def demo(args):
model = torch.nn.DataParallel(RAFT(args))
model.load_state_dict(torch.load(args.model))
model = model.module
model.to(DEVICE)
model.eval()
dataset = UnderfolderReader(folder='/tmp/hammerboy_dataset')
with torch.no_grad():
for sample in dataset:
im1 = sample['right']
im2 = sample['left']
image1 = torch.Tensor(im1).unsqueeze(0).unsqueeze(0).repeat(1, 3, 1, 1).to(DEVICE)
image2 = torch.Tensor(im2).unsqueeze(0).unsqueeze(0).repeat(1, 3, 1, 1).to(DEVICE)
t1 = time.perf_counter()
flow_low, flow_up = model(image1, image2, iters=20, test_mode=True)
t2 = time.perf_counter()
print("Time: ", t2 - t1)
# view(image1_warped, 'img2_warped')
# view(image1, 'img2')
viz_gt(sample['depth'])
viz_disparity(image1, flow_up)
cv2.waitKey(0)
def demo(args):
model = torch.nn.DataParallel(RAFT(args))
model.load_state_dict(torch.load(args.model, map_location=DEVICE))
model = model.module
model.to(DEVICE)
model.eval()
model = torch.jit.script(model)
with torch.no_grad():
images = glob.glob(os.path.join(args.path, '*.png')) + \
glob.glob(os.path.join(args.path, '*.jpg'))
images = sorted(images)
for imfile1, imfile2 in zip(images[:-1], images[1:]):
image1 = load_image(imfile1)
image2 = load_image(imfile2)
padder = InputPadder(image1.shape)
image1, image2 = padder.pad(image1, image2)
# export(
# model, image1, image2,
# torch.tensor([6], dtype=torch.int),
# torch.Tensor([False]),
# torch.Tensor([False]),
# torch.tensor([False], dtype=torch.bool)
# )
flow_low, flow_up = model(image1, image2, iters=1, test_mode=True)
viz(image1, flow_up)
def demo(args):
model = RAFT(args)
model = torch.nn.DataParallel(model)
model.load_state_dict(torch.load(args.model))
model.to(DEVICE)
model.eval()
with torch.no_grad():
# sintel images
image1 = load_image('images/sintel_0.png')
image2 = load_image('images/sintel_1.png')
flow_predictions = model(image1, image2, iters=args.iters, upsample=False)
display(image1[0], image2[0], flow_predictions[-1][0])
# kitti images
image1 = load_image('images/kitti_0.png')
image2 = load_image('images/kitti_1.png')
flow_predictions = model(image1, image2, iters=16)
display(image1[0], image2[0], flow_predictions[-1][0])
# davis images
image1 = load_image('images/davis_0.jpg')
image2 = load_image('images/davis_1.jpg')
flow_predictions = model(image1, image2, iters=16)
display(image1[0], image2[0], flow_predictions[-1][0])
def LoadModel(args):
model = torch.nn.DataParallel(RAFT(args))
model.load_state_dict(torch.load(args.model))
model = model.module
model.to(DEVICE)
model.eval()
return model
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)
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()
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.eval()
if args.stage != 'chairs':
model.module.freeze_bn()
_, evaluation_entries = read_splits()
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=4, drop_last=True,
sampler=eval_sampler)
if args.distributed:
group = dist.new_group([i for i in range(ngpus_per_node)])
print(validate_kitti(model.module, args, eval_loader, eppCbck, eppconcluer, group))
return
def demo(args):
model = torch.nn.DataParallel(RAFT(args))
model.load_state_dict(torch.load(args.model))
model = model.module
model.to(DEVICE)
model.eval()
with torch.no_grad():
images = glob.glob(os.path.join(args.path, '*.png')) + \
glob.glob(os.path.join(args.path, '*.jpg')) + \
glob.glob(os.path.join(args.path, '*.tiff'))
images = sorted(images)
for imfile1, imfile2 in zip(images[:-1], images[1:]):
image1 = load_image(imfile1)
image2 = load_image(imfile2)
padder = InputPadder(image1.shape)
image1, image2 = padder.pad(image1, image2)
t1 = time.perf_counter()
flow_low, flow_up = model(image1, image2, iters=5, test_mode=True)
t2 = time.perf_counter()
print("Time: ", t2 - t1)
############
height, width = image1.shape[-2:]
grid = geometry.create_meshgrid(height,
width,
normalized_coordinates=False).to(
image1.device)
print("SPODSAOPDA", flow_up.shape, grid.shape, grid.min(),
grid.max())
grid = flow_up.permute(0, 2, 3, 1) + grid
flow_up_norm = geometry.normalize_pixel_coordinates(
grid, height, width) # BxHxWx2
image1_warped = F.grid_sample(image2,
flow_up_norm,
align_corners=True)
view(image1_warped, 'img2_warped')
view(image1, 'img2')
viz(image1, flow_up)
def demo(args):
model = torch.nn.DataParallel(RAFT(args))
model.load_state_dict(torch.load(args.model))
model = model.module
model.to(DEVICE)
model.eval()
with torch.no_grad():
images = glob.glob(os.path.join(args.path, '*.png')) + \
glob.glob(os.path.join(args.path, '*.jpg'))
images = sorted(images)
for imfile1, imfile2 in zip(images[:-1], images[1:]):
image1 = load_image(imfile1)
image2 = load_image(imfile2)
padder = InputPadder(image1.shape)
image1, image2 = padder.pad(image1, image2)
flow_low, flow_up = model(image1, image2, iters=20, test_mode=True)
viz(image1, flow_up)
def demo2(args):
model = torch.nn.DataParallel(RAFT(args))
model.load_state_dict(torch.load(args.model))
model = model.module
model.to(DEVICE)
model.eval()
sensor = OAKSensor()
print("Sensor ok")
with torch.no_grad():
while True:
buffer = sensor.grab()
if buffer:
im1 = cv2.flip(buffer['left_stream'], 1)
im2 = cv2.flip(buffer['right_stream'], 1)
image1 = torch.Tensor(im1).unsqueeze(0).unsqueeze(0).repeat(1, 3, 1, 1).to(DEVICE)
image2 = torch.Tensor(im2).unsqueeze(0).unsqueeze(0).repeat(1, 3, 1, 1).to(DEVICE)
padder = InputPadder(image1.shape)
image1, image2 = padder.pad(image1, image2)
t1 = time.perf_counter()
flow_low, flow_up = model(image1, image2, iters=30, test_mode=True)
t2 = time.perf_counter()
print("Time: ", t2 - t1)
# view(image1_warped, 'img2_warped')
# view(image1, 'img2')
viz_disparity(image1, flow_up)
viz_gt(buffer['depth_stream'])
# cv2.imshow("image", buffer['rgb_stream'])
cv2.waitKey(1)
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(args):
import core.datasets
model = nn.DataParallel(RAFT(args), device_ids=args.gpus)
print("Parameter Count: %d" % count_parameters(model))
if args.restore_ckpt is not None:
model.load_state_dict(torch.load(args.restore_ckpt), strict=False)
model.to(DEVICE)
model.train()
if args.stage != 'chairs':
model.module.freeze_bn()
train_loader = core.datasets.fetch_dataloader(args)
optimizer, scheduler = fetch_optimizer(args, model)
total_steps = 0
scaler = GradScaler(enabled=args.mixed_precision)
logger = Logger(model, scheduler)
VAL_FREQ = 5000
should_keep_training = True
while should_keep_training:
for i_batch, data_blob in enumerate(train_loader):
optimizer.zero_grad()
image1, image2, flow, valid = [x.to(DEVICE) for x in data_blob]
if args.add_noise:
stdv = np.random.uniform(0.0, 5.0)
image1 = (image1 + stdv * torch.randn(*image1.shape).to(DEVICE)).clamp(0.0, 255.0)
image2 = (image2 + stdv * torch.randn(*image2.shape).to(DEVICE)).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()
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)
results = {}
for val_dataset in args.validation:
if val_dataset == 'chairs':
results.update(evaluate.validate_chairs(model.module))
elif val_dataset == 'sintel':
results.update(evaluate.validate_sintel(model.module))
elif val_dataset == 'kitti':
results.update(evaluate.validate_kitti(model.module))
logger.write_dict(results)
model.train()
if args.stage != 'chairs':
model.module.freeze_bn()
total_steps += 1
if total_steps > args.num_steps:
should_keep_training = False
break
logger.close()
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=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)
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
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=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()
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)
evaluation_entries = read_splits_mapping()
eval_dataset = KITTI_eigen_stereo15(root=args.dataset_stereo15_orgned_root,
inheight=args.evalheight,
inwidth=args.evalwidth,
entries=evaluation_entries,
mdPred_root=args.mdPred_root,
maxinsnum=args.maxinsnum,
istrain=True,
isgarg=True,
deepv2dpred_root=args.deepv2dpred_root,
prediction_root=args.prediction_root,
flowPred_root=args.flowPred_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,
num_workers=3,
drop_last=True,
sampler=eval_sampler)
print("Test splits contain %d images" % (eval_dataset.__len__()))
if args.distributed:
group = dist.new_group([i for i in range(ngpus_per_node)])
# validate_RAFT_flow(args, model, eval_loader, group, usestreodepth=False)
validate_RAFT_flow_pose(args,
model,
eval_loader,
group,
usestreodepth=False,
scale_info_src='mdPred')
# validate_RAFT_flow_pose(args, model, eval_loader, group, usestreodepth=True, scale_info_src='mdPred')
# validate_RAFT_flow_pose(args, model, eval_loader, group, usestreodepth=False, scale_info_src='deepv2d_depth')
# validate_RAFT_flow_pose(args, model, eval_loader, group, usestreodepth=True, scale_info_src='stereo_depth')
# validate_RAFT_flow_pose(args, model, eval_loader, group, usestreodepth=False, scale_info_src='stereo_depth')
# validate_RAFT_flow_pose(args, model, eval_loader, group, usestreodepth=True, scale_info_src='deppv2d_pose')
# validate_RAFT_flow_pose(args, model, eval_loader, group, usestreodepth=False, scale_info_src='deppv2d_pose')
# validate_RAFT_flow_pose(args, model, eval_loader, group, usestreodepth=False)
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=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()
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("Test splits contain %d images" % (eval_dataset.__len__()))
if args.distributed:
group = dist.new_group([i for i in range(ngpus_per_node)])
validate_kitti(model.module, args, eval_loader, group)
return
frame_utils.writeFlowKITTI(svpath, flow.permute(1, 2, 0).numpy())
# Image.fromarray(flow_viz.flow_to_image(flow.permute(1, 2, 0).numpy())).show()
# tensor2rgb(image1 / 255.0, viewind=0).show()
return
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model', help="restore checkpoint")
parser.add_argument('--dataset', help="dataset for evaluation")
parser.add_argument('--exportroot', help="dataset for evaluation")
parser.add_argument('--small', action='store_true', help='use small model')
parser.add_argument('--mixed_precision', action='store_true', help='use small model')
parser.add_argument('--alternate_corr', action='store_true', help='use efficent correlation implementation')
parser.add_argument('--odom_root', type=str)
parser.add_argument('--evalheight', type=int, default=1e10)
parser.add_argument('--evalwidth', type=int, default=1e10)
parser.add_argument('--maxinsnum', type=int, default=50)
args = parser.parse_args()
model = torch.nn.DataParallel(RAFT(args))
model.load_state_dict(torch.load(args.model))
torch.manual_seed(1234)
np.random.seed(1234)
ngpus_per_node = torch.cuda.device_count()
evaluation_entries = read_splits(args)
mp.spawn(validate_kitti_colorjitter, nprocs=ngpus_per_node, args=(model.module, args, ngpus_per_node, evaluation_entries))
out = ((epe > 3.0) & ((epe / mag) > 0.05)).float()
epe_list.append(epe[val].mean().item())
out_list.append(out[val].cpu().numpy())
epe_list = np.array(epe_list)
out_list = np.concatenate(out_list)
print("Validation KITTI: %f, %f" %
(np.mean(epe_list), 100 * np.mean(out_list)))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model', help="restore checkpoint")
parser.add_argument('--small', action='store_true', help='use small model')
parser.add_argument('--sintel_iters', type=int, default=50)
parser.add_argument('--kitti_iters', type=int, default=32)
args = parser.parse_args()
model = RAFT(args)
model = torch.nn.DataParallel(model)
model.load_state_dict(torch.load(args.model))
model.to('cuda')
model.eval()
validate_sintel(args, model, args.sintel_iters)
validate_kitti(args, model, args.kitti_iters)