn.train()
controller.eval()
for step, (inputs, targets) in enumerate(xloader):
scheduler.update(None, 1.0 * step / len(xloader))
targets = targets.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - xend)
with torch.no_grad():
_, _, sampled_arch = controller()
optimizer.zero_grad()
shared_cnn.module.update_arch(sampled_arch)
_, logits = shared_cnn(inputs)
loss = criterion(logits, targets)
loss.backward()
torch.nn.utils.clip_grad_norm_(shared_cnn.parameters(), 5)
optimizer.step()
# record
prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1s.update(prec1.item(), inputs.size(0))
top5s.update(prec5.item(), inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - xend)
xend = time.time()
if step % print_freq == 0 or step + 1 == len(xloader):
Sstr = (
"*Train-Shared-CNN* " + time_string() +
" [{:}][{:03d}/{:03d}]".format(epoch_str, step, 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)
Wstr = "[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=top1s, top5=top5s)
logger.log(Sstr + " " + Tstr + " " + Wstr)
return losses.avg, top1s.avg, top5s.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
def train_shared_cnn(xloader, shared_cnn, controller, criterion, scheduler,
optimizer, epoch_str, print_freq, logger):
data_time, batch_time = AverageMeter(), AverageMeter()
losses, top1s, top5s, xend = AverageMeter(), AverageMeter(), AverageMeter(
), time.time()
shared_cnn.train()
controller.eval()
ne = 10
for ni in range(ne):
with torch.no_grad():
_, _, sampled_arch = controller()
shared_cnn.module.update_arch(sampled_arch)
print(sampled_arch)
# arch_str = op_list2str(sampled_arch)
for step, (inputs, targets) in enumerate(xloader):
# print(step,inputs,targets)
scheduler.update(None, 1.0 * step / len(xloader))
targets = targets.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - xend)
optimizer.zero_grad()
_, logits = shared_cnn(inputs)
loss = criterion(logits, targets)
loss.backward()
torch.nn.utils.clip_grad_norm_(shared_cnn.parameters(), 5)
optimizer.step()
# record
prec1, prec5 = obtain_accuracy(logits.data,
targets.data,
topk=(1, 2))
losses.update(loss.item(), inputs.size(0))
top1s.update(prec1.item(), inputs.size(0))
top5s.update(prec5.item(), inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - xend)
xend = time.time()
# if step + 1 == len(xloader):
Sstr = '*Train-Shared-CNN* ' + time_string() + ' [{:03d}/10]'.format(
ni, ne)
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)
Wstr = '[Loss {loss.avg:.3f} Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f}]'.format(
loss=losses, top1=top1s, top5=top5s)
losses.reset()
top1s.reset()
top5s.reset()
logger.log(Sstr + ' ' + Tstr + ' ' + Wstr)
return losses.avg, top1s.avg, top5s.avg
def valid_fn(network, arch, args):
network.arch_cache = arch
train_bn(iter(train_loader), network, bn_iter)
network.eval()
try:
inputs, targets = next(loader_iter)
except:
loader_iter = iter(xloader)
inputs, targets = next(loader_iter)
inputs, targets = inputs.cuda(non_blocking=True), targets.cuda(
non_blocking=True)
_, logits = network(inputs)
val_top1 = obtain_accuracy(logits, targets.data)[0]
return val_top1
def test_archi_acc(self, arch):
if self.train_loader is not None:
self.model.apply(ResetRunningStats)
self.model.train()
for step, (data, target) in enumerate(self.train_loader):
# print('train step: {} total: {}'.format(step,max_train_iters))
# data, target = train_dataprovider.next()
# print('get data',data.shape)
#data = data.cuda()
output = self.model.forward(data, arch) #_with_architect
del data, target, output
base_top1, base_top5 = AverageMeter(), AverageMeter()
self.model.eval()
one_batch = None
for step, (data, target) in enumerate(self.val_loader):
# print('test step: {} total: {}'.format(step,max_test_iters))
if one_batch == None:
one_batch = data
batchsize = data.shape[0]
# print('get data',data.shape)
target = target.cuda(non_blocking=True)
#data, target = data.to(device), target.to(device)
_, logits = self.model.forward(data, arch) #_with_architect
prec1, prec5 = obtain_accuracy(logits.data,
target.data,
topk=(1, 5))
base_top1.update(prec1.item(), batchsize)
base_top5.update(prec5.item(), batchsize)
del data, target, logits, prec1, prec5
if self.lambda_t > 0.0:
start_time = time.time()
len_batch = min(len(one_batch), 50)
for i in range(len_batch):
_, _ = self.model.forward(one_batch[i:i + 1, :, :, :], arch)
end_time = time.time()
time_per = (end_time - start_time) / len_batch
else:
time_per = 0.0
#print('top1: {:.2f} top5: {:.2f}'.format(base_top1.avg * 100, base_top5.avg * 100))
return base_top1.avg, base_top5.avg, time_per
def get_arch_acc(xloader, network, sampled_arch):
with torch.no_grad():
network.eval()
loader_iter = iter(xloader)
network.module.set_cal_mode('dynamic', sampled_arch)
try:
inputs, targets = next(loader_iter)
except:
loader_iter = iter(xloader)
inputs, targets = next(loader_iter)
_, logits = network(inputs)
val_top1, val_top5 = obtain_accuracy(logits.cpu().data,
targets.data,
topk=(1, 5))
return val_top1.item()
def search_func(xloader, network, criterion, scheduler, w_optimizer, epoch_str,
print_freq, logger):
data_time, batch_time = AverageMeter(), AverageMeter()
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(
), AverageMeter()
network.train()
end = time.time()
for step, (base_inputs, base_targets, arch_inputs,
arch_targets) in enumerate(xloader):
scheduler.update(None, 1.0 * step / len(xloader))
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
network.module.random_genotype(True)
w_optimizer.zero_grad()
_, logits = network(base_inputs)
base_loss = criterion(logits, base_targets)
base_loss.backward()
nn.utils.clip_grad_norm_(network.parameters(), 5)
w_optimizer.step()
# record
base_prec1, base_prec5 = obtain_accuracy(logits.data,
base_targets.data,
topk=(1, 5))
base_losses.update(base_loss.item(), base_inputs.size(0))
base_top1.update(base_prec1.item(), base_inputs.size(0))
base_top5.update(base_prec5.item(), base_inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if step % print_freq == 0 or step + 1 == len(xloader):
Sstr = (
"*SEARCH* " + time_string() +
" [{:}][{:03d}/{:03d}]".format(epoch_str, step, 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)
Wstr = "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=base_top1, top5=base_top5)
logger.log(Sstr + " " + Tstr + " " + Wstr)
return base_losses.avg, base_top1.avg, base_top5.avg
def train_supernet(xloader, network, criterion, w_optimizer, epoch_str,
print_freq, logger):
data_time, batch_time = AverageMeter(), AverageMeter()
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(
), AverageMeter()
network.train()
end = time.time()
for step, (X, Y) in enumerate(xloader):
# measure data loading time
X, Y = X.cuda(non_blocking=True), Y.cuda(non_blocking=True)
data_time.update(time.time() - end)
# update the weights
network.random_genotype(True)
w_optimizer.zero_grad()
_, logits = network(X)
base_loss = criterion(logits, Y)
base_loss.backward()
nn.utils.clip_grad_norm_(network.parameters(), 5)
w_optimizer.step()
# record
base_prec1, base_prec5 = obtain_accuracy(logits.data,
Y.data,
topk=(1, 5))
base_losses.update(base_loss.item(), X.size(0))
base_top1.update(base_prec1.item(), X.size(0))
base_top5.update(base_prec5.item(), X.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if step % print_freq == 0 or step + 1 == len(xloader):
Sstr = '*SEARCH* ' + ' [{:}][{:03d}/{:03d}]'.format(
epoch_str, step, 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)
Wstr = '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=base_top1, top5=base_top5)
logger.info(Sstr + ' ' + Tstr + ' ' + Wstr)
return base_losses.avg, base_top1.avg, base_top5.avg
def procedure(xloader, teacher, network, criterion, scheduler, optimizer, mode,
config, extra_info, print_freq, logger):
data_time, batch_time, losses, top1, top5 = AverageMeter(), AverageMeter(
), AverageMeter(), AverageMeter(), AverageMeter()
Ttop1, Ttop5 = AverageMeter(), AverageMeter()
if mode == 'train':
network.train()
elif mode == 'valid':
network.eval()
else:
raise ValueError("The mode is not right : {:}".format(mode))
teacher.eval()
logger.log(
'[{:5s}] config :: auxiliary={:}, KD :: [alpha={:.2f}, temperature={:.2f}]'
.format(mode, config.auxiliary if hasattr(config, 'auxiliary') else -1,
config.KD_alpha, config.KD_temperature))
end = time.time()
for i, (inputs, targets) in enumerate(xloader):
if mode == 'train': scheduler.update(None, 1.0 * i / len(xloader))
# measure data loading time
data_time.update(time.time() - end)
# calculate prediction and loss
targets = targets.cuda(non_blocking=True)
if mode == 'train': optimizer.zero_grad()
student_f, logits = network(inputs)
if isinstance(logits, list):
assert len(
logits
) == 2, 'logits must has {:} items instead of {:}'.format(
2, len(logits))
logits, logits_aux = logits
else:
logits, logits_aux = logits, None
with torch.no_grad():
teacher_f, teacher_logits = teacher(inputs)
loss = loss_KD_fn(criterion, logits, teacher_logits, student_f,
teacher_f, targets, config.KD_alpha,
config.KD_temperature)
if config is not None and hasattr(
config, 'auxiliary') and config.auxiliary > 0:
loss_aux = criterion(logits_aux, targets)
loss += config.auxiliary * loss_aux
if mode == 'train':
loss.backward()
optimizer.step()
# record
sprec1, sprec5 = obtain_accuracy(logits.data,
targets.data,
topk=(1, 5))
losses.update(loss.item(), inputs.size(0))
top1.update(sprec1.item(), inputs.size(0))
top5.update(sprec5.item(), inputs.size(0))
# teacher
tprec1, tprec5 = obtain_accuracy(teacher_logits.data,
targets.data,
topk=(1, 5))
Ttop1.update(tprec1.item(), inputs.size(0))
Ttop5.update(tprec5.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 = ' {:5s} '.format(
mode.upper()) + time_string() + ' [{:}][{:03d}/{:03d}]'.format(
extra_info, i, len(xloader))
if scheduler is not None:
Sstr += ' {:}'.format(scheduler.get_min_info())
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)
Lstr += ' Teacher : acc@1={:.2f}, acc@5={:.2f}'.format(
Ttop1.avg, Ttop5.avg)
Istr = 'Size={:}'.format(list(inputs.size()))
logger.log(Sstr + ' ' + Tstr + ' ' + Lstr + ' ' + Istr)
logger.log(' **{:5s}** accuracy drop :: @1={:.2f}, @5={:.2f}'.format(
mode.upper(), Ttop1.avg - top1.avg, Ttop5.avg - top5.avg))
logger.log(
' **{mode:5s}** Prec@1 {top1.avg:.2f} Prec@5 {top5.avg:.2f} Error@1 {error1:.2f} Error@5 {error5:.2f} Loss:{loss:.3f}'
.format(mode=mode.upper(),
top1=top1,
top5=top5,
error1=100 - top1.avg,
error5=100 - top5.avg,
loss=losses.avg))
return losses.avg, top1.avg, top5.avg
def search_func(xloader,
network,
criterion,
scheduler,
w_optimizer,
a_optimizer,
epoch_str,
xargs,
logger,
ood_loader=None):
data_time, batch_time = AverageMeter(), AverageMeter()
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(
), AverageMeter()
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(
), AverageMeter()
network.train()
end = time.time()
for step, (base_inputs, base_targets, arch_inputs,
arch_targets) in enumerate(xloader):
base_inputs = base_inputs.cuda(non_blocking=True)
arch_inputs = arch_inputs.cuda(non_blocking=True)
if xargs.adv_outer:
arch_inputs.requires_grad = True
scheduler.update(None, 1.0 * step / len(xloader))
base_targets = base_targets.cuda(non_blocking=True)
arch_targets = arch_targets.cuda(non_blocking=True)
if xargs.ood_inner or xargs.ood_outer:
try:
ood_input, _ = next(ood_loader_iter)
except:
ood_loader_iter = iter(ood_loader)
ood_input, _ = next(ood_loader_iter)
ood_input = ood_input.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - end)
# update the weights
w_optimizer.zero_grad()
_, logits, _, _ = network(base_inputs)
base_loss = criterion(logits, base_targets)
if xargs.ood_inner and ood_loader is not None:
_, ood_logits, _, _ = network(ood_input)
ood_loss = F.kl_div(input=F.log_softmax(ood_logits, dim=-1),
target=torch.ones_like(ood_logits) /
ood_logits.size()[-1])
base_loss += ood_loss
base_loss.backward()
torch.nn.utils.clip_grad_norm_(network.parameters(), 5)
w_optimizer.step()
# record
base_prec1, base_prec5 = obtain_accuracy(logits.data,
base_targets.data,
topk=(1, 5))
base_losses.update(base_loss.item(), base_inputs.size(0))
base_top1.update(base_prec1.item(), base_inputs.size(0))
base_top5.update(base_prec5.item(), base_inputs.size(0))
# update the architecture-weight
a_optimizer.zero_grad()
grads = {}
loss_data = {}
# ---- acc loss ----
_, acc_logits, nop_loss, flp_loss = network(arch_inputs)
acc_loss = criterion(acc_logits, arch_targets)
loss_data['acc'] = acc_loss.item()
grads['acc'] = list(
torch.autograd.grad(acc_loss,
network.get_alphas(),
retain_graph=True))
# del acc_logits
# ---- end ----
# ---- nop loss ----
if xargs.nop_outer:
if xargs.nop_constrain == 'abs':
nop_loss = torch.abs(xargs.nop_constrain_min - nop_loss)
loss_data['nop'] = nop_loss.item()
grads['nop'] = list(
torch.autograd.grad(nop_loss,
network.get_alphas(),
retain_graph=True))
# ---- end ----
# ---- flp loss ----
if xargs.flp_outer:
if xargs.flp_constrain == 'abs':
flp_loss = torch.abs(xargs.flp_constrain_min - flp_loss)
loss_data['flp'] = flp_loss.item()
grads['flp'] = list(
torch.autograd.grad(flp_loss,
network.get_alphas(),
retain_graph=True))
# ---- end ----
# ---- ood loss ----
if xargs.ood_outer and ood_loader is not None:
_, ood_logits, _, _ = network(ood_input)
ood_loss = F.kl_div(input=F.log_softmax(ood_logits),
target=torch.ones_like(ood_logits) /
ood_logits.size()[-1])
loss_data['ood'] = ood_loss.item()
grads['ood'] = list(
torch.autograd.grad(ood_loss,
network.get_alphas(),
retain_graph=True))
del ood_logits
# ---- end ----
# ---- adv loss ----
if xargs.adv_outer:
if xargs.dataset == 'cifar10':
mean = (0.4914, 0.4822, 0.4465)
std = (0.2471, 0.2435, 0.2616)
elif xargs.dataset == 'cifar100':
mean = (0.5071, 0.4867, 0.4408)
std = (0.2675, 0.2565, 0.2761)
mean = torch.FloatTensor(mean).view(3, 1, 1)
std = torch.FloatTensor(std).view(3, 1, 1)
upper_limit = ((1 - mean) / std).cuda()
lower_limit = ((0 - mean) / std).cuda()
epsilon = ((xargs.epsilon / 255.) / std).cuda()
step_size = epsilon * 1.25
delta = (
(torch.rand(arch_inputs.size()) - 0.5) * 2).cuda() * epsilon
adv_grad = torch.autograd.grad(acc_loss,
arch_inputs,
retain_graph=True,
create_graph=False)[0]
adv_grad = adv_grad.detach().data
delta = clamp(delta + step_size * torch.sign(adv_grad), -epsilon,
epsilon)
delta = clamp(delta, lower_limit - arch_inputs.data,
upper_limit - arch_inputs.data)
adv_input = (arch_inputs.data + delta).cuda()
_, adv_logits, _, _ = network(adv_input)
adv_loss = criterion(adv_logits, arch_targets)
loss_data['adv'] = adv_loss.item()
grads['adv'] = list(
torch.autograd.grad(adv_loss,
network.get_alphas(),
retain_graph=True))
del mean, std, upper_limit, lower_limit, epsilon, step_size, delta, adv_grad, adv_input, adv_logits
# ---- end ----
# ---- MGDA ----
gn = gradient_normalizers(
grads, loss_data,
normalization_type=xargs.grad_norm) # loss+, loss, l2
for t in grads:
for gr_i in range(len(grads[t])):
grads[t][gr_i] = grads[t][gr_i] / (gn[t] + 1e-7)
if xargs.MGDA and (len(grads) > 1):
sol, _ = MinNormSolver.find_min_norm_element(
[grads[t] for t in grads])
print(sol) # acc, adv, nop
else:
sol = [1] * len(grads)
arch_loss = 0
for kk, t in enumerate(grads):
if t == 'acc':
arch_loss += float(sol[kk]) * acc_loss
elif t == 'adv':
arch_loss += float(sol[kk]) * adv_loss
elif t == 'nop':
arch_loss += float(sol[kk]) * nop_loss
elif t == 'ood':
arch_loss += float(sol[kk]) * ood_loss
elif t == 'flp':
arch_loss += float(sol[kk]) * flp_loss
# ---- end ----
arch_loss.backward()
a_optimizer.step()
# record
arch_prec1, arch_prec5 = obtain_accuracy(acc_logits.data,
arch_targets.data,
topk=(1, 5))
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
arch_top5.update(arch_prec5.item(), arch_inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if step % xargs.print_freq == 0 or step + 1 == len(xloader):
Sstr = '*SEARCH* ' + time_string(
) + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, 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)
Wstr = '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=base_top1, top5=base_top5)
Astr = 'Arch [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=arch_losses, top1=arch_top1, top5=arch_top5)
logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
def train_controller(xloader, network, criterion, optimizer, prev_baseline,
epoch_str, print_freq, logger):
# config. (containing some necessary arg)
# baseline: The baseline score (i.e. average val_acc) from the previous epoch
data_time, batch_time = AverageMeter(), AverageMeter()
GradnormMeter, LossMeter, ValAccMeter, EntropyMeter, BaselineMeter, RewardMeter, xend = AverageMeter(
), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(
), AverageMeter(), time.time()
controller_num_aggregate = 20
controller_train_steps = 50
controller_bl_dec = 0.99
controller_entropy_weight = 0.0001
network.eval()
network.controller.train()
network.controller.zero_grad()
loader_iter = iter(xloader)
for step in range(controller_train_steps * controller_num_aggregate):
try:
inputs, targets = next(loader_iter)
except:
loader_iter = iter(xloader)
inputs, targets = next(loader_iter)
inputs = inputs.cuda(non_blocking=True)
targets = targets.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - xend)
log_prob, entropy, sampled_arch = network.controller()
with torch.no_grad():
network.set_cal_mode('dynamic', sampled_arch)
_, logits = network(inputs)
val_top1, val_top5 = obtain_accuracy(logits.data,
targets.data,
topk=(1, 5))
val_top1 = val_top1.view(-1) / 100
reward = val_top1 + controller_entropy_weight * entropy
if prev_baseline is None:
baseline = val_top1
else:
baseline = prev_baseline - (1 - controller_bl_dec) * (
prev_baseline - reward)
loss = -1 * log_prob * (reward - baseline)
# account
RewardMeter.update(reward.item())
BaselineMeter.update(baseline.item())
ValAccMeter.update(val_top1.item() * 100)
LossMeter.update(loss.item())
EntropyMeter.update(entropy.item())
# Average gradient over controller_num_aggregate samples
loss = loss / controller_num_aggregate
loss.backward(retain_graph=True)
# measure elapsed time
batch_time.update(time.time() - xend)
xend = time.time()
if (step + 1) % controller_num_aggregate == 0:
grad_norm = torch.nn.utils.clip_grad_norm_(
network.controller.parameters(), 5.0)
GradnormMeter.update(grad_norm)
optimizer.step()
network.controller.zero_grad()
if step % print_freq == 0:
Sstr = '*Train-Controller* ' + time_string(
) + ' [{:}][{:03d}/{:03d}]'.format(
epoch_str, step,
controller_train_steps * controller_num_aggregate)
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)
Wstr = '[Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Reward {reward.val:.2f} ({reward.avg:.2f})] Baseline {basel.val:.2f} ({basel.avg:.2f})'.format(
loss=LossMeter,
top1=ValAccMeter,
reward=RewardMeter,
basel=BaselineMeter)
Estr = 'Entropy={:.4f} ({:.4f})'.format(EntropyMeter.val,
EntropyMeter.avg)
logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Estr)
return LossMeter.avg, ValAccMeter.avg, BaselineMeter.avg, RewardMeter.avg
def search_func(xloader, network, criterion, scheduler, w_optimizer,
a_optimizer, epoch_str, print_freq, algo, logger):
data_time, batch_time = AverageMeter(), AverageMeter()
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(
), AverageMeter()
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(
), AverageMeter()
end = time.time()
network.train()
for step, (base_inputs, base_targets, arch_inputs,
arch_targets) in enumerate(xloader):
scheduler.update(None, 1.0 * step / len(xloader))
base_inputs = base_inputs.cuda(non_blocking=True)
arch_inputs = arch_inputs.cuda(non_blocking=True)
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
if algo == 'setn':
sampled_arch = network.dync_genotype(True)
network.set_cal_mode('dynamic', sampled_arch)
elif algo == 'gdas':
network.set_cal_mode('gdas', None)
elif algo.startswith('darts'):
network.set_cal_mode('joint', None)
elif algo == 'random':
network.set_cal_mode('urs', None)
elif algo == 'enas':
with torch.no_grad():
network.controller.eval()
_, _, sampled_arch = network.controller()
network.set_cal_mode('dynamic', sampled_arch)
else:
raise ValueError('Invalid algo name : {:}'.format(algo))
network.zero_grad()
_, logits = network(base_inputs)
base_loss = criterion(logits, base_targets)
base_loss.backward()
w_optimizer.step()
# record
base_prec1, base_prec5 = obtain_accuracy(logits.data,
base_targets.data,
topk=(1, 5))
base_losses.update(base_loss.item(), base_inputs.size(0))
base_top1.update(base_prec1.item(), base_inputs.size(0))
base_top5.update(base_prec5.item(), base_inputs.size(0))
# update the architecture-weight
if algo == 'setn':
network.set_cal_mode('joint')
elif algo == 'gdas':
network.set_cal_mode('gdas', None)
elif algo.startswith('darts'):
network.set_cal_mode('joint', None)
elif algo == 'random':
network.set_cal_mode('urs', None)
elif algo != 'enas':
raise ValueError('Invalid algo name : {:}'.format(algo))
network.zero_grad()
if algo == 'darts-v2':
arch_loss, logits = backward_step_unrolled(
network, criterion, base_inputs, base_targets, w_optimizer,
arch_inputs, arch_targets)
a_optimizer.step()
elif algo == 'random' or algo == 'enas':
with torch.no_grad():
_, logits = network(arch_inputs)
arch_loss = criterion(logits, arch_targets)
else:
_, logits = network(arch_inputs)
arch_loss = criterion(logits, arch_targets)
arch_loss.backward()
a_optimizer.step()
# record
arch_prec1, arch_prec5 = obtain_accuracy(logits.data,
arch_targets.data,
topk=(1, 5))
arch_losses.update(arch_loss.item(), arch_inputs.size(0))
arch_top1.update(arch_prec1.item(), arch_inputs.size(0))
arch_top5.update(arch_prec5.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(xloader):
Sstr = '*SEARCH* ' + time_string(
) + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, 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)
Wstr = '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=base_top1, top5=base_top5)
Astr = 'Arch [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=arch_losses, top1=arch_top1, top5=arch_top5)
logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
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
arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(
), AverageMeter()
time_start = time.time()
time_pre = time.time()
search_model.eval()
# search_model.eval()
for step, (base_inputs, base_targets) in enumerate(valid_loader):
base_targets = base_targets.cuda(non_blocking=True)
# print('in',base_inputs[0])
# optim.zero_grad()
with torch.no_grad():
_, logits = search_model(base_inputs.cuda())
arch_prec1 = obtain_accuracy(logits.data, base_targets.data)
arch_top1.update(arch_prec1[0])
print('val_acc %.2f used %.2fs' % (arch_top1.avg, time.time() - time_pre))
time_pre = time.time()
reg_lambda = 0.001
for epoch in range(10000):
search_model.train()
for step, (base_inputs, base_targets, arch_inputs,
arch_targets) in enumerate(search_loader):
base_targets = base_targets.cuda(non_blocking=True)
arch_targets = arch_targets.cuda(non_blocking=True)
# print(torch.mean(base_targets.float()))
optim.zero_grad()
def search_find_best(xloader, train_loader, network, predictor, optimizer,
logger, args):
bn_iter = 0 if not args.track_running_stat else args.bn_train_iters
arch_pool = [Architect() for _ in range(args.init_pool_size)]
valid_accs = {}
best_arch, best_acc = None, 100.
# network.eval()
loader_iter = iter(xloader)
while True:
logger.info('arch pool: %d' % len(arch_pool))
# evaluate unseen architectures
with torch.no_grad():
for arch in arch_pool:
# arch_str = arch.struct.to_unique_str(True)
arch_str = arch.struct.tostr()
if arch_str in valid_accs:
continue
network.set_genotype(arch.struct)
if bn_iter > 0:
train_bn(train_loader, network, bn_iter)
network.eval()
arch_top1 = AverageMeter()
try:
inputs, targets = next(loader_iter)
except:
loader_iter = iter(xloader)
inputs, targets = next(loader_iter)
inputs, targets = inputs.cuda(), targets.cuda()
_, logits = network(inputs)
val_top1 = obtain_accuracy(logits, targets.data)[0]
arch_top1.update(val_top1.item(), targets.size(0))
arch_err = 100. - arch_top1.avg
valid_accs[arch_str] = arch_err
if arch_err < best_acc:
best_arch = arch.struct.tostr()
best_acc = arch_err
logger.info("best arch err ever: %2.2f" % best_acc)
logger.info(best_arch)
if len(arch_pool) >= args.max_samples:
break
# train predictor
p_train_data = [
arch2data(
a.struct.tostr(),
# valid_accs[a.struct.to_unique_str(True)])
valid_accs[a.struct.tostr()]) for a in arch_pool
]
p_train_queue = gd.DataListLoader(p_train_data,
args.p_batch_size,
shuffle=True)
for epoch in range(args.p_epochs):
# train_predictor(predictor, p_train_data, optimizer, p_batch_size)
predictor.fit(p_train_queue, optimizer, 0, None)
# grad search
checker = lambda arch: arch.struct.tostr() not in valid_accs
new_trace = predictor.grad_step_on_archs(arch_pool,
args.step_batch_size,
args.step_size, checker)
new_trace = sorted(new_trace, key=itemgetter(1))[:args.new_pop_limit]
# new_trace = sorted(new_trace, key=itemgetter(1))[:args.max_samples]
arch_pool = (arch_pool +
list(map(itemgetter(0), new_trace)))[:args.max_samples]
return best_arch, best_acc
def train_func(xloader, network, criterion, scheduler, w_optimizer, epoch_str,
print_freq, archs, arch_iter, logger):
data_time, batch_time = AverageMeter(), AverageMeter()
base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(
), AverageMeter()
val_losses, val_top1, val_top5 = AverageMeter(), AverageMeter(
), AverageMeter()
network.train()
end = time.time()
for step, (base_inputs, base_targets, val_inputs,
val_targets) in enumerate(xloader):
scheduler.update(None, 1.0 * step / len(xloader))
try:
arch = next(arch_iter)
except:
arch_iter = iter(archs)
arch = next(arch_iter)
base_inputs = base_inputs.cuda()
base_targets = base_targets.cuda(non_blocking=True)
val_inputs = val_inputs.cuda()
val_targets = val_targets.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - end)
# update the weights
w_optimizer.zero_grad()
_, logits, _ = network(base_inputs) #, arch)
base_loss = criterion(logits, base_targets)
base_loss.backward()
torch.nn.utils.clip_grad_norm_(network.parameters(), 5)
w_optimizer.step()
# record
base_prec1, base_prec5 = obtain_accuracy(logits.data,
base_targets.data,
topk=(1, 5))
base_losses.update(base_loss.item(), base_inputs.size(0))
base_top1.update(base_prec1.item(), base_inputs.size(0))
base_top5.update(base_prec5.item(), base_inputs.size(0))
# validate arch
_, logits, _ = network(val_inputs, arch)
val_loss = criterion(logits, val_targets)
# record
val_prec1, val_prec5 = obtain_accuracy(logits.data,
val_targets.data,
topk=(1, 5))
val_losses.update(val_loss.item(), val_inputs.size(0))
val_top1.update(val_prec1.item(), val_inputs.size(0))
val_top5.update(val_prec5.item(), val_inputs.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if step % print_freq == 0 or step + 1 == len(xloader):
Sstr = '*TRAIN* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(
epoch_str, step, 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)
Wstr = '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=base_top1, top5=base_top5)
Astr = 'Val [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=val_losses, top1=val_top1, top5=val_top5)
logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
return base_losses.avg, base_top1.avg, base_top5.avg, val_losses.avg, val_top1.avg, val_top5.avg, arch_iter
def main(xargs):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.set_num_threads(xargs.workers)
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
train_data, valid_data, xshape, class_num = get_datasets(
xargs.dataset, xargs.data_path, -1)
#config_path = 'configs/nas-benchmark/algos/GDAS.config'
config = load_config(xargs.config_path, {
'class_num': class_num,
'xshape': xshape
}, logger)
search_loader, _, valid_loader = get_nas_search_loaders(
train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/',
config.batch_size, xargs.workers)
logger.log(
'||||||| {:10s} ||||||| Search-Loader-Num={:}, batch size={:}'.format(
xargs.dataset, len(search_loader), config.batch_size))
logger.log('||||||| {:10s} ||||||| Config={:}'.format(
xargs.dataset, config))
search_space = get_search_spaces('cell', xargs.search_space_name)
if xargs.model_config is None and not args.constrain:
model_config = dict2config(
{
'name': 'GDAS',
'C': xargs.channel,
'N': xargs.num_cells,
'max_nodes': xargs.max_nodes,
'num_classes': class_num,
'space': search_space,
'inp_size': 0,
'affine': False,
'track_running_stats': bool(xargs.track_running_stats)
}, None)
elif xargs.model_config is None:
model_config = dict2config(
{
'name': 'GDAS',
'C': xargs.channel,
'N': xargs.num_cells,
'max_nodes': xargs.max_nodes,
'num_classes': class_num,
'space': search_space,
'inp_size': 32,
'affine': False,
'track_running_stats': bool(xargs.track_running_stats)
}, None)
else:
model_config = load_config(
xargs.model_config, {
'num_classes': class_num,
'space': search_space,
'affine': False,
'track_running_stats': bool(xargs.track_running_stats)
}, None)
search_model = get_cell_based_tiny_net(model_config)
#logger.log('search-model :\n{:}'.format(search_model))
logger.log('model-config : {:}'.format(model_config))
w_optimizer, w_scheduler, criterion = get_optim_scheduler(
search_model.get_weights(), config)
a_optimizer = torch.optim.Adam(search_model.get_alphas(),
lr=xargs.arch_learning_rate,
betas=(0.5, 0.999),
weight_decay=xargs.arch_weight_decay)
logger.log('w-optimizer : {:}'.format(w_optimizer))
logger.log('a-optimizer : {:}'.format(a_optimizer))
logger.log('w-scheduler : {:}'.format(w_scheduler))
logger.log('criterion : {:}'.format(criterion))
flop, param = get_model_infos(search_model, xshape)
#logger.log('{:}'.format(search_model))
logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
logger.log('search-space [{:} ops] : {:}'.format(len(search_space),
search_space))
if xargs.arch_nas_dataset is None:
api = None
else:
api = API(xargs.arch_nas_dataset)
logger.log('{:} create API = {:} done'.format(time_string(), api))
last_info, model_base_path, model_best_path = logger.path(
'info'), logger.path('model'), logger.path('best')
network, criterion = torch.nn.DataParallel(
search_model).cuda(), criterion.cuda()
#network, criterion = search_model.cuda(), criterion.cuda()
if last_info.exists(): # automatically resume from previous checkpoint
logger.log("=> loading checkpoint of the last-info '{:}' start".format(
last_info))
last_info = torch.load(last_info)
start_epoch = last_info['epoch']
checkpoint = torch.load(last_info['last_checkpoint'])
genotypes = checkpoint['genotypes']
valid_accuracies = checkpoint['valid_accuracies']
search_model.load_state_dict(checkpoint['search_model'])
w_scheduler.load_state_dict(checkpoint['w_scheduler'])
w_optimizer.load_state_dict(checkpoint['w_optimizer'])
a_optimizer.load_state_dict(checkpoint['a_optimizer'])
logger.log(
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch."
.format(last_info, start_epoch))
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
start_epoch, valid_accuracies, genotypes = 0, {
'best': -1
}, {
-1: search_model.genotype()
}
# start training
start_time, search_time, epoch_time, total_epoch = time.time(
), AverageMeter(), AverageMeter(), config.epochs + config.warmup
sampled_weights = []
for epoch in range(start_epoch, total_epoch + config.t_epochs):
w_scheduler.update(epoch, 0.0)
need_time = 'Time Left: {:}'.format(
convert_secs2time(
epoch_time.val * (total_epoch - epoch + config.t_epochs),
True))
epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
search_model.set_tau(xargs.tau_max -
(xargs.tau_max - xargs.tau_min) * epoch /
(total_epoch - 1))
logger.log('\n[Search the {:}-th epoch] {:}, tau={:}, LR={:}'.format(
epoch_str, need_time, search_model.get_tau(),
min(w_scheduler.get_lr())))
if epoch < total_epoch:
search_w_loss, search_w_top1, search_w_top5, valid_a_loss , valid_a_top1 , valid_a_top5 \
= search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger, xargs.bilevel)
else:
search_w_loss, search_w_top1, search_w_top5, valid_a_loss , valid_a_top1 , valid_a_top5, arch_iter \
= train_func(search_loader, network, criterion, w_scheduler, w_optimizer, epoch_str, xargs.print_freq, sampled_weights[0], arch_iter, logger)
search_time.update(time.time() - start_time)
logger.log(
'[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s'
.format(epoch_str, search_w_loss, search_w_top1, search_w_top5,
search_time.sum))
logger.log(
'[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'
.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
if (epoch + 1) % 50 == 0 and not config.t_epochs:
weights = search_model.sample_weights(100)
sampled_weights.append(weights)
elif (epoch + 1) == total_epoch and config.t_epochs:
weights = search_model.sample_weights(100)
sampled_weights.append(weights)
arch_iter = iter(weights)
# validate with single arch
single_weight = search_model.sample_weights(1)[0]
single_valid_acc = AverageMeter()
network.eval()
for i in range(10):
try:
val_input, val_target = next(valid_iter)
except Exception as e:
valid_iter = iter(valid_loader)
val_input, val_target = next(valid_iter)
n_val = val_input.size(0)
with torch.no_grad():
val_target = val_target.cuda(non_blocking=True)
_, logits, _ = network(val_input, weights=single_weight)
val_acc1, val_acc5 = obtain_accuracy(logits.data,
val_target.data,
topk=(1, 5))
single_valid_acc.update(val_acc1.item(), n_val)
logger.log('[{:}] valid : accuracy = {:.2f}'.format(
epoch_str, single_valid_acc.avg))
# check the best accuracy
valid_accuracies[epoch] = valid_a_top1
if valid_a_top1 > valid_accuracies['best']:
valid_accuracies['best'] = valid_a_top1
genotypes['best'] = search_model.genotype()
find_best = True
else:
find_best = False
if epoch < total_epoch:
genotypes[epoch] = search_model.genotype()
logger.log('<<<--->>> The {:}-th epoch : {:}'.format(
epoch_str, genotypes[epoch]))
# save checkpoint
save_path = save_checkpoint(
{
'epoch': epoch + 1,
'args': deepcopy(xargs),
'search_model': search_model.state_dict(),
'w_optimizer': w_optimizer.state_dict(),
'a_optimizer': a_optimizer.state_dict(),
'w_scheduler': w_scheduler.state_dict(),
'genotypes': genotypes,
'valid_accuracies': valid_accuracies
}, model_base_path, logger)
last_info = save_checkpoint(
{
'epoch': epoch + 1,
'args': deepcopy(args),
'last_checkpoint': save_path,
}, logger.path('info'), logger)
if find_best:
logger.log(
'<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'
.format(epoch_str, valid_a_top1))
copy_checkpoint(model_base_path, model_best_path, logger)
with torch.no_grad():
logger.log('{:}'.format(search_model.show_alphas()))
if api is not None and epoch < total_epoch:
logger.log('{:}'.format(api.query_by_arch(genotypes[epoch])))
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
network.eval()
# Evaluate the architectures sampled throughout the search
for i in range(len(sampled_weights) - 1):
logger.log('Sample eval : epoch {}'.format((i + 1) * 50 - 1))
for w in sampled_weights[i]:
sample_valid_acc = AverageMeter()
for i in range(10):
try:
val_input, val_target = next(valid_iter)
except Exception as e:
valid_iter = iter(valid_loader)
val_input, val_target = next(valid_iter)
n_val = val_input.size(0)
with torch.no_grad():
val_target = val_target.cuda(non_blocking=True)
_, logits, _ = network(val_input, weights=w)
val_acc1, val_acc5 = obtain_accuracy(logits.data,
val_target.data,
topk=(1, 5))
sample_valid_acc.update(val_acc1.item(), n_val)
w_gene = search_model.genotype(w)
if api is not None:
ind = api.query_index_by_arch(w_gene)
info = api.query_meta_info_by_index(ind)
metrics = info.get_metrics('cifar10', 'ori-test')
acc = metrics['accuracy']
else:
acc = 0.0
logger.log(
'sample valid : val_acc = {:.2f} test_acc = {:.2f}'.format(
sample_valid_acc.avg, acc))
# Evaluate the final sampling separately to find the top 10 architectures
logger.log('Final sample eval')
final_archs = []
for w in sampled_weights[-1]:
sample_valid_acc = AverageMeter()
for i in range(10):
try:
val_input, val_target = next(valid_iter)
except Exception as e:
valid_iter = iter(valid_loader)
val_input, val_target = next(valid_iter)
n_val = val_input.size(0)
with torch.no_grad():
val_target = val_target.cuda(non_blocking=True)
_, logits, _ = network(val_input, weights=w)
val_acc1, val_acc5 = obtain_accuracy(logits.data,
val_target.data,
topk=(1, 5))
sample_valid_acc.update(val_acc1.item(), n_val)
w_gene = search_model.genotype(w)
if api is not None:
ind = api.query_index_by_arch(w_gene)
info = api.query_meta_info_by_index(ind)
metrics = info.get_metrics('cifar10', 'ori-test')
acc = metrics['accuracy']
else:
acc = 0.0
logger.log('sample valid : val_acc = {:.2f} test_acc = {:.2f}'.format(
sample_valid_acc.avg, acc))
final_archs.append((w, sample_valid_acc.avg))
top_10 = sorted(final_archs, key=lambda x: x[1], reverse=True)[:10]
# Evaluate the top 10 architectures on the entire validation set
logger.log('Evaluating top archs')
for w, prev_acc in top_10:
full_valid_acc = AverageMeter()
for val_input, val_target in valid_loader:
n_val = val_input.size(0)
with torch.no_grad():
val_target = val_target.cuda(non_blocking=True)
_, logits, _ = network(val_input, weights=w)
val_acc1, val_acc5 = obtain_accuracy(logits.data,
val_target.data,
topk=(1, 5))
full_valid_acc.update(val_acc1.item(), n_val)
w_gene = search_model.genotype(w)
logger.log('genotype {}'.format(w_gene))
if api is not None:
ind = api.query_index_by_arch(w_gene)
info = api.query_meta_info_by_index(ind)
metrics = info.get_metrics('cifar10', 'ori-test')
acc = metrics['accuracy']
else:
acc = 0.0
logger.log(
'full valid : val_acc = {:.2f} test_acc = {:.2f} pval_acc = {:.2f}'
.format(full_valid_acc.avg, acc, prev_acc))
logger.log('\n' + '-' * 100)
# check the performance from the architecture dataset
logger.log(
'GDAS : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(
total_epoch, search_time.sum, genotypes[total_epoch - 1]))
if api is not None:
logger.log('{:}'.format(api.query_by_arch(genotypes[total_epoch - 1])))
logger.close()
