def set_transform(obj, dataset_opt):
"""This function create and set the transform to the obj as attributes
"""
obj.pre_transform = None
obj.test_transform = None
obj.train_transform = None
obj.val_transform = None
obj.inference_transform = None
for key_name in dataset_opt.keys():
if "transform" in key_name:
new_name = key_name.replace("transforms", "transform")
try:
transform = instantiate_transforms(
getattr(dataset_opt, key_name))
except Exception:
log.exception("Error trying to create {}, {}".format(
new_name, getattr(dataset_opt, key_name)))
continue
setattr(obj, new_name, transform)
inference_transform = explode_transform(obj.pre_transform)
inference_transform += explode_transform(obj.test_transform)
obj.inference_transform = Compose(
inference_transform) if len(inference_transform) > 0 else None
def test_lottery_transform_from_yaml(self):
"""
test the lottery transform when params are indicated in the yaml
"""
string = """
- transform: LotteryTransform
params:
transform_options:
- transform: GridSampling3D
params:
size: 0.1
- transform: Center
"""
conf = OmegaConf.create(string)
pos = torch.randn(10000, 3)
x = torch.randn(10000, 6)
dummy = torch.randn(10000, 6)
data = Data(pos=pos, x=x, dummy=dummy)
tr = instantiate_transforms(conf).transforms[0]
tr(data)
self.assertIsInstance(tr.random_transforms.transforms[0],
GridSampling3D)
self.assertIsInstance(tr.random_transforms.transforms[1], T.Center)
def test_random_param_transform_with_sphere_dropout(self):
"""
test the random param transform transform when params are indicated in the yaml
"""
string = """
- transform: RandomParamTransform
params:
transform_name: RandomSphereDropout
transform_params:
radius:
min: 1
max: 2
type: "float"
num_sphere:
min: 1
max: 5
type: "int"
"""
conf = OmegaConf.create(string)
pos = torch.randn(10000, 3)
x = torch.randn(10000, 6)
dummy = torch.randn(10000, 6)
data = Data(pos=pos, x=x, dummy=dummy)
tr = instantiate_transforms(conf).transforms[0]
tr(data)
def test_random_param_transform_with_grid_sampling(self):
"""
test the random param transform transform when params are indicated in the yaml
"""
string = """
- transform: RandomParamTransform
params:
transform_name: GridSampling3D
transform_params:
size:
min: 0.1
max: 0.3
type: "float"
mode:
value: "last"
"""
conf = OmegaConf.create(string)
pos = torch.randn(10000, 3)
x = torch.randn(10000, 6)
dummy = torch.randn(10000, 6)
data = Data(pos=pos, x=x, dummy=dummy)
tr = instantiate_transforms(conf).transforms[0]
tr(data)
def test_InstantiateTransforms(self):
conf = ListConfig([
{
"transform": "GridSampling",
"params": {
"size": 0.1
}
},
{
"transform": "Center"
},
])
t = instantiate_transforms(conf)
self.assertIsInstance(t.transforms[0], GridSampling)
self.assertIsInstance(t.transforms[1], T.Center)
def test_compose_transform(self):
string = """
- transform: ComposeTransform
params:
transform_options:
- transform: GridSampling3D
params:
size: 0.1
- transform: RandomNoise
params:
sigma: 0.05
"""
conf = OmegaConf.create(string)
pos = torch.randn(10000, 3)
x = torch.randn(10000, 6)
dummy = torch.randn(10000, 6)
data = Data(pos=pos, x=x, dummy=dummy)
tr = instantiate_transforms(conf)
tr(data)
