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import torch
class LossMeter(object):
def __init__(self, loss_func):
self.running_loss = []
self.count = 0 # count = number of all received values, including those cleared.
self.loss_func = loss_func
def update(self, pred, gt, get_result=False, fg_weight=None):
self.count += 1
if fg_weight is not None:
loss = self.loss_func(pred, gt, weight=[1, fg_weight])
else:
loss = self.loss_func(pred, gt)
self.running_loss.append(loss.detach())
if get_result:
return loss
def get_metric(self):
avg = 0
for p in self.running_loss:
avg += p
loss_avg = avg*1.0 / len(self.running_loss) if len(self.running_loss)!=0 else None
return loss_avg
def reset(self):
self.running_loss = []
class MultiLossMeter(object):
def __init__(self):
self.running_loss = {}
self.loss_names = None
self.count = 0
def reset(self):
self.count = 0
self.running_loss = {}
if self.loss_names is not None:
for term in self.loss_names:
self.running_loss[term] = 0.0
def update(self, losses, loss_names):
if self.loss_names is None:
self.loss_names = loss_names
self.reset()
self.count += 1
loss_terms = dict(zip(loss_names, losses))
# update running loss
for term in self.running_loss.keys():
if term in loss_terms.keys():
self.running_loss[term] += loss_terms[term].detach()
def get_metric(self):
keys = self.running_loss.keys()
avg_terms = {}
for key in keys:
avg_terms[key] = 0.0
for key in keys:
avg_terms[key] = self.running_loss[key] * 1.0 / self.count
return avg_terms
class RunningStats:
def __init__(self):
self.n = 0
self.old_m = 0
self.new_m = 0
self.old_s = 0
self.new_s = 0
def clear(self):
self.n = 0
def push(self, x):
self.n += 1
if self.n == 1:
self.old_m = self.new_m = x
self.old_s = 0
else:
self.new_m = self.old_m + (x - self.old_m) / self.n
self.new_s = self.old_s + (x - self.old_m) * (x - self.new_m)
self.old_m = self.new_m
self.old_s = self.new_s
def mean(self):
return self.new_m if self.n else 0.0
def variance(self):
return self.new_s / (self.n - 1) if self.n > 1 else 0.0
def std(self):
return math.sqrt(self.variance())
class TorchRunningStats:
def __init__(self):
self.n = torch.tensor(0).long().cuda()
self.old_m = torch.tensor(0.0).float().cuda()
self.new_m = torch.tensor(0.0).float().cuda()
self.old_s = torch.tensor(0.0).float().cuda()
self.new_s = torch.tensor(0.0).float().cuda()
def clear(self):
self.n = torch.tensor(0).long().cuda()
def push(self, x):
self.n += 1
if self.n == 1:
self.old_m = self.new_m = x
self.old_s = 0
else:
self.new_m = self.old_m + (x - self.old_m) / self.n
self.new_s = self.old_s + (x - self.old_m) * (x - self.new_m)
self.old_m = self.new_m
self.old_s = self.new_s
def mean(self):
return self.new_m if self.n else 0.0
def variance(self):
return self.new_s / (self.n - 1) if self.n > 1 else 0.0
def std(self):
return torch.sqrt(self.variance())
def l1_loss(pred, gt):
return abs(pred - gt)
if __name__ == '__main__':
loss_meter = LossMeter(l1_loss)
data = [
(1, 2),
(3, 5),
(0, 4),
]
for (pred, gt) in data:
loss = loss_meter.update(pred, gt, get_result=True)
print(loss)
print(loss_meter.get_metric()) |