def test_albumentations_mixup(single_target_csv):
def mixup(batch, alpha=1.0):
images = batch["input"]
targets = batch["target"].float().unsqueeze(1)
lam = np.random.beta(alpha, alpha)
perm = torch.randperm(images.size(0))
batch["input"] = images * lam + images[perm] * (1 - lam)
batch["target"] = targets * lam + targets[perm] * (1 - lam)
for e in batch["metadata"]:
e.update({"lam": lam})
return batch
train_transform = {
# applied only on images as ApplyToKeys is used with `input`
"post_tensor_transform": ApplyToKeys("input", AlbumentationsAdapter(albumentations.HorizontalFlip(p=0.5))),
"per_batch_transform": mixup,
}
# merge the default transform for this task with new one.
train_transform = merge_transforms(default_transforms((256, 256)), train_transform)
img_data = ImageClassificationData.from_csv(
"image",
"target",
train_file=single_target_csv,
batch_size=2,
num_workers=0,
train_transform=train_transform,
)
batch = next(iter(img_data.train_dataloader()))
assert "lam" in batch["metadata"][0]
def test_merge_transforms(base_transforms, additional_transforms, expected_result):
result = merge_transforms(base_transforms, additional_transforms)
assert result.keys() == expected_result.keys()
for key in result.keys():
if result[key] == _MOCK_TRANSFORM:
assert expected_result[key] == _MOCK_TRANSFORM
elif isinstance(result[key], nn.Sequential):
assert isinstance(expected_result[key], nn.Sequential)
assert len(result[key]) == len(expected_result[key])
for module, expected_module in zip(result[key], expected_result[key]):
assert module.func == expected_module.func
def train_default_transforms(image_size: Tuple[int, int]) -> Dict[str, Callable]:
"""During training, we apply the default transforms with additional ``RandomHorizontalFlip`` and ``ColorJitter``."""
return merge_transforms(
default_transforms(image_size), {
"post_tensor_transform": nn.Sequential(
ApplyToKeys(
[DefaultDataKeys.INPUT, DefaultDataKeys.TARGET],
KorniaParallelTransforms(K.augmentation.RandomHorizontalFlip(p=0.5)),
),
),
}
)
def train_default_transforms(
image_size: Tuple[int, int]) -> Dict[str, Callable]:
"""During training, we apply the default transforms with additional ``RandomHorizontalFlip``."""
if _KORNIA_AVAILABLE and os.getenv("FLASH_TESTING", "0") != "1":
# Better approach as all transforms are applied on tensor directly
transforms = {
"post_tensor_transform":
ApplyToKeys(DefaultDataKeys.INPUT,
K.augmentation.RandomHorizontalFlip()),
}
else:
transforms = {
"pre_tensor_transform":
ApplyToKeys(DefaultDataKeys.INPUT, T.RandomHorizontalFlip())
}
return merge_transforms(default_transforms(image_size), transforms)
def train_default_transforms(
spectrogram_size: Tuple[int, int], time_mask_param: Optional[int],
freq_mask_param: Optional[int]) -> Dict[str, Callable]:
"""During training we apply the default transforms with optional ``TimeMasking`` and ``Frequency Masking``."""
augs = []
if time_mask_param is not None:
augs.append(
ApplyToKeys(DefaultDataKeys.INPUT,
TAudio.TimeMasking(time_mask_param=time_mask_param)))
if freq_mask_param is not None:
augs.append(
ApplyToKeys(
DefaultDataKeys.INPUT,
TAudio.FrequencyMasking(freq_mask_param=freq_mask_param)))
if len(augs) > 0:
return merge_transforms(
default_transforms(spectrogram_size),
{"post_tensor_transform": nn.Sequential(*augs)})
return default_transforms(spectrogram_size)
def test_transforms(self, tmpdir):
tudataset = TUDataset(root=tmpdir, name="KKI")
train_dataset = tudataset
val_dataset = tudataset
test_dataset = tudataset
predict_dataset = tudataset
# instantiate the data module
dm = GraphClassificationData.from_datasets(
train_dataset=train_dataset,
val_dataset=val_dataset,
test_dataset=test_dataset,
predict_dataset=predict_dataset,
train_transform=merge_transforms(
GraphClassificationPreprocess.default_transforms(),
{
"pre_tensor_transform":
OneHotDegree(tudataset.num_features - 1)
},
),
val_transform=merge_transforms(
GraphClassificationPreprocess.default_transforms(),
{
"pre_tensor_transform":
OneHotDegree(tudataset.num_features - 1)
},
),
test_transform=merge_transforms(
GraphClassificationPreprocess.default_transforms(),
{
"pre_tensor_transform":
OneHotDegree(tudataset.num_features - 1)
},
),
predict_transform=merge_transforms(
GraphClassificationPreprocess.default_transforms(),
{
"pre_tensor_transform":
OneHotDegree(tudataset.num_features - 1)
},
),
batch_size=2,
)
assert dm is not None
assert dm.train_dataloader() is not None
assert dm.val_dataloader() is not None
assert dm.test_dataloader() is not None
# check training data
data = next(iter(dm.train_dataloader()))
assert list(data.x.size())[1] == tudataset.num_features * 2
assert list(data.y.size()) == [2]
# check val data
data = next(iter(dm.val_dataloader()))
assert list(data.x.size())[1] == tudataset.num_features * 2
assert list(data.y.size()) == [2]
# check test data
data = next(iter(dm.test_dataloader()))
assert list(data.x.size())[1] == tudataset.num_features * 2
assert list(data.y.size()) == [2]
