def infer_epoch(self, valid_queue, model, criterion, device):
expect(self._is_setup, "trainer.setup should be called first")
objs = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
objective_perfs = utils.OrderedStats()
model.eval()
context = torch.no_grad if self.eval_no_grad else nullcontext
with context():
for step, (inputs, target) in enumerate(valid_queue):
inputs = inputs.to(device)
target = target.to(device)
logits = model(inputs)
loss = criterion(logits, target)
perfs = self._perf_func(inputs, logits, target, model)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = inputs.size(0)
objective_perfs.update(dict(zip(self._perf_names, perfs)), n=n)
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
if step % self.report_every == 0:
self.logger.info("valid %03d %e %f %f %s", step, objs.avg, top1.avg, top5.avg,
"; ".join(["{}: {:.3f}".format(perf_n, v) \
for perf_n, v in objective_perfs.avgs().items()]))
return top1.avg, objs.avg, objective_perfs.avgs()
def _candnet_perf_use_param(
cand_net, params, data, loss_criterion, forward_kwargs, return_outputs=False
):
# TODO: only support cnn now. because directly use utils.accuracy
# should use self._report_loss_funcs instead maybe
outputs = cand_net.forward_with_params(
data[0], params=params, **forward_kwargs, mode="train"
)
loss = loss_criterion(data[0], outputs, data[1])
acc = utils.accuracy(outputs, data[1], topk=(1,))[0]
if return_outputs:
return outputs, loss, acc
return loss, acc
def train_epoch(self, train_queue, model, criterion, optimizer, device,
epoch):
expect(self._is_setup, "trainer.setup should be called first")
objs = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
model.train()
for step, (inputs, target) in enumerate(train_queue):
inputs = inputs.to(device)
target = target.to(device)
optimizer.zero_grad()
if self.auxiliary_head: # assume model return two logits in train mode
logits, logits_aux = model(inputs)
loss = self._obj_loss(
inputs,
logits,
target,
model,
add_evaluator_regularization=self.add_regularization)
loss_aux = criterion(logits_aux, target)
loss += self.auxiliary_weight * loss_aux
else:
logits = model(inputs)
loss = self._obj_loss(
inputs,
logits,
target,
model,
add_evaluator_regularization=self.add_regularization)
#torch.distributed.all_reduce(loss, op=torch.distributed.ReduceOp.SUM)
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), self.grad_clip)
optimizer.step()
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = inputs.size(0)
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
del loss
if step % self.report_every == 0:
self.logger.info("train %03d %.3f; %.2f%%; %.2f%%", step,
objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg
def infer_epoch(self, valid_queue, model, criterion, device):
expect(self._is_setup, "trainer.setup should be called first")
objs = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
objective_perfs = utils.OrderedStats()
all_perfs = []
model.eval()
context = torch.no_grad if self.eval_no_grad else nullcontext
with context():
for step, (inputs, target) in enumerate(valid_queue):
inputs = inputs.to(device)
target = target.to(device)
logits = model(inputs)
loss = criterion(logits, target)
perfs = self._perf_func(inputs, logits, target, model)
all_perfs.append(perfs)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = inputs.size(0)
# objective_perfs.update(dict(zip(self._perf_names, perfs)), n=n)
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
del loss
if step % self.report_every == 0:
all_perfs_by_name = list(zip(*all_perfs))
# support use objective aggregate fn, for stat method other than mean
# e.g., adversarial distance median; detection mAP (see det_trainer.py)
obj_perfs = {
k: self.objective.aggregate_fn(k, False)(v)
for k, v in zip(self._perf_names, all_perfs_by_name)
}
self.logger.info("valid %03d %e %f %f %s", step, objs.avg, top1.avg, top5.avg,
"; ".join(["{}: {:.3f}".format(perf_n, v) \
# for perf_n, v in objective_perfs.avgs().items()]))
for perf_n, v in obj_perfs.items()]))
all_perfs_by_name = list(zip(*all_perfs))
obj_perfs = {
k: self.objective.aggregate_fn(k, False)(v)
for k, v in zip(self._perf_names, all_perfs_by_name)
}
return top1.avg, objs.avg, obj_perfs