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,
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 run_pred_eval(pred_runner, pred_kwargs, eval_runner, eval_preds=None):
all_predictions = dict()
for pred_prefix, pred_kwargs_n in pred_kwargs.items():
print("Prediction :", pred_prefix)
preds = pred_runner.get_predictions(**pred_kwargs_n)
for preds_name, preds_n in preds.items():
all_predictions[f'{pred_prefix}/{preds_name}'] = preds_n
all_predictions = OrderedDict({k: v for k, v in sorted(all_predictions.items(), key=lambda item: item[0])})
eval_metrics, eval_dfs = dict(), dict()
for preds_k, preds in all_predictions.items():
print("Evaluation :", preds_k)
if eval_preds is None or preds_k in eval_preds:
eval_metrics[preds_k], eval_dfs[preds_k] = eval_runner.evaluate(preds)
all_predictions = gather_predictions(all_predictions)
if get_rank() == 0:
results = format_results(all_predictions,
eval_metrics,
eval_dfs)
else:
results = None
return results
def summary(self):
summary, dfs = dict(), dict()
for meter_k, meter in sorted(self.meters.items()):
meter.gather_distributed(tmp_dir=self.tmp_dir)
if get_rank() == 0 and len(meter.datas) > 0:
summary_, df_ = meter.summary()
dfs[meter_k] = df_
for k, v in summary_.items():
summary[meter_k + '/' + k] = v
return summary, dfs
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)
def run_inference(args):
logger.info(f"{'-'*80}")
for k, v in args.__dict__.items():
logger.info(f"{k}: {v}")
logger.info(f"{'-'*80}")
scene_ds = make_scene_dataset(args.ds_name, n_frames=args.n_frames)
if args.icp:
scene_ds.load_depth = args.icp
# if args.debug and 'tless' in args.ds_name:
# # Try to debug ICP on T-LESS ??????
# view_id = 142
# mask = scene_ds.frame_index['view_id'] == view_id
# scene_ds.frame_index = scene_ds.frame_index[mask].reset_index(drop=True)
# scene_id = 1
# mask = scene_ds.frame_index['scene_id'] == scene_id
# scene_ds.frame_index = scene_ds.frame_index[mask].reset_index(drop=True)
scene_ds_multi = MultiViewWrapper(scene_ds, n_views=args.n_views)
if args.n_groups is not None:
scene_ds_multi.frame_index = scene_ds_multi.frame_index[:args.
n_groups].reset_index(
drop=True)
pred_kwargs = dict()
pred_runner = BopPredictionRunner(scene_ds_multi,
batch_size=args.pred_bsz,
cache_data=False,
n_workers=args.n_workers)
detector = load_detector(args.detector_run_id)
pose_predictor, mesh_db = load_pose_models(
coarse_run_id=args.coarse_run_id,
refiner_run_id=args.refiner_run_id,
n_workers=args.n_workers,
)
icp_refiner = None
if args.icp:
renderer = pose_predictor.coarse_model.renderer
icp_refiner = ICPRefiner(
mesh_db,
renderer=renderer,
resolution=pose_predictor.coarse_model.cfg.input_resize)
mv_predictor = None
if args.n_views > 1:
mv_predictor = MultiviewScenePredictor(mesh_db)
pred_kwargs.update({
'maskrcnn_detections':
dict(
detector=detector,
pose_predictor=pose_predictor,
n_coarse_iterations=args.n_coarse_iterations,
n_refiner_iterations=args.n_refiner_iterations,
icp_refiner=icp_refiner,
mv_predictor=mv_predictor,
)
})
all_predictions = dict()
for pred_prefix, pred_kwargs_n in pred_kwargs.items():
logger.info(f"Prediction: {pred_prefix}")
preds = pred_runner.get_predictions(**pred_kwargs_n)
for preds_name, preds_n in preds.items():
all_predictions[f'{pred_prefix}/{preds_name}'] = preds_n
logger.info("Done with inference.")
torch.distributed.barrier()
for k, v in all_predictions.items():
all_predictions[k] = v.gather_distributed(tmp_dir=get_tmp_dir()).cpu()
if get_rank() == 0:
save_dir = Path(args.save_dir)
save_dir.mkdir(exist_ok=True, parents=True)
logger.info(f'Finished inference on {args.ds_name}')
results = format_results(all_predictions, dict(), dict())
torch.save(results, save_dir / 'results.pth.tar')
(save_dir / 'config.yaml').write_text(yaml.dump(args))
logger.info(f'Saved predictions in {save_dir}')
torch.distributed.barrier()
return
def run_detection_eval(args, detector=None):
logger.info(f"{'-'*80}")
for k, v in args.__dict__.items():
logger.info(f"{k}: {v}")
logger.info(f"{'-'*80}")
scene_ds = make_scene_dataset(args.ds_name, n_frames=args.n_frames)
pred_kwargs = dict()
pred_runner = DetectionRunner(scene_ds,
batch_size=args.pred_bsz,
cache_data=len(pred_kwargs) > 1,
n_workers=args.n_workers)
if not args.skip_model_predictions:
if detector is not None:
model = detector
else:
model = load_detector(args.detector_run_id)
pred_kwargs.update(
{'model': dict(detector=model, gt_detections=False)})
all_predictions = dict()
if args.external_predictions:
if 'ycbv' in args.ds_name:
all_predictions['posecnn'] = load_posecnn_results().cpu()
elif 'tless' in args.ds_name:
all_predictions['retinanet/pix2pose'] = load_pix2pose_results(
all_detections=True).cpu()
else:
pass
for pred_prefix, pred_kwargs_n in pred_kwargs.items():
logger.info(f"Prediction: {pred_prefix}")
preds = pred_runner.get_predictions(**pred_kwargs_n)
for preds_name, preds_n in preds.items():
all_predictions[f'{pred_prefix}/{preds_name}'] = preds_n
logger.info("Done with predictions")
torch.distributed.barrier()
# Evaluation.
meters = get_meters(scene_ds)
logger.info(f"Meters: {meters}")
eval_runner = DetectionEvaluation(scene_ds,
meters,
batch_size=args.eval_bsz,
cache_data=len(all_predictions) > 1,
n_workers=args.n_workers,
sampler=pred_runner.sampler)
eval_metrics, eval_dfs = dict(), dict()
if not args.skip_evaluation:
for preds_k, preds in all_predictions.items():
do_eval = True
if do_eval:
logger.info(f"Evaluation of predictions: {preds_k}")
if len(preds) == 0:
preds = eval_runner.make_empty_predictions()
eval_metrics[preds_k], eval_dfs[
preds_k] = eval_runner.evaluate(preds)
else:
logger.info(f"Skipped: {preds_k}")
for k, v in all_predictions.items():
all_predictions[k] = v.gather_distributed(tmp_dir=get_tmp_dir()).cpu()
results = None
if get_rank() == 0:
save_dir = Path(args.save_dir)
save_dir.mkdir(exist_ok=True, parents=True)
logger.info(f'Finished evaluation on {args.ds_name}')
results = format_results(all_predictions, eval_metrics, eval_dfs)
torch.save(results, save_dir / 'results.pth.tar')
(save_dir / 'summary.txt').write_text(results.get('summary_txt', ''))
(save_dir / 'config.yaml').write_text(yaml.dump(args))
logger.info(f'Saved predictions+metrics in {save_dir}')
logger.info("Done with evaluation")
torch.distributed.barrier()
return results
def main():
loggers = [logging.getLogger(name) for name in logging.root.manager.loggerDict]
for logger in loggers:
if 'cosypose' in logger.name:
logger.setLevel(logging.DEBUG)
logger.info("Starting ...")
init_distributed_mode()
parser = argparse.ArgumentParser('Evaluation')
parser.add_argument('--config', default='tless-bop', type=str)
parser.add_argument('--debug', action='store_true')
parser.add_argument('--job_dir', default='', type=str)
parser.add_argument('--comment', default='', type=str)
parser.add_argument('--nviews', dest='n_views', default=1, type=int)
args = parser.parse_args()
coarse_run_id = None
refiner_run_id = None
n_workers = 8
n_plotters = 8
n_views = 1
n_frames = None
scene_id = None
group_id = None
n_groups = None
n_views = args.n_views
skip_mv = args.n_views < 2
skip_predictions = False
object_set = 'tless'
if 'tless' in args.config:
object_set = 'tless'
coarse_run_id = 'tless-coarse--10219'
refiner_run_id = 'tless-refiner--585928'
n_coarse_iterations = 1
n_refiner_iterations = 4
elif 'ycbv' in args.config:
object_set = 'ycbv'
refiner_run_id = 'ycbv-refiner-finetune--251020'
n_coarse_iterations = 0
n_refiner_iterations = 2
else:
raise ValueError(args.config)
if args.config == 'tless-siso':
ds_name = 'tless.primesense.test'
assert n_views == 1
elif args.config == 'tless-vivo':
ds_name = 'tless.primesense.test.bop19'
elif args.config == 'ycbv':
ds_name = 'ycbv.test.keyframes'
else:
raise ValueError(args.config)
if args.debug:
if 'tless' in args.config:
scene_id = None
group_id = 64
n_groups = 2
else:
scene_id = 48
n_groups = 2
n_frames = None
n_workers = 0
n_plotters = 0
n_rand = np.random.randint(1e10)
save_dir = RESULTS_DIR / f'{args.config}-n_views={n_views}-{args.comment}-{n_rand}'
logger.info(f"SAVE DIR: {save_dir}")
logger.info(f"Coarse: {coarse_run_id}")
logger.info(f"Refiner: {refiner_run_id}")
# Load dataset
scene_ds = make_scene_dataset(ds_name)
if scene_id is not None:
mask = scene_ds.frame_index['scene_id'] == scene_id
scene_ds.frame_index = scene_ds.frame_index[mask].reset_index(drop=True)
if n_frames is not None:
scene_ds.frame_index = scene_ds.frame_index[mask].reset_index(drop=True)[:n_frames]
# Predictions
predictor, mesh_db = load_models(coarse_run_id, refiner_run_id, n_workers=n_plotters, object_set=object_set)
mv_predictor = MultiviewScenePredictor(mesh_db)
base_pred_kwargs = dict(
n_coarse_iterations=n_coarse_iterations,
n_refiner_iterations=n_refiner_iterations,
skip_mv=skip_mv,
pose_predictor=predictor,
mv_predictor=mv_predictor,
)
if skip_predictions:
pred_kwargs = {}
elif 'tless' in ds_name:
pix2pose_detections = load_pix2pose_results(all_detections='bop19' in ds_name).cpu()
pred_kwargs = {
'pix2pose_detections': dict(
detections=pix2pose_detections,
**base_pred_kwargs
),
}
elif 'ycbv' in ds_name:
posecnn_detections = load_posecnn_results()
pred_kwargs = {
'posecnn_init': dict(
detections=posecnn_detections,
use_detections_TCO=posecnn_detections,
**base_pred_kwargs
),
}
else:
raise ValueError(ds_name)
scene_ds_pred = MultiViewWrapper(scene_ds, n_views=n_views)
if group_id is not None:
mask = scene_ds_pred.frame_index['group_id'] == group_id
scene_ds_pred.frame_index = scene_ds_pred.frame_index[mask].reset_index(drop=True)
elif n_groups is not None:
scene_ds_pred.frame_index = scene_ds_pred.frame_index[:n_groups]
pred_runner = MultiviewPredictionRunner(
scene_ds_pred, batch_size=1, n_workers=n_workers,
cache_data=len(pred_kwargs) > 1)
all_predictions = dict()
for pred_prefix, pred_kwargs_n in pred_kwargs.items():
logger.info(f"Prediction: {pred_prefix}")
preds = pred_runner.get_predictions(**pred_kwargs_n)
for preds_name, preds_n in preds.items():
all_predictions[f'{pred_prefix}/{preds_name}'] = preds_n
logger.info("Done with predictions")
torch.distributed.barrier()
# Evaluation
predictions_to_evaluate = set()
if 'ycbv' in ds_name:
det_key = 'posecnn_init'
all_predictions['posecnn'] = posecnn_detections
predictions_to_evaluate.add('posecnn')
elif 'tless' in ds_name:
det_key = 'pix2pose_detections'
else:
raise ValueError(ds_name)
predictions_to_evaluate.add(f'{det_key}/refiner/iteration={n_refiner_iterations}')
if args.n_views > 1:
for k in [
# f'ba_input',
# f'ba_output',
f'ba_output+all_cand'
]:
predictions_to_evaluate.add(f'{det_key}/{k}')
all_predictions = OrderedDict({k: v for k, v in sorted(all_predictions.items(), key=lambda item: item[0])})
# Evaluation.
meters = get_pose_meters(scene_ds)
mv_group_ids = list(iter(pred_runner.sampler))
scene_ds_ids = np.concatenate(scene_ds_pred.frame_index.loc[mv_group_ids, 'scene_ds_ids'].values)
sampler = ListSampler(scene_ds_ids)
eval_runner = PoseEvaluation(scene_ds, meters, n_workers=n_workers,
cache_data=True, batch_size=1, sampler=sampler)
eval_metrics, eval_dfs = dict(), dict()
for preds_k, preds in all_predictions.items():
if preds_k in predictions_to_evaluate:
logger.info(f"Evaluation : {preds_k} (N={len(preds)})")
if len(preds) == 0:
preds = eval_runner.make_empty_predictions()
eval_metrics[preds_k], eval_dfs[preds_k] = eval_runner.evaluate(preds)
preds.cpu()
else:
logger.info(f"Skipped: {preds_k} (N={len(preds)})")
all_predictions = gather_predictions(all_predictions)
metrics_to_print = dict()
if 'ycbv' in ds_name:
metrics_to_print.update({
f'posecnn/ADD(-S)_ntop=1_matching=CLASS/AUC/objects/mean': f'PoseCNN/AUC of ADD(-S)',
f'{det_key}/refiner/iteration={n_refiner_iterations}/ADD(-S)_ntop=1_matching=CLASS/AUC/objects/mean': f'Singleview/AUC of ADD(-S)',
f'{det_key}/refiner/iteration={n_refiner_iterations}/ADD-S_ntop=1_matching=CLASS/AUC/objects/mean': f'Singleview/AUC of ADD-S',
f'{det_key}/ba_output+all_cand/ADD(-S)_ntop=1_matching=CLASS/AUC/objects/mean': f'Multiview (n={args.n_views})/AUC of ADD(-S)',
f'{det_key}/ba_output+all_cand/ADD-S_ntop=1_matching=CLASS/AUC/objects/mean': f'Multiview (n={args.n_views})/AUC of ADD-S',
})
elif 'tless' in ds_name:
metrics_to_print.update({
f'{det_key}/refiner/iteration={n_refiner_iterations}/ADD-S_ntop=BOP_matching=OVERLAP/AUC/objects/mean': f'Singleview/AUC of ADD-S',
# f'{det_key}/refiner/iteration={n_refiner_iterations}/ADD-S_ntop=BOP_matching=BOP/0.1d': f'Singleview/ADD-S<0.1d',
f'{det_key}/refiner/iteration={n_refiner_iterations}/ADD-S_ntop=ALL_matching=BOP/mAP': f'Singleview/mAP@ADD-S<0.1d',
f'{det_key}/ba_output+all_cand/ADD-S_ntop=BOP_matching=OVERLAP/AUC/objects/mean': f'Multiview (n={args.n_views})/AUC of ADD-S',
# f'{det_key}/ba_output+all_cand/ADD-S_ntop=BOP_matching=BOP/0.1d': f'Multiview (n={args.n_views})/ADD-S<0.1d',
f'{det_key}/ba_output+all_cand/ADD-S_ntop=ALL_matching=BOP/mAP': f'Multiview (n={args.n_views}/mAP@ADD-S<0.1d)',
})
else:
raise ValueError
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}")
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()
