def validate_num_classes(vector_file: Union[str, Path], num_classes: int,
attribute_name: str, ignore_index: int):
"""Check that `num_classes` is equal to number of classes detected in the specified attribute for each GeoPackage.
FIXME: this validation **will not succeed** if a Geopackage contains only a subset of `num_classes` (e.g. 3 of 4).
Args:
vector_file: full file path of the vector image
num_classes: number of classes set in config_template.yaml
attribute_name: name of the value field representing the required classes in the vector image file
ignore_index: (int) target value that is ignored during training and does not contribute to the input gradient
Return:
List of unique attribute values found in gpkg vector file
"""
distinct_att = set()
with fiona.open(vector_file, 'r') as src:
for feature in tqdm(src,
leave=False,
position=1,
desc=f'Scanning features'):
distinct_att.add(get_key_recursive(
attribute_name,
feature)) # Use property of set to store unique values
detected_classes = len(distinct_att) - len(
[ignore_index]) if ignore_index in distinct_att else len(distinct_att)
if detected_classes != num_classes:
raise ValueError(
'The number of classes in the yaml.config {} is different than the number of classes in '
'the file {} {}'.format(num_classes, vector_file,
str(list(distinct_att))))
return distinct_att
def validate_num_classes(vector_file: Union[str, Path], num_classes: int,
attribute_name: str, ignore_index: int,
target_ids: List):
"""Check that `num_classes` is equal to number of classes detected in the specified attribute for each GeoPackage.
FIXME: this validation **will not succeed** if a Geopackage contains only a subset of `num_classes` (e.g. 3 of 4).
Args:
:param vector_file: full file path of the vector image
:param num_classes: number of classes set in config_template.yaml
:param attribute_name: name of the value field representing the required classes in the vector image file
:param ignore_index: (int) target value that is ignored during training and does not contribute to
the input gradient
:param target_ids: list of identifiers to burn from the vector file (None = use all)
Return:
List of unique attribute values found in gpkg vector file
"""
if isinstance(vector_file, str):
vector_file = Path(vector_file)
if not vector_file.is_file():
raise FileNotFoundError(f"Could not locate gkpg file at {vector_file}")
unique_att_vals = set()
with fiona.open(vector_file, 'r') as src:
for feature in tqdm(src,
leave=False,
position=1,
desc=f'Scanning features'):
# Use property of set to store unique values
unique_att_vals.add(int(get_key_recursive(attribute_name,
feature)))
# if dontcare value is defined, remove from list of unique attribute values for verification purposes
if ignore_index in unique_att_vals:
unique_att_vals.remove(ignore_index)
# if burning a subset of gpkg's classes
if target_ids:
if not len(target_ids) == num_classes:
raise ValueError(
f'Yaml parameters mismatch. \n'
f'Got target_ids {target_ids} (sample sect) with length {len(target_ids)}. '
f'Expected match with num_classes {num_classes} (global sect))'
)
# make sure target ids are a subset of all attribute values in gpkg
if not set(target_ids).issubset(unique_att_vals):
logging.warning(
f'\nFailed scan of vector file: {vector_file}\n'
f'\tExpected to find all target ids {target_ids}. \n'
f'\tFound {unique_att_vals} for attribute "{attribute_name}"')
else:
# this can happen if gpkg doens't contain all classes, thus the warning rather than exception
if len(unique_att_vals) < num_classes:
logging.warning(
f'Found {str(list(unique_att_vals))} classes in file {vector_file}. Expected {num_classes}'
)
# this should not happen, thus the exception raised
elif len(unique_att_vals) > num_classes:
raise ValueError(
f'Found {str(list(unique_att_vals))} classes in file {vector_file}. Expected {num_classes}'
)
return unique_att_vals