def train(args):
model = RAFT(args)
model = nn.DataParallel(model)
print("Parameter Count: %d" % count_parameters(model))
if args.restore_ckpt is not None:
model.load_state_dict(torch.load(args.restore_ckpt))
model.cuda()
model.train()
if 'chairs' not in args.dataset:
model.module.freeze_bn()
train_loader = fetch_dataloader(args)
optimizer, scheduler = fetch_optimizer(args, model)
total_steps = 0
logger = Logger(model, scheduler)
should_keep_training = True
while should_keep_training:
for i_batch, data_blob in enumerate(train_loader):
image1, image2, flow, valid = [x.cuda() for x in data_blob]
optimizer.zero_grad()
flow_predictions = model(image1, image2, iters=args.iters)
loss, metrics = sequence_loss(flow_predictions, flow, valid)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
scheduler.step()
total_steps += 1
logger.push(metrics)
if total_steps % VAL_FREQ == VAL_FREQ - 1:
PATH = 'checkpoints/%d_%s.pth' % (total_steps + 1, args.name)
torch.save(model.state_dict(), PATH)
if total_steps == args.num_steps:
should_keep_training = False
break
PATH = 'checkpoints/%s.pth' % args.name
torch.save(model.state_dict(), PATH)
return PATH
def train(gpu, ngpus_per_node, args):
print("Using GPU %d for training" % gpu)
args.gpu = gpu
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=ngpus_per_node,
rank=args.gpu)
model = RAFT(args)
if args.distributed:
torch.cuda.set_device(args.gpu)
args.batch_size = int(args.batch_size / ngpus_per_node)
model = nn.SyncBatchNorm.convert_sync_batchnorm(module=model)
model = model.to(f'cuda:{args.gpu}')
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.gpu],
find_unused_parameters=True,
output_device=args.gpu)
else:
model = torch.nn.DataParallel(model)
model.cuda()
logroot = os.path.join(args.logroot, args.name)
print("Parameter Count: %d, saving location: %s" %
(count_parameters(model), logroot))
if args.restore_ckpt is not None:
print("=> loading checkpoint '{}'".format(args.restore_ckpt))
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.restore_ckpt, map_location=loc)
model.load_state_dict(checkpoint, strict=False)
model.train()
if args.stage != 'chairs':
model.module.freeze_bn()
train_entries, evaluation_entries = read_splits()
aug_params = {
'crop_size': args.image_size,
'min_scale': -0.2,
'max_scale': 0.4,
'do_flip': False
}
train_dataset = VirtualKITTI2(aug_params,
split='training',
root=args.dataset_root,
entries=train_entries)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset) if args.distributed else None
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
pin_memory=False,
shuffle=(train_sampler is None),
num_workers=args.num_workers,
drop_last=True,
sampler=train_sampler)
eval_dataset = VirtualKITTI2(split='evaluation',
root=args.dataset_root,
entries=evaluation_entries)
eval_sampler = torch.utils.data.distributed.DistributedSampler(
eval_dataset) if args.distributed else None
eval_loader = data.DataLoader(eval_dataset,
batch_size=args.batch_size,
pin_memory=False,
shuffle=(eval_sampler is None),
num_workers=args.num_workers,
drop_last=True,
sampler=eval_sampler)
if args.distributed:
group = dist.new_group([i for i in range(ngpus_per_node)])
optimizer, scheduler = fetch_optimizer(args, model)
total_steps = 0
scaler = GradScaler(enabled=args.mixed_precision)
if args.gpu == 0:
logger = Logger(model, scheduler, logroot)
logger_evaluation = Logger(
model, scheduler,
os.path.join(args.logroot, 'evaluation_VRKitti', args.name))
VAL_FREQ = 500
add_noise = True
epoch = 0
should_keep_training = True
while should_keep_training:
for i_batch, data_blob in enumerate(train_loader):
optimizer.zero_grad()
image1, image2, flow, valid = data_blob
image1 = Variable(image1, requires_grad=True)
image1 = image1.cuda(gpu, non_blocking=True)
image2 = Variable(image2, requires_grad=True)
image2 = image2.cuda(gpu, non_blocking=True)
flow = Variable(flow, requires_grad=True)
flow = flow.cuda(gpu, non_blocking=True)
valid = Variable(valid, requires_grad=True)
valid = valid.cuda(gpu, non_blocking=True)
if add_noise:
stdv = np.random.uniform(0.0, 5.0)
image1 = (image1 + stdv * torch.randn(*image1.shape).cuda(
gpu, non_blocking=True)).clamp(0.0, 255.0)
image2 = (image2 + stdv * torch.randn(*image2.shape).cuda(
gpu, non_blocking=True)).clamp(0.0, 255.0)
flow_predictions = model(image1, image2, iters=args.iters)
loss, metrics = sequence_loss(flow_predictions, flow, valid,
args.gamma)
scaler.scale(loss).backward()
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
scaler.step(optimizer)
scheduler.step()
scaler.update()
if args.gpu == 0:
logger.push(metrics, image1, image2, flow, flow_predictions,
valid)
if total_steps % VAL_FREQ == VAL_FREQ - 1:
results = validate_VRKitti2(model.module, args, eval_loader,
group)
model.train()
if args.stage != 'chairs':
model.module.freeze_bn()
if args.gpu == 0:
logger_evaluation.write_dict(results, total_steps)
PATH = os.path.join(
logroot, '%s.pth' % (str(total_steps + 1).zfill(3)))
torch.save(model.state_dict(), PATH)
total_steps += 1
if total_steps > args.num_steps:
should_keep_training = False
break
epoch = epoch + 1
if args.gpu == 0:
logger.close()
PATH = os.path.join(logroot, 'final.pth')
torch.save(model.state_dict(), PATH)
return PATH
def train(gpu, ngpus_per_node, args):
print("Using GPU %d for training" % gpu)
args.gpu = gpu
if args.distributed:
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=ngpus_per_node, rank=args.gpu)
model = RAFT(args=args)
if args.distributed:
torch.cuda.set_device(args.gpu)
args.batch_size = int(args.batch_size / ngpus_per_node)
model = nn.SyncBatchNorm.convert_sync_batchnorm(module=model)
model = model.to(f'cuda:{args.gpu}')
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True, output_device=args.gpu)
else:
model = torch.nn.DataParallel(model)
model.cuda()
logroot = os.path.join(args.logroot, args.name)
print("Parameter Count: %d, saving location: %s" % (count_parameters(model), logroot))
if args.restore_ckpt is not None:
print("=> loading checkpoint '{}'".format(args.restore_ckpt))
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.restore_ckpt, map_location=loc)
model.load_state_dict(checkpoint, strict=False)
model.train()
train_entries, evaluation_entries = read_splits()
train_dataset = KITTI_eigen(root=args.dataset_root, inheight=args.inheight, inwidth=args.inwidth, entries=train_entries, maxinsnum=args.maxinsnum,
depth_root=args.depth_root, depthvls_root=args.depthvlsgt_root, prediction_root=args.prediction_root, ins_root=args.ins_root,
istrain=True, muteaug=False, banremovedup=False, isgarg=True)
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if args.distributed else None
train_loader = data.DataLoader(train_dataset, batch_size=args.batch_size, pin_memory=True, num_workers=int(args.num_workers / ngpus_per_node), drop_last=True, sampler=train_sampler)
eval_dataset = KITTI_eigen(root=args.dataset_root, inheight=args.evalheight, inwidth=args.evalwidth, entries=evaluation_entries, maxinsnum=args.maxinsnum,
depth_root=args.depth_root, depthvls_root=args.depthvlsgt_root, prediction_root=args.prediction_root, ins_root=args.ins_root, istrain=False, isgarg=True)
eval_sampler = torch.utils.data.distributed.DistributedSampler(eval_dataset) if args.distributed else None
eval_loader = data.DataLoader(eval_dataset, batch_size=1, pin_memory=True, num_workers=3, drop_last=True, sampler=eval_sampler)
print("Training splits contain %d images while test splits contain %d images" % (train_dataset.__len__(), eval_dataset.__len__()))
if args.distributed:
group = dist.new_group([i for i in range(ngpus_per_node)])
optimizer, scheduler = fetch_optimizer(args, model, int(train_dataset.__len__() / 2))
total_steps = 0
if args.gpu == 0:
logger = Logger(logroot)
logger_evaluation = Logger(os.path.join(args.logroot, 'evaluation_eigen_background', args.name))
logger.create_summarywriter()
logger_evaluation.create_summarywriter()
VAL_FREQ = 5000
epoch = 0
minout = 1
st = time.time()
should_keep_training = True
while should_keep_training:
train_sampler.set_epoch(epoch)
for i_batch, data_blob in enumerate(train_loader):
optimizer.zero_grad()
image1 = data_blob['img1'].cuda(gpu) / 255.0
image2 = data_blob['img2'].cuda(gpu) / 255.0
flowmap = data_blob['flowmap'].cuda(gpu)
outputs = model(image1, image2)
selector = (flowmap[:, 0, :, :] != 0)
flow_loss = sequence_loss(outputs, flowmap, selector, gamma=args.gamma, max_flow=MAX_FLOW)
metrics = dict()
metrics['flow_loss'] = flow_loss
loss = flow_loss
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
scheduler.step()
if args.gpu == 0:
logger.write_dict(metrics, step=total_steps)
if total_steps % SUM_FREQ == 0:
dr = time.time() - st
resths = (args.num_steps - total_steps) * dr / (total_steps + 1) / 60 / 60
print("Step: %d, rest hour: %f, flowloss: %f" % (total_steps, resths, flow_loss.item()))
logger.write_vls(data_blob, outputs, selector.unsqueeze(1), total_steps)
if total_steps % VAL_FREQ == 1:
if args.gpu == 0:
results = validate_kitti(model.module, args, eval_loader, logger, group, total_steps)
else:
results = validate_kitti(model.module, args, eval_loader, None, group, None)
if args.gpu == 0:
logger_evaluation.write_dict(results, total_steps)
if minout > results['out']:
minout = results['out']
PATH = os.path.join(logroot, 'minout.pth')
torch.save(model.state_dict(), PATH)
print("model saved to %s" % PATH)
model.train()
total_steps += 1
if total_steps > args.num_steps:
should_keep_training = False
break
epoch = epoch + 1
if args.gpu == 0:
logger.close()
PATH = os.path.join(logroot, 'final.pth')
torch.save(model.state_dict(), PATH)
return
def train(gpu, ngpus_per_node, args):
print("Using GPU %d for training" % gpu)
args.gpu = gpu
if args.distributed:
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=ngpus_per_node, rank=args.gpu)
model = RAFT(args)
if args.distributed:
torch.cuda.set_device(args.gpu)
args.batch_size = int(args.batch_size / ngpus_per_node)
model = nn.SyncBatchNorm.convert_sync_batchnorm(module=model)
model = model.to(f'cuda:{args.gpu}')
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True, output_device=args.gpu)
eppCbck = eppConstrainer_background(height=args.image_size[0], width=args.image_size[1], bz=args.batch_size)
eppCbck.to(f'cuda:{args.gpu}')
eppconcluer = eppConcluer()
eppconcluer.to(f'cuda:{args.gpu}')
else:
model = torch.nn.DataParallel(model)
model.cuda()
logroot = os.path.join(args.logroot, args.name)
print("Parameter Count: %d, saving location: %s" % (count_parameters(model), logroot))
if args.restore_ckpt is not None:
print("=> loading checkpoint '{}'".format(args.restore_ckpt))
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.restore_ckpt, map_location=loc)
model.load_state_dict(checkpoint, strict=False)
model.train()
if args.stage != 'chairs':
model.module.freeze_bn()
train_entries, evaluation_entries = read_splits()
aug_params = {'crop_size': args.image_size, 'min_scale': -0.2, 'max_scale': 0.4, 'do_flip': False}
train_dataset = KITTI_eigen(aug_params, split='training', root=args.dataset_root, entries=train_entries, semantics_root=args.semantics_root, depth_root=args.depth_root)
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if args.distributed else None
train_loader = data.DataLoader(train_dataset, batch_size=args.batch_size, pin_memory=True,
shuffle=(train_sampler is None), num_workers=int(args.num_workers / ngpus_per_node), drop_last=True,
sampler=train_sampler)
eval_dataset = KITTI_eigen(split='evaluation', root=args.dataset_root, entries=evaluation_entries, semantics_root=args.semantics_root, depth_root=args.depth_root)
eval_sampler = torch.utils.data.distributed.DistributedSampler(eval_dataset) if args.distributed else None
eval_loader = data.DataLoader(eval_dataset, batch_size=1, pin_memory=True,
shuffle=(eval_sampler is None), num_workers=3, drop_last=True,
sampler=eval_sampler)
if args.distributed:
group = dist.new_group([i for i in range(ngpus_per_node)])
optimizer, scheduler = fetch_optimizer(args, model)
total_steps = 0
if args.gpu == 0:
logger = Logger(model, scheduler, logroot, args.num_steps)
logger_evaluation = Logger(model, scheduler, os.path.join(args.logroot, 'evaluation_eigen_background', args.name), args.num_steps)
VAL_FREQ = 5000
add_noise = False
epoch = 0
should_keep_training = True
print(validate_kitti(model.module, args, eval_loader, eppCbck, eppconcluer, group))
while should_keep_training:
train_sampler.set_epoch(epoch)
for i_batch, data_blob in enumerate(train_loader):
optimizer.zero_grad()
image1 = data_blob['img1']
image1 = Variable(image1, requires_grad=True)
image1 = image1.cuda(gpu, non_blocking=True)
image2 = data_blob['img2']
image2 = Variable(image2, requires_grad=True)
image2 = image2.cuda(gpu, non_blocking=True)
flow = data_blob['flow']
flow = Variable(flow, requires_grad=True)
flow = flow.cuda(gpu, non_blocking=True)
valid = data_blob['valid']
valid = Variable(valid, requires_grad=True)
valid = valid.cuda(gpu, non_blocking=True)
E = data_blob['E']
E = Variable(E, requires_grad=True)
E = E.cuda(gpu, non_blocking=True)
semantic_selector = data_blob['semantic_selector']
semantic_selector = Variable(semantic_selector, requires_grad=True)
semantic_selector = semantic_selector.cuda(gpu, non_blocking=True)
if add_noise:
stdv = np.random.uniform(0.0, 5.0)
image1 = (image1 + stdv * torch.randn(*image1.shape).cuda(gpu, non_blocking=True)).clamp(0.0, 255.0)
image2 = (image2 + stdv * torch.randn(*image2.shape).cuda(gpu, non_blocking=True)).clamp(0.0, 255.0)
flow_predictions = model(image1, image2, iters=args.iters)
metrics = dict()
loss_flow, metrics_flow = sequence_flowloss(flow_predictions, flow, valid, args.gamma)
loss_eppc, metrics_eppc = sequence_eppcloss(eppCbck, flow_predictions, semantic_selector, E, args.gamma)
metrics.update(metrics_flow)
metrics.update(metrics_eppc)
loss = loss_flow + loss_eppc * args.eppcw
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
scheduler.step()
if args.gpu == 0:
logger.push(metrics, image1, image2, flow, flow_predictions, valid, data_blob['depth'])
if total_steps % VAL_FREQ == VAL_FREQ - 1:
results = validate_kitti(model.module, args, eval_loader, eppCbck, eppconcluer, group)
model.train()
if args.stage != 'chairs':
model.module.freeze_bn()
if args.gpu == 0:
logger_evaluation.write_dict(results, total_steps)
PATH = os.path.join(logroot, '%s.pth' % (str(total_steps + 1).zfill(3)))
torch.save(model.state_dict(), PATH)
total_steps += 1
if total_steps > args.num_steps:
should_keep_training = False
break
epoch = epoch + 1
if args.gpu == 0:
logger.close()
PATH = os.path.join(logroot, 'final.pth')
torch.save(model.state_dict(), PATH)
return PATH
def train(gpu, ngpus_per_node, args):
print("Using GPU %d for training" % gpu)
args.gpu = gpu
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=ngpus_per_node,
rank=args.gpu)
model = RAFT(args)
if args.distributed:
torch.cuda.set_device(args.gpu)
args.batch_size = int(args.batch_size / ngpus_per_node)
model = nn.SyncBatchNorm.convert_sync_batchnorm(module=model)
model = model.to(f'cuda:{args.gpu}')
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.gpu],
find_unused_parameters=True,
output_device=args.gpu)
else:
model = torch.nn.DataParallel(model)
model.cuda()
logroot = os.path.join(args.logroot, args.name)
print("Parameter Count: %d, saving location: %s" %
(count_parameters(model), logroot))
if args.restore_ckpt is not None:
print("=> loading checkpoint '{}'".format(args.restore_ckpt))
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.restore_ckpt, map_location=loc)
model.load_state_dict(checkpoint, strict=False)
model.train()
train_entries, evaluation_entries = read_splits()
train_dataset = VirtualKITTI2(args=args,
root=args.dataset_root,
inheight=args.inheight,
inwidth=args.inwidth,
entries=train_entries,
istrain=True)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset) if args.distributed else None
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
pin_memory=False,
shuffle=(train_sampler is None),
num_workers=args.num_workers,
drop_last=True,
sampler=train_sampler)
eval_dataset = VirtualKITTI2(args=args,
root=args.dataset_root,
inheight=args.evalheight,
inwidth=args.evalwidth,
entries=evaluation_entries,
istrain=False)
eval_sampler = torch.utils.data.distributed.DistributedSampler(
eval_dataset) if args.distributed else None
eval_loader = data.DataLoader(eval_dataset,
batch_size=args.batch_size,
pin_memory=False,
shuffle=(eval_sampler is None),
num_workers=2,
drop_last=True,
sampler=eval_sampler)
print(
"Training split contains %d images, validation split contained %d images"
% (len(train_entries), len(evaluation_entries)))
if args.distributed:
group = dist.new_group([i for i in range(ngpus_per_node)])
optimizer, scheduler = fetch_optimizer(args, model)
total_steps = 0
VAL_ITERINC = 4
if args.gpu == 0:
logger = Logger(model, scheduler, logroot)
logger.create_summarywriter()
logger_evaluations = dict()
for num_iters in range(args.iters, args.iters * 2 + 1, VAL_ITERINC):
logger_evaluation = Logger(
model, scheduler,
os.path.join(
args.logroot, 'evaluation_VRKitti',
"{}_iternum{}".format(args.name,
str(num_iters).zfill(2))))
logger_evaluation.create_summarywriter()
logger_evaluations[num_iters] = logger_evaluation
VAL_FREQ = 2000
maxout = 1
epoch = 0
st = time.time()
should_keep_training = True
while should_keep_training:
for i_batch, data_blob in enumerate(train_loader):
optimizer.zero_grad()
image1 = data_blob['img1'].cuda(gpu, non_blocking=True)
image2 = data_blob['img2'].cuda(gpu, non_blocking=True)
flow = data_blob['flowmap'].cuda(gpu, non_blocking=True)
# exlude invalid pixels and extremely large diplacements
mag = torch.sum(flow**2, dim=1).sqrt()
valid = ((flow[:, 0] != 0) * (flow[:, 1] != 0) *
(mag < MAX_FLOW)).unsqueeze(1)
flow_predictions = model(image1, image2, iters=args.iters)
loss, metrics = sequence_loss(flow_predictions, flow, valid,
args.gamma)
metrics = dict()
metrics['loss_flow'] = loss.float().item()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
scheduler.step()
if args.gpu == 0:
logger.write_dict(metrics, total_steps)
if total_steps % SUM_FREQ == 0:
dr = time.time() - st
resths = (args.num_steps -
total_steps) * dr / (total_steps + 1) / 60 / 60
print("Step: %d, rest hour: %f, flow loss: %f" %
(total_steps, resths, loss.item()))
logger.write_vls(data_blob, flow_predictions, valid,
total_steps)
if total_steps % VAL_FREQ == 1:
for num_iters in range(args.iters, args.iters * 2 + 1,
VAL_ITERINC):
if args.gpu == 0 and num_iters == 24:
results = validate_VRKitti2(model.module, args,
eval_loader, num_iters,
group, logger, total_steps)
else:
results = validate_VRKitti2(model.module, args,
eval_loader, num_iters,
group, None, None)
if args.gpu == 0:
logger_evaluations[num_iters].write_dict(
results, total_steps)
if num_iters == 24:
if results['out'] < maxout:
maxout = results['out']
PATH = os.path.join(logroot, 'minout.pth')
torch.save(model.state_dict(), PATH)
print("model saved to %s" % PATH)
model.train()
total_steps += 1
if total_steps > args.num_steps:
should_keep_training = False
break
epoch = epoch + 1
if args.gpu == 0:
logger.close()
PATH = os.path.join(logroot, 'final.pth')
torch.save(model.state_dict(), PATH)
return PATH