def targets(
ctx: click.Context,
shapefile: str,
record: Tuple[str, ...],
name: str,
every: int,
dtype: str,
normalise: bool,
random_seed: int,
) -> None:
"""Build target file from shapefile."""
record_list = list(record)
categorical = dtype == "categorical"
batchMB = ctx.obj.batchMB
catching_f = errors.catch_and_exit(targets_entrypoint)
catching_f(
batchMB,
shapefile,
record_list,
name,
every,
categorical,
normalise,
random_seed,
)
def traintest(ctx: click.Context, targets: str, split: Tuple[int, ...],
random_seed: int, name: str, features: str,
halfwidth: int) -> None:
"""Extract training and testing data to train and validate a model."""
fold, nfolds = split
catching_f = errors.catch_and_exit(traintest_entrypoint)
catching_f(targets, fold, nfolds, random_seed, name, halfwidth,
ctx.obj.nworkers, features, ctx.obj.batchMB)
def predict(
ctx: click.Context,
config: str,
checkpoint: str,
data: str
) -> None:
"""Predict using a learned model."""
catching_f = errors.catch_and_exit(predict_entrypoint)
catching_f(config, checkpoint, data, ctx.obj.batchMB, ctx.obj.gpu)
def train(
ctx: click.Context,
data: str,
config: str,
maxpoints: Optional[int],
random_seed: int,
) -> None:
"""Train a model specified by an sklearn input configuration."""
catching_f = errors.catch_and_exit(train_entrypoint)
catching_f(data, config, maxpoints, random_seed, ctx.obj.batchMB)
def tifs(ctx: click.Context, categorical: Tuple[str, ...],
continuous: Tuple[str, ...], normalise: bool, name: str,
ignore_crs: bool) -> None:
"""Build a tif stack from a set of input files."""
nworkers = ctx.obj.nworkers
batchMB = ctx.obj.batchMB
cat_list = list(categorical)
con_list = list(continuous)
catching_f = errors.catch_and_exit(tifs_entrypoint)
catching_f(nworkers, batchMB, cat_list, con_list, normalise, name,
ignore_crs)
def train(ctx: click.Context,
data: str,
config: str,
epochs: int,
batchsize: int,
test_batchsize: int,
iterations: Optional[int],
checkpoint: Optional[str]
) -> None:
"""Train a model specified by a config file."""
log.info("Ignoring batch-mb option, using specified or default batchsize")
catching_f = errors.catch_and_exit(train_entrypoint)
catching_f(data, config, epochs, batchsize, test_batchsize,
iterations, ctx.obj.gpu, checkpoint)
def query(
ctx: click.Context, strip: Tuple[int, int], name: str, features: str, halfwidth: int
) -> None:
"""Extract query data for making prediction images."""
catching_f = errors.catch_and_exit(query_entrypoint)
catching_f(features, ctx.obj.batchMB, ctx.obj.nworkers, halfwidth, strip, name)