def forward_loss(model, criterion, input, target, meter):
"""forward model and return loss"""
if getattr(FLAGS, 'normalize', False):
input = input #(128 * input).round_().clamp_(-128, 127)
else:
input = (255 * input).round_()
output = model(input)
loss = torch.mean(criterion(output, target))
# topk
_, pred = output.topk(max(FLAGS.topk))
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
correct_k = []
for k in FLAGS.topk:
correct_k.append(correct[:k].float().sum(0))
res = torch.cat([loss.view(1)] + correct_k, dim=0)
if getattr(FLAGS, 'distributed', False) and getattr(FLAGS, 'distributed_all_reduce', False):
res = dist_all_reduce_tensor(res)
res = res.cpu().detach().numpy()
bs = (res.size - 1) // len(FLAGS.topk)
for i, k in enumerate(FLAGS.topk):
error_list = list(1. - res[1+i*bs:1+(i+1)*bs])
if meter is not None:
meter['top{}_error'.format(k)].cache_list(error_list)
if meter is not None:
meter['loss'].cache(res[0])
return loss
def forward_loss(
model, criterion, input, target, meter, soft_target=None,
soft_criterion=None, return_soft_target=False, return_acc=False):
"""forward model and return loss"""
output = model(input)
if soft_target is not None:
loss = torch.mean(soft_criterion(output, soft_target))
else:
loss = torch.mean(criterion(output, target))
# topk
_, pred = output.topk(max(FLAGS.topk))
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
correct_k = []
for k in FLAGS.topk:
correct_k.append(correct[:k].float().sum(0))
tensor = torch.cat([loss.view(1)] + correct_k, dim=0)
# allreduce
tensor = dist_all_reduce_tensor(tensor)
# cache to meter
tensor = tensor.cpu().detach().numpy()
bs = (tensor.size-1)//2
for i, k in enumerate(FLAGS.topk):
error_list = list(1.-tensor[1+i*bs:1+(i+1)*bs])
if return_acc and k == 1:
top1_error = sum(error_list) / len(error_list)
return loss, top1_error
if meter is not None:
meter['top{}_error'.format(k)].cache_list(error_list)
if meter is not None:
meter['loss'].cache(tensor[0])
if return_soft_target:
return loss, torch.nn.functional.softmax(output, dim=1)
return loss