def _get_transform(**params) -> Callable:
key_value_flag = params.pop("_key_value", False)
if key_value_flag:
transforms_composition = {
key: ConfigExperiment._get_transform(**params_)
for key, params_ in params.items()
}
transform = AugmentorCompose({
key: Augmentor(
dict_key=key,
augment_fn=transform,
input_key=key,
output_key=key,
)
for key, transform in transforms_composition.items()
})
else:
if "transforms" in params:
transforms_composition = [
ConfigExperiment._get_transform(**transform_params)
for transform_params in params["transforms"]
]
params.update(transforms=transforms_composition)
transform = TRANSFORMS.get_from_params(**params)
return transform
def get_transforms(self, stage: str = None, mode: str = None):
"""
Args:
stage (str)
mode (str)
"""
if mode == "train":
Augmentor1 = Augmentor(
dict_key="images",
augment_fn=lambda x: torch.from_numpy(x).float(),
)
Augmentor2 = Augmentor(dict_key="labels",
augment_fn=lambda x: torch.from_numpy(x))
return transforms.Compose([Augmentor1, Augmentor2])
elif mode == "valid":
Augmentor1 = Augmentor(
dict_key="images",
augment_fn=lambda x: torch.from_numpy(x).float(),
)
Augmentor2 = Augmentor(dict_key="labels",
augment_fn=lambda x: torch.from_numpy(x))
return transforms.Compose([Augmentor1, Augmentor2])
def get_transforms(
stage: str = None,
dataset: str = None
):
if dataset == 'train':
train_transforms = compose([
pre_transforms(),
hard_transforms(),
post_transforms()
])
train_data_transforms = Augmentor(
dict_key="image",
augment_fn=lambda x: train_transforms(image=x)["image"]
)
return train_data_transforms
elif dataset == 'valid':
valid_transforms = compose([pre_transforms(), post_transforms()])
valid_data_transforms = Augmentor(
dict_key="image",
augment_fn=lambda x: valid_transforms(image=x)["image"]
)
return valid_data_transforms
else:
raise NotImplementedError
dict_transform=transform,
batch_size=bs,
num_workers=num_workers,
shuffle=False,
)
loaders["train"] = train_loader
loaders["valid"] = valid_loader
return loaders
data_transform = transforms.Compose([
Augmentor(
dict_key="features",
augment_fn=lambda x: torch.from_numpy(x.copy().astype(np.float32) /
255.0).unsqueeze_(0),
),
Augmentor(
dict_key="features",
augment_fn=transforms.Normalize((0.5, ), (0.5, )),
),
Augmentor(
dict_key="targets",
augment_fn=lambda x: torch.from_numpy(x.copy().astype(np.float32) /
255.0).unsqueeze_(0),
),
])
loaders = get_loaders(data_transform)
