def test_runall(self):
def is_known_to_fail(model_name):
forward_failing = [
"MinkUNet_WIP",
"pointcnn",
"RSConv_4LD",
"RSConv_2LD",
"randlanet",
"ResUNet32",
"Res16UNet34",
]
if not HAS_MINKOWSKI:
forward_failing += ["Res16", "MinkUNet", "ResUNetBN2B"]
for failing in forward_failing:
if failing.lower() in model_name.lower():
return True
return False
for type_file in self.model_type_files:
associated_task = os.path.normpath(type_file).split(
os.path.sep)[-2]
models_config = OmegaConf.load(type_file)
models_config = OmegaConf.merge(models_config, self.data_config)
models_config.update("data.task", associated_task)
models_config.update("data.grid_size", 0.05)
for model_name in models_config.models.keys():
with self.subTest(model_name):
if not is_known_to_fail(model_name):
models_config.update("model_name", model_name)
dataset = get_dataset(
models_config.models[model_name].conv_type,
associated_task)
try:
model = instantiate_model(models_config, dataset)
except Exception as e:
print(e)
raise Exception(models_config)
model.set_input(dataset[0], device)
try:
model.forward()
model.backward()
except Exception as e:
print("Forward or backward failing")
raise e
try:
if has_zero_grad(model_name):
ratio = 1
else:
ratio = test_hasgrad(model)
if ratio < 1:
print(
"Model %s.%s.%s has %i%% of parameters with 0 gradient"
% (associated_task,
type_file.split("/")[-1][:-5],
model_name, 100 * ratio))
except Exception as e:
print("Model with zero gradient %s: %s" %
(type_file, model_name))
raise e
def test_runall(self):
def is_known_to_fail(model_name):
forward_failing = ["MinkUNet_WIP", "pointcnn", "RSConv_4LD", "RSConv_2LD", "randlanet"]
for failing in forward_failing:
if failing.lower() in model_name.lower():
return True
return False
def get_dataset(conv_type, task):
features = 2
if task == "registration":
if conv_type.lower() == "dense":
return PairMockDataset(features, num_points=2048)
if conv_type.lower() == "sparse":
tr = Compose(
[XYZFeature(True, True, True), GridSampling3D(size=0.01, quantize_coords=True, mode="last")]
)
return PairMockDatasetGeometric(features, transform=tr, num_points=1024)
return PairMockDatasetGeometric(features)
else:
if conv_type.lower() == "dense":
return MockDataset(features, num_points=2048)
if conv_type.lower() == "sparse":
return MockDatasetGeometric(
features,
transform=GridSampling3D(size=0.01, quantize_coords=True, mode="last"),
num_points=1024,
)
return MockDatasetGeometric(features)
for type_file in self.model_type_files:
associated_task = type_file.split("/")[-2]
models_config = OmegaConf.load(type_file)
models_config = OmegaConf.merge(models_config, self.data_config)
models_config.update("data.task", associated_task)
for model_name in models_config.models.keys():
with self.subTest(model_name):
if not is_known_to_fail(model_name):
models_config.update("model_name", model_name)
dataset = get_dataset(models_config.models[model_name].conv_type, associated_task)
model = instantiate_model(models_config, dataset)
model.set_input(dataset[0], device)
try:
model.forward()
model.backward()
except Exception as e:
print("Forward or backward failing")
raise e
try:
ratio = test_hasgrad(model)
if ratio < 1:
print(
"Model %s.%s.%s has %i%% of parameters with 0 gradient"
% (associated_task, type_file.split("/")[-1][:-5], model_name, 100 * ratio)
)
except Exception as e:
print("Model with zero gradient %s: %s" % (type_file, model_name))
raise e
def test_largekpconv(self):
params = load_model_config("segmentation", "kpconv", "KPConvPaper")
params.update("data.use_category", True)
params.update("data.first_subsampling", 0.02)
dataset = MockDatasetGeometric(5)
model = instantiate_model(params, dataset)
model.set_input(dataset[0], device)
model.forward()
model.backward()
ratio = test_hasgrad(model)
if ratio < 1:
print("Model segmentation.kpconv.KPConvPaper has %i%% of parameters with 0 gradient" % (100 * ratio))
def test_pointnet2ms(self):
params = load_model_config("segmentation", "pointnet2", "pointnet2_largemsg")
params.update("data.use_category", True)
dataset = MockDataset(5, num_points=2048)
model = instantiate_model(params, dataset)
model.set_input(dataset[0], device)
model.forward()
model.backward()
ratio = test_hasgrad(model)
if ratio < 1:
print(
"Model segmentation.pointnet2.pointnet2_largemsgs has %i%% of parameters with 0 gradient"
% (100 * ratio)
)
def test_siamese_minkowski(self):
params = load_model_config("registration", "minkowski", "MinkUNet_Fragment")
transform = Compose(
[XYZFeature(True, True, True), GridSampling3D(size=0.01, quantize_coords=True, mode="last")]
)
dataset = PairMockDatasetGeometric(5, transform=transform, num_points=1024, is_pair_ind=True)
model = instantiate_model(params, dataset)
d = dataset[0]
model.set_input(d, device)
model.forward()
model.backward()
ratio = test_hasgrad(model)
if ratio < 1:
print(
"Model registration.minkowski.MinkUNet_Fragment has %i%% of parameters with 0 gradient" % (100 * ratio)
)
def test_runall(self):
def is_known_to_fail(model_name):
forward_failing = [
"path_pretrained",
"MinkUNet_WIP",
"pointcnn",
"RSConv_4LD",
"RSConv_2LD",
"randlanet",
"PVCNN",
"ResUNet32",
]
if not HAS_MINKOWSKI:
forward_failing += [
"Res16", "MinkUNet", "ResUNetBN2B", "ResUNet32",
"Res16UNet34"
]
for cm in [2, 4, 6]:
for h in [1, 2, 3, 4]:
for s in ["", "_unshared"]:
forward_failing += [
"MS_SVCONV_B{}cm_X2_{}head{}".format(cm, h, s)
]
for failing in forward_failing:
if failing.lower() in model_name.lower():
return True
return False
def is_torch_sparse_backend(model_name):
torchsparse_backend = [
"ResUNet32",
"Res16UNet34",
]
for cm in [2, 4, 6]:
for h in [1, 2, 3, 4]:
for s in ["", "_unshared"]:
torchsparse_backend += [
"MS_SVCONV_B{}cm_X2_{}head{}".format(cm, h, s)
]
for backend in torchsparse_backend:
if backend.lower() in model_name.lower():
return True
return False
for type_file in self.model_type_files:
associated_task = os.path.normpath(type_file).split(
os.path.sep)[-2]
# models_config = OmegaConf.load(type_file)
models_config = OmegaConf.create(
{"models": OmegaConf.load(type_file)})
models_config = OmegaConf.merge(models_config, self.data_config)
# Update to OmegaConf 2.0
if omegaconf.__version__ == '1.4.1':
models_config.update("data.task", associated_task)
models_config.update("data.grid_size", 0.05)
else:
OmegaConf.update(models_config,
"data.task",
associated_task,
merge=True)
OmegaConf.update(models_config,
"data.grid_size",
0.05,
merge=True)
models = models_config.get("models")
models_keys = models.keys() if models is not None else []
for model_name in models_keys:
if model_name == 'defaults':
# workaround for recursive defaults
continue
with self.subTest(model_name):
if not is_known_to_fail(model_name):
if omegaconf.__version__ == '1.4.1':
models_config.update("model_name", model_name)
else:
OmegaConf.update(models_config,
"model_name",
model_name,
merge=True)
# modify the backend in minkowski to have the forward
if is_torch_sparse_backend(model_name):
models_config.models[
model_name].backend = "minkowski"
dataset = get_dataset(
models_config.models[model_name].conv_type,
associated_task)
try:
model = instantiate_model(models_config, dataset)
except Exception as e:
print(e)
raise Exception(models_config)
model.set_input(dataset[0], device)
try:
model.forward()
model.backward()
except Exception as e:
print("Forward or backward failing")
raise e
try:
if has_zero_grad(model_name):
ratio = 1
else:
ratio = test_hasgrad(model)
if ratio < 1:
print(
"Model %s.%s.%s has %i%% of parameters with 0 gradient"
% (associated_task,
type_file.split("/")[-1][:-5],
model_name, 100 * ratio))
except Exception as e:
print("Model with zero gradient %s: %s" %
(type_file, model_name))
raise e
