def main(cfg: omegaconf.DictConfig):
# fix paths
cfg = fix_path_in_configs(CW_DIR, cfg,
[["dataset", "root"], ["model", "resume"]])
# set a seed
PytorchTools.set_seed(cfg.general.seed, cfg.general.device,
cfg.general.reproducibility)
# set a device
cfg.general.device = PytorchTools.select_device(cfg.general.device)
# get a trained model env
checkpoint, checkpoint_cfg = get_checkpoint(cfg.model.resume)
# change cfg
checkpoint_cfg.dataset.root = cfg.dataset.root
model = get_model(checkpoint_cfg)
dataset = get_dataset(checkpoint_cfg)
criterion = get_losses(checkpoint_cfg)
# set trained params
model.load_state_dict(checkpoint["model"])
# start test
test(cfg, model, dataset["test"], criterion)
print("Finish test.")
def main():
# get configs
cfg, _, _ = get_configs(CONFIG_PATH)
# make a output folder
make_folders(cfg.output_folder)
# set a seed
PytorchTools.set_seed(cfg.seed, cfg.device, cfg.reproducibility)
# set a device
cfg.device = PytorchTools.select_device(cfg.device)
# get a trained model
checkpoint, checkpoint_cfg = get_checkpoint(cfg.resume)
model = get_model(checkpoint_cfg)
dataset = get_dataset(checkpoint_cfg)
criterion = get_losses()
# set trained params
model.load_state_dict(checkpoint["model"])
eval(checkpoint_cfg, model, dataset["test"], criterion)
print("Finish test.")
def main(cfg: omegaconf.DictConfig):
# fix paths
cfg = fix_path_in_configs(
CW_DIR, cfg,
[["dataset", "root"], ["general", "sizetxt"], ["model", "resume"]])
# set a seed
PytorchTools.set_seed(cfg.general.seed, cfg.general.device,
cfg.general.reproducibility)
# set a device
cfg.general.device = PytorchTools.select_device(cfg.general.device)
# get a trained model env
checkpoint, checkpoint_cfg = get_checkpoint(cfg.model.resume)
# change cfg
checkpoint_cfg.dataset.root = cfg.dataset.root
checkpoint_cfg.dataset.name = "s3dis"
checkpoint_cfg.dataset.mode = "scene"
model = get_model(checkpoint_cfg)
dataset = get_dataset(checkpoint_cfg)
criterion = get_losses(checkpoint_cfg)
# set trained params
model.load_state_dict(checkpoint["model"])
# get ins size txt file
mean_num_pts_in_group = np.loadtxt(cfg.general.sizetxt)
# test
eval(model, dataset["test"], criterion, mean_num_pts_in_group,
cfg.general.device)
def main(cfg:omegaconf.DictConfig):
# fix paths
cfg = fix_path_in_configs(CW_DIR, cfg, [["dataset","root"]])
# set a seed
PytorchTools.set_seed(
cfg.general.seed,
cfg.general.device,
cfg.general.reproducibility
)
# set a device
cfg.general.device = PytorchTools.select_device(cfg.general.device)
model = get_model(cfg)
dataset = get_dataset(cfg)
criterion = get_losses(cfg)
optimizer = get_optimizer(cfg, model)
scheduler = get_scheduler(cfg, optimizer)
# get a logger
writer = SummaryWriter("./")
# start training
loader = tqdm(range(cfg.general.start_epoch, cfg.general.epochs),
desc="Training", ncols=70)
for epoch in loader:
# print('Epoch {}/{}:'.format(epoch, cfg.general.epochs))
# training
train_log = train(
cfg,
model,
dataset["train"],
optimizer,
criterion,
scheduler
)
dict2tensorboard(train_log, writer, epoch)
# save params and model
if (epoch+1) % cfg.general.save_epoch == 0 and \
cfg.general.save_epoch != -1:
save_params("model.path.tar", epoch+1, cfg, model, optimizer,
scheduler)
print('Epoch {}/{}:'.format(cfg.general.epochs, cfg.general.epochs))
save_params("f_model.path.tar", cfg.general.epochs, cfg, model, optimizer,
scheduler)
writer.close()
print("Finish training.")
def main():
# get configs
cfg, _, _ = get_configs(CONFIG_PATH)
# make a output folder
make_folders(cfg.output_folder)
# set a seed
PytorchTools.set_seed(cfg.seed, cfg.device, cfg.reproducibility)
# set a device
cfg.device = PytorchTools.select_device(cfg.device)
model = get_model(cfg)
dataset = get_dataset(cfg)
criterion = get_losses()
optimizer = get_optimizer(cfg, model)
scheduler = get_scheduler(cfg, optimizer)
# get a logger
writer = SummaryWriter(cfg.output_folder)
# start training
for epoch in range(cfg.start_epoch, cfg.epochs):
print('Epoch {}/{}:'.format(epoch, cfg.epochs))
# training
train_log = train(cfg, model, dataset["train"], optimizer, criterion,
scheduler)
dict2tensorboard(train_log, writer, epoch)
# save params and model
if (epoch+1) % cfg.save_epoch == 0 and cfg.save_epoch != -1:
save_params(os.path.join(cfg.output_folder, "model.path.tar"),
epoch+1, cfg, model, optimizer, scheduler)
print('Epoch {}/{}:'.format(cfg.epochs, cfg.epochs))
save_params(os.path.join(cfg.output_folder, "f_model.path.tar"),
cfg.epochs, cfg, model, optimizer, scheduler)
writer.close()
print("Finish training.")
