def infer(self, graph, criterion, valid_queue, *args, **kwargs):
try:
config = kwargs.get('config', graph.config)
device = kwargs['device']
except:
raise ('No configuration specified in graph or kwargs')
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
graph.eval()
with torch.no_grad():
for step, (input, target) in enumerate(valid_queue):
input = input.to(device)
target = target.to(device, non_blocking=True)
# logits, _ = graph(input)
logits = graph(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data.item(), n)
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
if step % config.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg,
top1.avg, top5.avg)
return top1.avg, objs.avg
def train(self, epoch, graph, optimizer, criterion, train_queue,
valid_queue, *args, **kwargs):
try:
config = kwargs.get('config', graph.config)
device = kwargs['device']
arch_optimizer = kwargs['arch_optimizer']
except Exception as e:
raise ModuleNotFoundError(
'No configuration specified in graph or kwargs')
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
# Adjust arch optimizer for new search epoch
arch_optimizer.new_epoch(epoch)
start_time = time.time()
for step, (input_train, target_train) in enumerate(train_queue):
graph.train()
n = input_train.size(0)
input_train = input_train.to(device)
target_train = target_train.to(device, non_blocking=True)
# Architecture update
arch_optimizer.forward_pass_adjustment()
input_valid, target_valid = next(iter(valid_queue))
input_valid = input_valid.to(device)
target_valid = target_valid.to(device, non_blocking=True)
arch_optimizer.step(graph, criterion, input_train, target_train,
input_valid, target_valid, self.lr,
self.optimizer, config.unrolled)
optimizer.zero_grad()
# OP-weight update
arch_optimizer.forward_pass_adjustment()
logits = graph(input_train)
loss = criterion(logits, target_train)
loss.backward()
nn.utils.clip_grad_norm_(graph.parameters(), config.grad_clip)
optimizer.step()
prec1, prec5 = utils.accuracy(logits, target_train, topk=(1, 5))
objs.update(loss.data.item(), n)
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
if step % config.report_freq == 0:
arch_key = list(
arch_optimizer.architectural_weights.keys())[-1]
logging.info('train %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
end_time = time.time()
return top1.avg, objs.avg, end_time - start_time
def train_batch(self, arch):
if self.steps % len(self.train_queue) == 0:
self.scheduler.step()
self.objs = utils.AvgrageMeter()
self.top1 = utils.AvgrageMeter()
self.top5 = utils.AvgrageMeter()
lr = self.scheduler.get_lr()[0]
weights = self.get_weights_from_arch(arch)
self.set_arch_model_weights(weights)
step = self.steps % len(self.train_queue)
input, target = next(self.train_iter)
self.model.train()
n = input.size(0)
input = input.cuda()
target = target.cuda(non_blocking=True)
# get a random_ws minibatch from the search queue with replacement
self.optimizer.zero_grad()
logits = self.model(input, discrete=True)
loss = self.criterion(logits, target)
loss.backward()
nn.utils.clip_grad_norm(self.model.parameters(), self.args.grad_clip)
self.optimizer.step()
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
self.objs.update(loss.data.item(), n)
self.top1.update(prec1.data.item(), n)
self.top5.update(prec5.data.item(), n)
if step % self.args.report_freq == 0:
logging.info('train %03d %e %f %f', step, self.objs.avg, self.top1.avg, self.top5.avg)
self.steps += 1
if self.steps % len(self.train_queue) == 0:
# Save the model weights
self.epochs += 1
self.train_iter = iter(self.train_queue)
valid_err = self.evaluate(arch)
logging.info('epoch %d | train_acc %f | valid_acc %f' % (self.epochs, self.top1.avg, 1 - valid_err))
self.save(epoch=self.epochs)
def train(self, epoch, graph, optimizer, criterion, train_queue,
valid_queue, *args, **kwargs):
try:
config = kwargs.get('config', graph.config)
device = kwargs['device']
except Exception as e:
raise ModuleNotFoundError(
'No configuration specified in graph or kwargs')
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
start_time = time.time()
for step, (input, target) in enumerate(train_queue):
graph.train()
n = input.size(0)
input = input.to(device)
target = target.to(device, non_blocking=True)
optimizer.zero_grad()
# logits, logits_aux = graph(input)
logits = graph(input)
loss = criterion(logits, target)
# if config.auxiliary:
# loss_aux = criterion(logits_aux, target)
# loss += config.auxiliary_weight * loss_aux
loss.backward()
nn.utils.clip_grad_norm_(graph.parameters(), config.grad_clip)
optimizer.step()
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
objs.update(loss.data.item(), n)
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
if step % config.report_freq == 0:
logging.info('train %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
end_time = time.time()
return top1.avg, objs.avg, end_time - start_time
def evaluate_test(self, arch, split=None, discrete=False, normalize=True):
# Return error since we want to minimize obj val
logging.info(arch)
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
weights = self.get_weights_from_arch(arch)
self.set_arch_model_weights(weights)
self.model.eval()
if split is None:
n_batches = 10
else:
n_batches = len(self.test_queue)
for step in range(n_batches):
try:
input, target = next(self.test_iter)
except Exception as e:
logging.info('looping back over valid set')
self.test_iter = iter(self.test_queue)
input, target = next(self.test_iter)
input = input.cuda()
target = target.cuda(non_blocking=True)
logits = self.model(input, discrete=discrete, normalize=normalize)
loss = self.criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data.item(), n)
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
if step % self.args.report_freq == 0:
logging.info('test %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
return 1 - 0.01 * top1.avg