def __post_init__(self) -> None:
# make sure all properties are populated
common_util.check_properties_are_not_none(self, ignore=["mask_channel"])
if not self.image_channels:
raise ValueError("image_channels cannot be empty")
if not self.ground_truth_channels:
raise ValueError("ground_truth_channels cannot be empty")
def __post_init__(self) -> None:
check_properties_are_not_none(self)
# check to make sure paths are no long paths are provided
long_paths = list(filter(is_long_path, self.checkpoint_paths))
if long_paths:
raise ValueError(
f"Following paths: {long_paths} are greater than {MAX_PATH_LENGTH}"
)
def __post_init__(self) -> None:
# make sure all properties are populated
common_util.check_properties_are_not_none(self)
# ensure the center crops for the labels and mask are compatible with each other
ml_util.check_size_matches(arg1=self.mask_center_crop,
arg2=self.labels_center_crop,
matching_dimensions=self._get_matching_dimensions())
def __post_init__(self) -> None:
# make sure all properties are populated
common_util.check_properties_are_not_none(self)
ml_util.check_size_matches(arg1=self.image, arg2=self.mask,
matching_dimensions=self._get_matching_dimensions())
ml_util.check_size_matches(arg1=self.image, arg2=self.labels,
matching_dimensions=self._get_matching_dimensions())
def __post_init__(self) -> None:
common_util.check_properties_are_not_none(self)
if len(self.train_results_per_epoch) != len(self.val_results_per_epoch) != len(self.learning_rates_per_epoch):
raise Exception("train_results_per_epoch must be the same length as val_results_per_epoch found "
"and learning_rates_per_epoch, found: train_metrics_per_epoch={}, "
"val_metrics_per_epoch={}, learning_rates_per_epoch={}"
.format(len(self.train_results_per_epoch), len(self.val_results_per_epoch),
len(self.learning_rates_per_epoch)))
def __post_init__(self) -> None:
common_util.check_properties_are_not_none(self)
ml_util.check_size_matches(arg1=self.image, arg2=self.prediction,
dim1=3, dim2=3,
matching_dimensions=[])
ml_util.check_size_matches(arg1=self.image, arg2=self.labels,
dim1=3, dim2=4,
matching_dimensions=[-1, -2, -3])
def __post_init__(self) -> None:
common_util.check_properties_are_not_none(self)
# perform dataset split validity assertions
unique_train, unique_test, unique_val = self.unique_subjects()
intersection = set.intersection(set(unique_train), set(unique_test), set(unique_val))
if len(intersection) != 0:
raise ValueError("Train, Test, and Val splits must have no intersection, found: {}".format(intersection))
if (not self.allow_empty) and any([len(x) == 0 for x in [unique_train, unique_val]]):
raise ValueError("train_ids({}), val_ids({}) must have at least one value"
.format(len(unique_train), len(unique_val)))
def __post_init__(self) -> None:
# make sure all properties are populated
common_util.check_properties_are_not_none(self,
ignore=["mask_channel"])
if not self.image_channels:
raise ValueError("image_channels cannot be empty")
if not self.ground_truth_channels:
raise ValueError("ground_truth_channels cannot be empty")
if self.ground_truth_channels.count(
None) > 0 and not self.allow_incomplete_labels:
raise ValueError("all ground_truth_channels must be provided")
def __post_init__(self) -> None:
check_properties_are_not_none(self, ignore=["subject_ids"])
if len(self.model_outputs.data) != len(self.labels.data):
raise ValueError(
"model_outputs and labels must have the same length, "
f"found {len(self.model_outputs.data)} and {len(self.labels.data)}"
)
if not torch.equal(self.model_outputs.batch_sizes,
self.labels.batch_sizes):
raise ValueError(
"batch_sizes for model_outputs and labels must be equal, "
f"found {self.model_outputs.batch_sizes} and {self.labels.batch_sizes}"
)
if not torch.equal(self.model_outputs.sorted_indices,
self.labels.sorted_indices):
raise ValueError(
"sorted_indices for model_outputs and labels must be equal, "
f"found {self.model_outputs.sorted_indices} and {self.labels.sorted_indices}"
)
if not torch.equal(self.model_outputs.unsorted_indices,
self.labels.unsorted_indices):
raise ValueError(
"unsorted_indices for model_outputs and labels must be equal, "
f"found {self.model_outputs.unsorted_indices} and {self.labels.unsorted_indices}"
)
_expected_subjects = self.labels.batch_sizes.max().item()
if self.subject_ids is not None and len(
self.subject_ids) != _expected_subjects:
raise ValueError(
f"expected {_expected_subjects} subject_ids but found {len(self.subject_ids)}"
)
def __post_init__(self) -> None:
common_util.check_properties_are_not_none(self)
def pairwise_intersection(*collections: Iterable) -> Set:
"""Returns any element that appears in more than one collection."""
intersection = set()
for col1, col2 in combinations(map(set, collections), 2):
intersection |= col1 & col2
return intersection
# perform dataset split validity assertions
unique_train, unique_test, unique_val = self.unique_subjects()
intersection = pairwise_intersection(unique_train, unique_test,
unique_val)
if len(intersection) != 0:
raise ValueError(
"Train, Test, and Val splits must have no intersection, found: {}"
.format(intersection))
if self.group_column is not None:
groups_train = self.train[self.group_column].unique()
groups_test = self.test[self.group_column].unique()
groups_val = self.val[self.group_column].unique()
group_intersection = pairwise_intersection(groups_train,
groups_test, groups_val)
if len(group_intersection) != 0:
raise ValueError(
"Train, Test, and Val splits must have no intersecting groups, found: {}"
.format(group_intersection))
if (not self.allow_empty) and any(
[len(x) == 0 for x in [unique_train, unique_val]]):
raise ValueError(
"train_ids({}), val_ids({}) must have at least one value".
format(len(unique_train), len(unique_val)))
def _validate(self) -> None:
check_properties_are_not_none(self)
if len(self.checkpoints_roots) == 0:
raise ValueError("checkpoints_roots must not be empty")
def __post_init__(self) -> None:
common_util.check_properties_are_not_none(
self, ignore=["torch_cuda_random_state"])
def __post_init__(self) -> None:
common_util.check_properties_are_not_none(self)
def __post_init__(self) -> None:
check_properties_are_not_none(self)
def __post_init__(self) -> None:
common_util.check_properties_are_not_none(self, ignore=["dataset_csv_file", "run_recovery_id"])
def __post_init__(self) -> None:
common_util.check_properties_are_not_none(self, ignore=["metrics"])
