def run_epoch(self):
self.set_model_mode('train')
losses = MetricMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
# Decide to iterate over labeled or unlabeled dataset
len_train_loader_u = len(self.train_loader_u)
self.num_batches = len_train_loader_u
train_loader_u_iter = iter(self.train_loader_u)
# self.fix_model.load_state_dict(self.model.state_dict())
# self.fix_model.eval()
self.netB = copy.deepcopy(self.model.backbone)
self.netB.eval()
if self.model.head is not None:
self.netH = copy.deepcopy(self.model.head)
self.netH.eval()
self.center = self.obtain_center()
end = time.time()
for self.batch_idx in range(self.num_batches):
batch_u = next(train_loader_u_iter)
data_time.update(time.time() - end)
loss_summary = self.forward_backward(batch_u)
batch_time.update(time.time() - end)
losses.update(loss_summary)
if (self.batch_idx + 1) % self.cfg.TRAIN.PRINT_FREQ == 0:
nb_this_epoch = self.num_batches - (self.batch_idx + 1)
nb_future_epochs = (self.max_epoch -
(self.epoch + 1)) * self.num_batches
eta_seconds = batch_time.avg * (nb_this_epoch +
nb_future_epochs)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
print('epoch [{0}/{1}][{2}/{3}]\t'
'time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'eta {eta}\t'
'{losses}\t'
'lr {lr}'.format(self.epoch + 1,
self.max_epoch,
self.batch_idx + 1,
self.num_batches,
batch_time=batch_time,
data_time=data_time,
eta=eta,
losses=losses,
lr=self.get_current_lr()))
n_iter = self.epoch * self.num_batches + self.batch_idx
for name, meter in losses.meters.items():
self.write_scalar('train/' + name, meter.avg, n_iter)
end = time.time()
def run_epoch(self):
self.set_model_mode('train')
losses = MetricMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
self.num_batches = len(self.train_loader_x)
end = time.time()
for self.batch_idx, batch in enumerate(self.train_loader_x):
data_time.update(time.time() - end)
loss_summary = self.forward_backward(batch)
batch_time.update(time.time() - end)
losses.update(loss_summary)
if (self.batch_idx + 1) % self.cfg.TRAIN.PRINT_FREQ == 0:
nb_this_epoch = self.num_batches - (self.batch_idx + 1)
nb_future_epochs = (
self.max_epoch - (self.epoch + 1)
) * self.num_batches
eta_seconds = batch_time.avg * (nb_this_epoch+nb_future_epochs)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
print(
'epoch [{0}/{1}][{2}/{3}]\t'
'time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'eta {eta}\t'
'{losses}\t'
'lr {lr}'.format(
self.epoch + 1,
self.max_epoch,
self.batch_idx + 1,
self.num_batches,
batch_time=batch_time,
data_time=data_time,
eta=eta,
losses=losses,
lr=self.get_current_lr()
)
)
n_iter = self.epoch * self.num_batches + self.batch_idx
for name, meter in losses.meters.items():
self.write_scalar('train/' + name, meter.avg, n_iter)
end = time.time()
def run_epoch(self):
self.set_model_mode('train')
losses = MetricMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
# Decide to iterate over labeled or unlabeled dataset
len_train_loader_x = len(self.train_loader_x)
len_train_loader_u = len(self.train_loader_u)
if self.cfg.TRAIN.COUNT_ITER == 'train_x':
self.num_batches = len_train_loader_x
elif self.cfg.TRAIN.COUNT_ITER == 'train_u':
self.num_batches = len_train_loader_u
elif self.cfg.TRAIN.COUNT_ITER == 'smaller_one':
self.num_batches = min(len_train_loader_x, len_train_loader_u)
else:
raise ValueError
train_loader_x_iter = iter(self.train_loader_x)
train_loader_u_iter = iter(self.train_loader_u)
end = time.time()
for self.batch_idx in range(self.num_batches):
try:
batch_x = next(train_loader_x_iter)
except StopIteration:
train_loader_x_iter = iter(self.train_loader_x)
batch_x = next(train_loader_x_iter)
try:
batch_u = next(train_loader_u_iter)
except StopIteration:
train_loader_u_iter = iter(self.train_loader_u)
batch_u = next(train_loader_u_iter)
data_time.update(time.time() - end)
loss_summary = self.forward_backward(batch_x, batch_u)
batch_time.update(time.time() - end)
losses.update(loss_summary)
if (self.batch_idx + 1) % self.cfg.TRAIN.PRINT_FREQ == 0:
nb_this_epoch = self.num_batches - (self.batch_idx + 1)
nb_future_epochs = (self.max_epoch -
(self.epoch + 1)) * self.num_batches
eta_seconds = batch_time.avg * (nb_this_epoch +
nb_future_epochs)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
print('epoch [{0}/{1}][{2}/{3}]\t'
'time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'eta {eta}\t'
'{losses}\t'
'lr {lr}'.format(self.epoch + 1,
self.max_epoch,
self.batch_idx + 1,
self.num_batches,
batch_time=batch_time,
data_time=data_time,
eta=eta,
losses=losses,
lr=self.get_current_lr()))
n_iter = self.epoch * self.num_batches + self.batch_idx
for name, meter in losses.meters.items():
self.write_scalar('train/' + name, meter.avg, n_iter)
self.write_scalar('train/lr', self.get_current_lr(), n_iter)
end = time.time()