def main(cfg):
OmegaConf.set_struct(
cfg,
False) # This allows getattr and hasattr methods to function correctly
log.info(cfg.pretty())
workdir = os.path.join(BASE_DIR, cfg.model_name)
if not os.path.exists(workdir):
os.makedirs(workdir)
cfg.checkpoint_dir = workdir
cfg.tracker_options.full_res = True
local_models = {}
for fold, url in MODELS_URL[cfg.model_name].items():
local_file = os.path.join(workdir,
"{}_{}.pt".format(cfg.model_name, fold))
local_models[fold] = local_file
download_file(url, local_file)
conf_paths = []
for fold, model_name in local_models.items():
cfg.model_name = model_name.replace(".pt", "")
cfg.tracker_options.full_res = True
trainer = Trainer(cfg)
assert str(trainer._checkpoint.data_config.fold) == fold
trainer.eval(stage_name="test")
conf_path = os.path.join(workdir, "{}.npy".format(cfg.model_name))
np.save(conf_path,
trainer._tracker.full_confusion_matrix.get_confusion_matrix())
conf_paths.append(conf_path)
confusion_matrix = ConfusionMatrix.create_from_matrix(
np.sum([np.load(p) for p in conf_paths], axis=0))
log_confusion_matrix(confusion_matrix)
def main(cfg):
OmegaConf.set_struct(
cfg,
False) # This allows getattr and hasattr methods to function correctly
print(cfg.pretty())
trainer = Trainer(cfg)
trainer.eval()
def main(cfg):
OmegaConf.set_struct(cfg, False) # This allows getattr and hasattr methods to function correctly
if cfg.pretty_print:
print(OmegaConf.to_yaml(cfg))
trainer = Trainer(cfg)
trainer.train()
#
# # https://github.com/facebookresearch/hydra/issues/440
GlobalHydra.get_state().clear()
return 0
def test_trainer_on_scannet_object_detection(self):
self.path_outputs = os.path.join(DIR_PATH,
"data/scannet-fixed/outputs")
if not os.path.exists(self.path_outputs):
os.makedirs(self.path_outputs)
os.chdir(self.path_outputs)
cfg = OmegaConf.load(
os.path.join(DIR_PATH,
"data/scannet-fixed/config_object_detection.yaml"))
cfg.training.epochs = 2
cfg.training.num_workers = 0
cfg.data.is_test = True
cfg.data.dataroot = os.path.join(DIR_PATH, "data/")
trainer = Trainer(cfg)
trainer.train()
def test_trainer_on_shapenet_fixed(self):
self.path_outputs = os.path.join(DIR_PATH, "data/shapenet/outputs")
if not os.path.exists(self.path_outputs):
os.makedirs(self.path_outputs)
os.chdir(self.path_outputs)
cfg = OmegaConf.load(
os.path.join(DIR_PATH, "data/shapenet/shapenet_config.yaml"))
cfg.training.epochs = 2
cfg.training.num_workers = 0
cfg.data.is_test = True
cfg.data.dataroot = os.path.join(DIR_PATH, "data/")
trainer = Trainer(cfg)
trainer.train()
self.assertEqual(trainer.early_break, True)
self.assertEqual(trainer.profiling, False)
self.assertEqual(trainer.precompute_multi_scale, False)
self.assertEqual(trainer.wandb_log, False)
keys = [k for k in trainer._tracker.get_metrics().keys()]
self.assertEqual(keys, ["test_loss_seg", "test_Cmiou", "test_Imiou"])
trainer._cfg.voting_runs = 2
trainer.eval()
def test_trainer_on_scannet_segmentation(self):
self.path_outputs = os.path.join(DIR_PATH, "data/scannet/outputs")
if not os.path.exists(self.path_outputs):
os.makedirs(self.path_outputs)
os.chdir(self.path_outputs)
cfg = OmegaConf.load(
os.path.join(DIR_PATH, "data/scannet/config_segmentation.yaml"))
cfg.training.epochs = 2
cfg.training.num_workers = 0
cfg.data.is_test = True
cfg.data.dataroot = os.path.join(DIR_PATH, "data/")
trainer = Trainer(cfg)
trainer.train()
trainer._cfg.voting_runs = 2
trainer._cfg.tracker_options.full_res = True
trainer._cfg.tracker_options.make_submission = True
trainer.eval()