def train(self):
curr_iter = self.curr_iter
data_loader = self.data_loader
data_loader_iter = self.data_loader.__iter__()
data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer()
total_loss = 0
total_num = 0.0
while (curr_iter < self.config.opt.max_iter):
curr_iter += 1
epoch = curr_iter / len(self.data_loader)
batch_loss, batch_pos_loss, batch_neg_loss = self._train_iter(
data_loader_iter, [data_meter, data_timer, total_timer])
total_loss += batch_loss
total_num += 1
if curr_iter % self.lr_update_freq == 0 or curr_iter == 1:
lr = self.scheduler.get_last_lr()
self.scheduler.step()
if self.is_master:
logging.info(f" Epoch: {epoch}, LR: {lr}")
self._save_checkpoint(curr_iter,
'checkpoint_' + str(curr_iter))
if curr_iter % self.config.trainer.stat_freq == 0 and self.is_master:
self.writer.add_scalar('train/loss', batch_loss, curr_iter)
self.writer.add_scalar('train/pos_loss', batch_pos_loss,
curr_iter)
self.writer.add_scalar('train/neg_loss', batch_neg_loss,
curr_iter)
logging.info(
"Train Epoch: {:.3f} [{}/{}], Current Loss: {:.3e}".format(
epoch, curr_iter, len(self.data_loader), batch_loss) +
"\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}, LR: {}"
.format(data_meter.avg, total_timer.avg - data_meter.avg,
total_timer.avg, self.scheduler.get_last_lr()))
data_meter.reset()
total_timer.reset()
def _train_epoch(self, epoch):
config = self.config
gc.collect()
self.model.train()
# Epoch starts from 1
total_loss = 0
total_num = 0.0
data_loader = self.data_loader
data_loader_iter = self.data_loader.__iter__()
iter_size = self.iter_size
data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer()
pos_dist_meter, neg_dist_meter = AverageMeter(), AverageMeter()
start_iter = (epoch - 1) * (len(data_loader) // iter_size)
for curr_iter in range(len(data_loader) // iter_size):
self.optimizer.zero_grad()
batch_loss = 0
data_time = 0
total_timer.tic()
for iter_idx in range(iter_size):
data_timer.tic()
input_dict = data_loader_iter.next()
data_time += data_timer.toc(average=False)
# pairs consist of (xyz1 index, xyz0 index)
sinput0 = ME.SparseTensor(
input_dict['sinput0_F'], coords=input_dict['sinput0_C']).to(self.device)
F0 = self.model(sinput0).F
sinput1 = ME.SparseTensor(
input_dict['sinput1_F'], coords=input_dict['sinput1_C']).to(self.device)
F1 = self.model(sinput1).F
pos_pairs = input_dict['correspondences']
loss, pos_dist, neg_dist = self.triplet_loss(
F0,
F1,
pos_pairs,
num_pos=config.triplet_num_pos * config.batch_size,
num_hn_samples=config.triplet_num_hn * config.batch_size,
num_rand_triplet=config.triplet_num_rand * config.batch_size)
loss /= iter_size
loss.backward()
batch_loss += loss.item()
pos_dist_meter.update(pos_dist)
neg_dist_meter.update(neg_dist)
self.optimizer.step()
gc.collect()
torch.cuda.empty_cache()
total_loss += batch_loss
total_num += 1.0
total_timer.toc()
data_meter.update(data_time)
if curr_iter % self.config.stat_freq == 0:
self.writer.add_scalar('train/loss', batch_loss, start_iter + curr_iter)
logging.info(
"Train Epoch: {} [{}/{}], Current Loss: {:.3e}, Pos dist: {:.3e}, Neg dist: {:.3e}"
.format(epoch, curr_iter,
len(self.data_loader) //
iter_size, batch_loss, pos_dist_meter.avg, neg_dist_meter.avg) +
"\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}".format(
data_meter.avg, total_timer.avg - data_meter.avg, total_timer.avg))
pos_dist_meter.reset()
neg_dist_meter.reset()
data_meter.reset()
total_timer.reset()
def _train_epoch(self, epoch):
gc.collect()
self.model.train()
# Epoch starts from 1
total_loss = 0
total_num = 0.0
data_loader = self.data_loader
data_loader_iter = self.data_loader.__iter__()
iter_size = self.iter_size
data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer()
start_iter = (epoch - 1) * (len(data_loader) // iter_size)
for curr_iter in range(len(data_loader) // iter_size):
self.optimizer.zero_grad()
batch_pos_loss, batch_neg_loss, batch_loss = 0, 0, 0
data_time = 0
total_timer.tic()
for iter_idx in range(iter_size):
data_timer.tic()
input_dict = data_loader_iter.next()
data_time += data_timer.toc(average=False)
sinput0 = ME.SparseTensor(
input_dict['sinput0_F'], coords=input_dict['sinput0_C']).to(self.device)
F0 = self.model(sinput0).F
sinput1 = ME.SparseTensor(
input_dict['sinput1_F'], coords=input_dict['sinput1_C']).to(self.device)
F1 = self.model(sinput1).F
pos_pairs = input_dict['correspondences']
pos_loss, neg_loss = self.contrastive_hardest_negative_loss(
F0,
F1,
pos_pairs,
num_pos=self.config.num_pos_per_batch * self.config.batch_size,
num_hn_samples=self.config.num_hn_samples_per_batch *
self.config.batch_size)
pos_loss /= iter_size
neg_loss /= iter_size
loss = pos_loss + self.neg_weight * neg_loss
loss.backward()
batch_loss += loss.item()
batch_pos_loss += pos_loss.item()
batch_neg_loss += neg_loss.item()
self.optimizer.step()
gc.collect()
torch.cuda.empty_cache()
total_loss += batch_loss
total_num += 1.0
total_timer.toc()
data_meter.update(data_time)
if curr_iter % self.config.stat_freq == 0:
self.writer.add_scalar('train/loss', batch_loss, start_iter + curr_iter)
self.writer.add_scalar('train/pos_loss', batch_pos_loss, start_iter + curr_iter)
self.writer.add_scalar('train/neg_loss', batch_neg_loss, start_iter + curr_iter)
logging.info(
"Train Epoch: {} [{}/{}], Current Loss: {:.3e} Pos: {:.3f} Neg: {:.3f}"
.format(epoch, curr_iter,
len(self.data_loader) //
iter_size, batch_loss, batch_pos_loss, batch_neg_loss) +
"\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}".format(
data_meter.avg, total_timer.avg - data_meter.avg, total_timer.avg))
data_meter.reset()
total_timer.reset()
def _train_epoch(self, epoch):
gc.collect()
self.model.train()
# Epoch starts from 1
total_loss = 0
total_num = 0.0
data_loader = self.data_loader
data_loader_iter = self.data_loader.__iter__()
iter_size = self.iter_size
start_iter = (epoch - 1) * (len(data_loader) // iter_size)
data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer()
# Main training
for curr_iter in range(len(data_loader) // iter_size):
self.optimizer.zero_grad()
batch_pos_loss, batch_neg_loss, batch_loss = 0, 0, 0
data_time = 0
total_timer.tic()
for iter_idx in range(iter_size):
# Caffe iter size
data_timer.tic()
input_dict = data_loader_iter.next()
data_time += data_timer.toc(average=False)
# pairs consist of (xyz1 index, xyz0 index)
sinput0 = ME.SparseTensor(
input_dict['sinput0_F'], coords=input_dict['sinput0_C']).to(self.device)
F0 = self.model(sinput0).F
sinput1 = ME.SparseTensor(
input_dict['sinput1_F'], coords=input_dict['sinput1_C']).to(self.device)
F1 = self.model(sinput1).F
N0, N1 = len(sinput0), len(sinput1)
pos_pairs = input_dict['correspondences']
neg_pairs = self.generate_rand_negative_pairs(pos_pairs, max(N0, N1), N0, N1)
pos_pairs = pos_pairs.long().to(self.device)
neg_pairs = torch.from_numpy(neg_pairs).long().to(self.device)
neg0 = F0.index_select(0, neg_pairs[:, 0])
neg1 = F1.index_select(0, neg_pairs[:, 1])
pos0 = F0.index_select(0, pos_pairs[:, 0])
pos1 = F1.index_select(0, pos_pairs[:, 1])
# Positive loss
pos_loss = (pos0 - pos1).pow(2).sum(1)
# Negative loss
neg_loss = F.relu(self.neg_thresh -
((neg0 - neg1).pow(2).sum(1) + 1e-4).sqrt()).pow(2)
pos_loss_mean = pos_loss.mean() / iter_size
neg_loss_mean = neg_loss.mean() / iter_size
# Weighted loss
loss = pos_loss_mean + self.neg_weight * neg_loss_mean
loss.backward(
) # To accumulate gradient, zero gradients only at the begining of iter_size
batch_loss += loss.item()
batch_pos_loss += pos_loss_mean.item()
batch_neg_loss += neg_loss_mean.item()
self.optimizer.step()
torch.cuda.empty_cache()
total_loss += batch_loss
total_num += 1.0
total_timer.toc()
data_meter.update(data_time)
# Print logs
if curr_iter % self.config.stat_freq == 0:
self.writer.add_scalar('train/loss', batch_loss, start_iter + curr_iter)
self.writer.add_scalar('train/pos_loss', batch_pos_loss, start_iter + curr_iter)
self.writer.add_scalar('train/neg_loss', batch_neg_loss, start_iter + curr_iter)
logging.info(
"Train Epoch: {} [{}/{}], Current Loss: {:.3e} Pos: {:.3f} Neg: {:.3f}"
.format(epoch, curr_iter,
len(self.data_loader) //
iter_size, batch_loss, batch_pos_loss, batch_neg_loss) +
"\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}".format(
data_meter.avg, total_timer.avg - data_meter.avg, total_timer.avg))
data_meter.reset()
total_timer.reset()
def _train_epoch(self, epoch, data_loader_iter):
# Epoch starts from 1
total_loss = 0
total_num = 0.0
iter_size = self.iter_size
data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer()
for curr_iter in range(self.train_max_iter):
self.optimizer.zero_grad()
batch_pos_loss, batch_neg_loss, batch_loss = 0, 0, 0
data_time = 0
total_timer.tic()
for iter_idx in range(iter_size):
data_timer.tic()
input_dict = self.get_data(data_loader_iter)
data_time += data_timer.toc(average=False)
F0 = self.model(input_dict['img0'].to(self.device))
F1 = self.model(input_dict['img1'].to(self.device))
pos_loss, neg_loss = self.contrastive_loss(
input_dict['img0'].numpy() + 0.5,
input_dict['img1'].numpy() + 0.5,
F0,
F1,
input_dict['pairs'],
num_pos=self.config.num_pos_per_batch,
num_hn_samples=self.config.num_hn_samples_per_batch)
pos_loss /= iter_size
neg_loss /= iter_size
loss = pos_loss + self.neg_weight * neg_loss
loss.backward()
batch_loss += loss.item()
batch_pos_loss += pos_loss.item()
batch_neg_loss += neg_loss.item()
self.optimizer.step()
gc.collect()
torch.cuda.empty_cache()
total_loss += batch_loss
total_num += 1.0
total_timer.toc()
data_meter.update(data_time)
torch.cuda.empty_cache()
if curr_iter % self.config.stat_freq == 0:
self.writer.add_scalar('train/loss', batch_loss, curr_iter)
self.writer.add_scalar('train/pos_loss', batch_pos_loss,
curr_iter)
self.writer.add_scalar('train/neg_loss', batch_neg_loss,
curr_iter)
logging.info(
"Train epoch {}, iter {}, Current Loss: {:.3e} Pos: {:.3f} Neg: {:.3f}"
.format(epoch, curr_iter, batch_loss, batch_pos_loss,
batch_neg_loss) +
"\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}"
.format(data_meter.avg, total_timer.avg -
data_meter.avg, total_timer.avg))
data_meter.reset()
total_timer.reset()
