def __init__(self,
scene_ds,
meters,
batch_size=64,
cache_data=True,
n_workers=4,
sampler=None):
self.rank = get_rank()
self.world_size = get_world_size()
self.tmp_dir = get_tmp_dir()
self.scene_ds = scene_ds
if sampler is None:
sampler = DistributedSceneSampler(scene_ds,
num_replicas=self.world_size,
rank=self.rank,
shuffle=True)
dataloader = DataLoader(scene_ds,
batch_size=batch_size,
num_workers=n_workers,
sampler=sampler,
collate_fn=self.collate_fn)
if cache_data:
self.dataloader = list(tqdm(dataloader))
else:
self.dataloader = dataloader
self.meters = meters
self.meters = OrderedDict({
k: v
for k, v in sorted(self.meters.items(), key=lambda item: item[0])
})
def gather_distributed(self, tmp_dir):
tmp_dir = Path(tmp_dir)
tmp_dir.mkdir(exist_ok=True, parents=True)
rank, world_size = get_rank(), get_world_size()
tmp_file_template = (tmp_dir / 'rank={rank}.pth.tar').as_posix()
if rank > 0:
tmp_file = tmp_file_template.format(rank=rank)
torch.save(self.datas, tmp_file)
if world_size > 1:
torch.distributed.barrier()
if rank == 0 and world_size > 1:
all_datas = self.datas
for n in range(1, world_size):
tmp_file = tmp_file_template.format(rank=n)
datas = torch.load(tmp_file)
for k in all_datas.keys():
all_datas[k].extend(datas.get(k, []))
Path(tmp_file).unlink()
self.datas = all_datas
if world_size > 1:
torch.distributed.barrier()
return
def __init__(self, scene_ds, batch_size=8, cache_data=False, n_workers=4):
self.rank = get_rank()
self.world_size = get_world_size()
self.tmp_dir = get_tmp_dir()
sampler = DistributedSceneSampler(scene_ds, num_replicas=self.world_size, rank=self.rank)
self.sampler = sampler
dataloader = DataLoader(scene_ds, batch_size=batch_size,
num_workers=n_workers,
sampler=sampler, collate_fn=self.collate_fn)
if cache_data:
self.dataloader = list(tqdm(dataloader))
else:
self.dataloader = dataloader
def __init__(self, scene_ds, batch_size=1, cache_data=False, n_workers=4):
self.rank = get_rank()
self.world_size = get_world_size()
self.tmp_dir = get_tmp_dir()
assert batch_size == 1, 'Multiple view groups not supported for now.'
sampler = DistributedSceneSampler(scene_ds,
num_replicas=self.world_size,
rank=self.rank)
self.sampler = sampler
dataloader = DataLoader(scene_ds,
batch_size=batch_size,
num_workers=n_workers,
sampler=sampler,
collate_fn=self.collate_fn)
if cache_data:
self.dataloader = list(tqdm(dataloader))
else:
self.dataloader = dataloader
def gather_distributed(self, tmp_dir=None):
rank, world_size = get_rank(), get_world_size()
tmp_file_template = (tmp_dir / 'rank={rank}.pth.tar').as_posix()
if rank > 0:
tmp_file = tmp_file_template.format(rank=rank)
torch.save(self, tmp_file)
if world_size > 1:
torch.distributed.barrier()
datas = [self]
if rank == 0 and world_size > 1:
for n in range(1, world_size):
tmp_file = tmp_file_template.format(rank=n)
data = torch.load(tmp_file)
datas.append(data)
Path(tmp_file).unlink()
if world_size > 1:
torch.distributed.barrier()
return concatenate(datas)
metrics_to_print.update({
f'{det_key}/ba_input/ADD-S_ntop=BOP_matching=OVERLAP/norm': f'Multiview before BA/ADD-S (m)',
f'{det_key}/ba_output/ADD-S_ntop=BOP_matching=OVERLAP/norm': f'Multiview after BA/ADD-S (m)',
})
if get_rank() == 0:
save_dir.mkdir()
results = format_results(all_predictions, eval_metrics, eval_dfs, print_metrics=False)
(save_dir / 'full_summary.txt').write_text(results.get('summary_txt', ''))
full_summary = results['summary']
summary_txt = 'Results:'
for k, v in metrics_to_print.items():
if k in full_summary:
summary_txt += f"\n{v}: {full_summary[k]}"
logger.info(f"{'-'*80}")
logger.info(summary_txt)
logger.info(f"{'-'*80}")
torch.save(results, save_dir / 'results.pth.tar')
(save_dir / 'summary.txt').write_text(summary_txt)
logger.info(f"Saved: {save_dir}")
if __name__ == '__main__':
patch_tqdm()
main()
time.sleep(2)
if get_world_size() > 1:
torch.distributed.barrier()
def train_pose(args):
torch.set_num_threads(1)
if args.resume_run_id:
resume_dir = EXP_DIR / args.resume_run_id
resume_args = yaml.load((resume_dir / 'config.yaml').read_text())
keep_fields = set([
'resume_run_id',
'epoch_size',
])
vars(args).update({
k: v
for k, v in vars(resume_args).items() if k not in keep_fields
})
args.train_refiner = args.TCO_input_generator == 'gt+noise'
args.train_coarse = not args.train_refiner
args.save_dir = EXP_DIR / args.run_id
logger.info(f"{'-'*80}")
for k, v in args.__dict__.items():
logger.info(f"{k}: {v}")
logger.info(f"{'-'*80}")
# Initialize distributed
device = torch.cuda.current_device()
init_distributed_mode()
world_size = get_world_size()
args.n_gpus = world_size
args.global_batch_size = world_size * args.batch_size
logger.info(f'Connection established with {world_size} gpus.')
# Make train/val datasets
def make_datasets(dataset_names):
datasets = []
for (ds_name, n_repeat) in dataset_names:
assert 'test' not in ds_name
ds = make_scene_dataset(ds_name)
logger.info(f'Loaded {ds_name} with {len(ds)} images.')
for _ in range(n_repeat):
datasets.append(ds)
return ConcatDataset(datasets)
# tracking dataset
scene_ds_train = make_datasets(args.train_ds_names)
scene_ds_val = make_datasets(args.val_ds_names)
ds_kwargs = dict(
resize=args.input_resize,
rgb_augmentation=args.rgb_augmentation,
background_augmentation=args.background_augmentation,
min_area=args.min_area,
gray_augmentation=args.gray_augmentation,
)
ds_train = PoseTrackingDataset(scene_ds_train, **ds_kwargs)
ds_val = PoseTrackingDataset(scene_ds_val, **ds_kwargs)
train_sampler = PartialSampler(ds_train, epoch_size=args.epoch_size)
ds_iter_train = DataLoader(ds_train,
sampler=train_sampler,
batch_size=args.batch_size,
num_workers=args.n_dataloader_workers,
collate_fn=ds_train.collate_fn,
drop_last=False,
pin_memory=True)
ds_iter_train = MultiEpochDataLoader(ds_iter_train)
val_sampler = PartialSampler(ds_val, epoch_size=int(0.1 * args.epoch_size))
ds_iter_val = DataLoader(ds_val,
sampler=val_sampler,
batch_size=args.batch_size,
num_workers=args.n_dataloader_workers,
collate_fn=ds_val.collate_fn,
drop_last=False,
pin_memory=True)
ds_iter_val = MultiEpochDataLoader(ds_iter_val)
# Make model
# renderer = BulletBatchRenderer(object_set=args.urdf_ds_name, n_workers=args.n_rendering_workers)
object_ds = make_object_dataset(args.object_ds_name)
mesh_db = MeshDataBase.from_object_ds(object_ds).batched(
n_sym=args.n_symmetries_batch).cuda().float()
model = create_model_pose_custom(cfg=args, mesh_db=mesh_db).cuda()
eval_bundle = make_eval_bundle(args, model)
if args.resume_run_id:
resume_dir = EXP_DIR / args.resume_run_id
path = resume_dir / 'checkpoint.pth.tar'
logger.info(f'Loading checkpoing from {path}')
save = torch.load(path)
state_dict = save['state_dict']
model.load_state_dict(state_dict)
start_epoch = save['epoch'] + 1
else:
start_epoch = 0
end_epoch = args.n_epochs
if args.run_id_pretrain is not None:
pretrain_path = EXP_DIR / args.run_id_pretrain / 'checkpoint.pth.tar'
logger.info(f'Using pretrained model from {pretrain_path}.')
model.load_state_dict(torch.load(pretrain_path)['state_dict'])
# Synchronize models across processes.
model = sync_model(model)
model = torch.nn.parallel.DistributedDataParallel(model,
device_ids=[device],
output_device=device)
# Optimizer
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
# Warmup
if args.n_epochs_warmup == 0:
lambd = lambda epoch: 1
else:
n_batches_warmup = args.n_epochs_warmup * (args.epoch_size //
args.batch_size)
lambd = lambda batch: (batch + 1) / n_batches_warmup
lr_scheduler_warmup = torch.optim.lr_scheduler.LambdaLR(optimizer, lambd)
lr_scheduler_warmup.last_epoch = start_epoch * args.epoch_size // args.batch_size
# LR schedulers
# Divide LR by 10 every args.lr_epoch_decay
lr_scheduler = torch.optim.lr_scheduler.StepLR(
optimizer,
step_size=args.lr_epoch_decay,
gamma=0.1,
)
lr_scheduler.last_epoch = start_epoch - 1
lr_scheduler.step()
for epoch in range(start_epoch, end_epoch):
meters_train = defaultdict(lambda: AverageValueMeter())
meters_val = defaultdict(lambda: AverageValueMeter())
meters_time = defaultdict(lambda: AverageValueMeter())
h = functools.partial(h_pose_custom,
model=model,
cfg=args,
n_iterations=args.n_iterations,
mesh_db=mesh_db,
input_generator=args.TCO_input_generator)
def train_epoch():
model.train()
iterator = tqdm(ds_iter_train, ncols=80)
t = time.time()
for n, sample in enumerate(iterator):
if n > 0:
meters_time['data'].add(time.time() - t)
optimizer.zero_grad()
t = time.time()
loss = h(data=sample, meters=meters_train)
meters_time['forward'].add(time.time() - t)
iterator.set_postfix(loss=loss.item())
meters_train['loss_total'].add(loss.item())
t = time.time()
loss.backward()
total_grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(),
max_norm=args.clip_grad_norm,
norm_type=2)
meters_train['grad_norm'].add(
torch.as_tensor(total_grad_norm).item())
optimizer.step()
meters_time['backward'].add(time.time() - t)
meters_time['memory'].add(torch.cuda.max_memory_allocated() /
1024.**2)
if epoch < args.n_epochs_warmup:
lr_scheduler_warmup.step()
t = time.time()
if epoch >= args.n_epochs_warmup:
lr_scheduler.step()
@torch.no_grad()
def validation():
model.eval()
for sample in tqdm(ds_iter_val, ncols=80):
loss = h(data=sample, meters=meters_val)
meters_val['loss_total'].add(loss.item())
@torch.no_grad()
def test():
model.eval()
return run_eval(eval_bundle, epoch=epoch)
train_epoch()
if epoch % args.val_epoch_interval == 0:
validation()
test_dict = None
if epoch % args.test_epoch_interval == 0:
test_dict = test()
log_dict = dict()
log_dict.update({
'grad_norm':
meters_train['grad_norm'].mean,
'grad_norm_std':
meters_train['grad_norm'].std,
'learning_rate':
optimizer.param_groups[0]['lr'],
'time_forward':
meters_time['forward'].mean,
'time_backward':
meters_time['backward'].mean,
'time_data':
meters_time['data'].mean,
'gpu_memory':
meters_time['memory'].mean,
'time':
time.time(),
'n_iterations': (epoch + 1) * len(ds_iter_train),
'n_datas':
(epoch + 1) * args.global_batch_size * len(ds_iter_train),
})
for string, meters in zip(('train', 'val'),
(meters_train, meters_val)):
for k in dict(meters).keys():
log_dict[f'{string}_{k}'] = meters[k].mean
log_dict = reduce_dict(log_dict)
if get_rank() == 0:
log(config=args,
model=model,
epoch=epoch,
log_dict=log_dict,
test_dict=test_dict)
dist.barrier()
import os
import torch
from cosypose.utils.distributed import init_distributed_mode, get_world_size, get_tmp_dir, get_rank
from cosypose.utils.logging import get_logger
logger = get_logger(__name__)
if __name__ == '__main__':
init_distributed_mode()
proc_id = get_rank()
n_tasks = get_world_size()
n_cpus = os.environ.get('N_CPUS', 'not specified')
logger.info(f'Number of processes (=num GPUs): {n_tasks}')
logger.info(f'Process ID: {proc_id}')
logger.info(f'TMP Directory for this job: {get_tmp_dir()}')
logger.info(f'GPU CUDA ID: {torch.cuda.current_device()}')
logger.info(f'Max number of CPUs for this process: {n_cpus}')
def train_detector(args):
torch.set_num_threads(1)
if args.resume_run_id:
resume_dir = EXP_DIR / args.resume_run_id
resume_args = yaml.load((resume_dir / 'config.yaml').read_text())
keep_fields = set([
'resume_run_id',
'epoch_size',
])
vars(args).update({
k: v
for k, v in vars(resume_args).items() if k not in keep_fields
})
args = check_update_config(args)
args.save_dir = EXP_DIR / args.run_id
logger.info(f"{'-'*80}")
for k, v in args.__dict__.items():
logger.info(f"{k}: {v}")
logger.info(f"{'-'*80}")
# Initialize distributed
device = torch.cuda.current_device()
init_distributed_mode()
world_size = get_world_size()
args.n_gpus = world_size
args.global_batch_size = world_size * args.batch_size
logger.info(f'Connection established with {world_size} gpus.')
# Make train/val datasets
def make_datasets(dataset_names):
datasets = []
all_labels = set()
for (ds_name, n_repeat) in dataset_names:
assert 'test' not in ds_name
ds = make_scene_dataset(ds_name)
logger.info(f'Loaded {ds_name} with {len(ds)} images.')
all_labels = all_labels.union(set(ds.all_labels))
for _ in range(n_repeat):
datasets.append(ds)
return ConcatDataset(datasets), all_labels
scene_ds_train, train_labels = make_datasets(args.train_ds_names)
scene_ds_val, _ = make_datasets(args.val_ds_names)
label_to_category_id = dict()
label_to_category_id['background'] = 0
for n, label in enumerate(sorted(list(train_labels)), 1):
label_to_category_id[label] = n
logger.info(
f'Training with {len(label_to_category_id)} categories: {label_to_category_id}'
)
args.label_to_category_id = label_to_category_id
ds_kwargs = dict(
resize=args.input_resize,
rgb_augmentation=args.rgb_augmentation,
background_augmentation=args.background_augmentation,
gray_augmentation=args.gray_augmentation,
label_to_category_id=label_to_category_id,
)
ds_train = DetectionDataset(scene_ds_train, **ds_kwargs)
ds_val = DetectionDataset(scene_ds_val, **ds_kwargs)
train_sampler = PartialSampler(ds_train, epoch_size=args.epoch_size)
ds_iter_train = DataLoader(ds_train,
sampler=train_sampler,
batch_size=args.batch_size,
num_workers=args.n_dataloader_workers,
collate_fn=collate_fn,
drop_last=False,
pin_memory=True)
ds_iter_train = MultiEpochDataLoader(ds_iter_train)
val_sampler = PartialSampler(ds_val, epoch_size=int(0.1 * args.epoch_size))
ds_iter_val = DataLoader(ds_val,
sampler=val_sampler,
batch_size=args.batch_size,
num_workers=args.n_dataloader_workers,
collate_fn=collate_fn,
drop_last=False,
pin_memory=True)
ds_iter_val = MultiEpochDataLoader(ds_iter_val)
model = create_model_detector(cfg=args,
n_classes=len(
args.label_to_category_id)).cuda()
if args.resume_run_id:
resume_dir = EXP_DIR / args.resume_run_id
path = resume_dir / 'checkpoint.pth.tar'
logger.info(f'Loading checkpoing from {path}')
save = torch.load(path)
state_dict = save['state_dict']
model.load_state_dict(state_dict)
start_epoch = save['epoch'] + 1
else:
start_epoch = 0
end_epoch = args.n_epochs
if args.run_id_pretrain is not None:
pretrain_path = EXP_DIR / args.run_id_pretrain / 'checkpoint.pth.tar'
logger.info(f'Using pretrained model from {pretrain_path}.')
model.load_state_dict(torch.load(pretrain_path)['state_dict'])
elif args.pretrain_coco:
state_dict = load_state_dict_from_url(
model_urls['maskrcnn_resnet50_fpn_coco'])
keep = lambda k: 'box_predictor' not in k and 'mask_predictor' not in k
state_dict = {k: v for k, v in state_dict.items() if keep(k)}
model.load_state_dict(state_dict, strict=False)
logger.info(
'Using model pre-trained on coco. Removed predictor heads.')
else:
logger.info('Training MaskRCNN from scratch.')
# Synchronize models across processes.
model = sync_model(model)
model = torch.nn.parallel.DistributedDataParallel(model,
device_ids=[device],
output_device=device)
# Optimizer
params = [p for p in model.parameters() if p.requires_grad]
if args.optimizer.lower() == 'sgd':
optimizer = torch.optim.SGD(params,
lr=args.lr,
weight_decay=args.weight_decay,
momentum=args.momentum)
elif args.optimizer.lower() == 'adam':
optimizer = torch.optim.Adam(params,
lr=args.lr,
weight_decay=args.weight_decay)
else:
raise ValueError(f'Unknown optimizer {args.optimizer}')
# Warmup
if args.n_epochs_warmup == 0:
lambd = lambda epoch: 1
else:
n_batches_warmup = args.n_epochs_warmup * (args.epoch_size //
args.batch_size)
lambd = lambda batch: (batch + 1) / n_batches_warmup
lr_scheduler_warmup = torch.optim.lr_scheduler.LambdaLR(optimizer, lambd)
lr_scheduler_warmup.last_epoch = start_epoch * args.epoch_size // args.batch_size
# LR schedulers
# Divide LR by 10 every args.lr_epoch_decay
lr_scheduler = torch.optim.lr_scheduler.StepLR(
optimizer,
step_size=args.lr_epoch_decay,
gamma=0.1,
)
lr_scheduler.last_epoch = start_epoch - 1
lr_scheduler.step()
for epoch in range(start_epoch, end_epoch):
meters_train = defaultdict(AverageValueMeter)
meters_val = defaultdict(AverageValueMeter)
meters_time = defaultdict(AverageValueMeter)
h = functools.partial(h_maskrcnn, model=model, cfg=args)
def train_epoch():
model.train()
iterator = tqdm(ds_iter_train, ncols=80)
t = time.time()
for n, sample in enumerate(iterator):
if n > 0:
meters_time['data'].add(time.time() - t)
optimizer.zero_grad()
t = time.time()
loss = h(data=sample, meters=meters_train)
meters_time['forward'].add(time.time() - t)
iterator.set_postfix(loss=loss.item())
meters_train['loss_total'].add(loss.item())
t = time.time()
loss.backward()
total_grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(), max_norm=np.inf, norm_type=2)
meters_train['grad_norm'].add(
torch.as_tensor(total_grad_norm).item())
optimizer.step()
meters_time['backward'].add(time.time() - t)
meters_time['memory'].add(torch.cuda.max_memory_allocated() /
1024.**2)
if epoch < args.n_epochs_warmup:
lr_scheduler_warmup.step()
t = time.time()
if epoch >= args.n_epochs_warmup:
lr_scheduler.step()
@torch.no_grad()
def validation():
model.train()
for sample in tqdm(ds_iter_val, ncols=80):
loss = h(data=sample, meters=meters_val)
meters_val['loss_total'].add(loss.item())
train_epoch()
if epoch % args.val_epoch_interval == 0:
validation()
test_dict = None
if epoch % args.test_epoch_interval == 0:
model.eval()
test_dict = run_eval(args, model, epoch)
log_dict = dict()
log_dict.update({
'grad_norm':
meters_train['grad_norm'].mean,
'grad_norm_std':
meters_train['grad_norm'].std,
'learning_rate':
optimizer.param_groups[0]['lr'],
'time_forward':
meters_time['forward'].mean,
'time_backward':
meters_time['backward'].mean,
'time_data':
meters_time['data'].mean,
'gpu_memory':
meters_time['memory'].mean,
'time':
time.time(),
'n_iterations': (epoch + 1) * len(ds_iter_train),
'n_datas':
(epoch + 1) * args.global_batch_size * len(ds_iter_train),
})
for string, meters in zip(('train', 'val'),
(meters_train, meters_val)):
for k in dict(meters).keys():
log_dict[f'{string}_{k}'] = meters[k].mean
log_dict = reduce_dict(log_dict)
if get_rank() == 0:
log(config=args,
model=model,
epoch=epoch,
log_dict=log_dict,
test_dict=test_dict)
dist.barrier()
