def main(ctx: click.Context, profile: Optional[str], verbose: int,
tmpdir: str):
"""The main click command.
Sets the profile, verbosity, and tmp_dir in RVConfig.
"""
# Make sure current directory is on PYTHON_PATH
# so that we can run against modules in current dir.
sys.path.append(os.curdir)
rv_config.set_verbosity(verbosity=verbose + 1)
rv_config.set_tmp_dir_root(tmp_dir_root=tmpdir)
rv_config.set_everett_config(profile=profile)
def _run_command(cfg_json_uri: str,
command: str,
split_ind: Optional[int] = None,
num_splits: Optional[int] = None,
runner: Optional[str] = None):
"""Run a single command using a serialized PipelineConfig.
Args:
cfg_json_uri: URI of a JSON file with a serialized PipelineConfig
command: name of command to run
split_ind: the index that a split command should assume
num_splits: the total number of splits to use
runner: the name of the runner to use
"""
pipeline_cfg_dict = file_to_json(cfg_json_uri)
rv_config_dict = pipeline_cfg_dict.get('rv_config')
rv_config.set_everett_config(profile=rv_config.profile,
config_overrides=rv_config_dict)
tmp_dir_obj = rv_config.get_tmp_dir()
tmp_dir = tmp_dir_obj.name
cfg = build_config(pipeline_cfg_dict)
pipeline = cfg.build(tmp_dir)
if num_splits is not None and split_ind is None and runner is not None:
runner = registry.get_runner(runner)()
split_ind = runner.get_split_ind()
command_fn = getattr(pipeline, command)
if num_splits is not None and num_splits > 1:
msg = 'Running {} command split {}/{}...'.format(
command, split_ind + 1, num_splits)
click.secho(msg, fg='green', bold=True)
command_fn(split_ind=split_ind, num_splits=num_splits)
else:
msg = 'Running {} command...'.format(command)
click.secho(msg, fg='green', bold=True)
command_fn()
def __init__(self,
model_bundle_uri,
tmp_dir,
update_stats=False,
channel_order=None):
"""Creates a new Predictor.
Args:
model_bundle_uri: URI of the model bundle to use. Can be any
type of URI that Raster Vision can read.
tmp_dir: Temporary directory in which to store files that are used
by the Predictor. This directory is not cleaned up by this
class.
channel_order: Option for a new channel order to use for the
imagery being predicted against. If not present, the
channel_order from the original configuration in the predict
package will be used.
"""
self.tmp_dir = tmp_dir
self.update_stats = update_stats
self.model_loaded = False
bundle_path = download_if_needed(model_bundle_uri, tmp_dir)
bundle_dir = join(tmp_dir, 'bundle')
make_dir(bundle_dir)
with zipfile.ZipFile(bundle_path, 'r') as bundle_zip:
bundle_zip.extractall(path=bundle_dir)
config_path = join(bundle_dir, 'pipeline-config.json')
config_dict = file_to_json(config_path)
rv_config.set_everett_config(
config_overrides=config_dict.get('rv_config'))
config_dict = upgrade_config(config_dict)
self.pipeline = build_config(config_dict).build(tmp_dir)
self.scene = None
if not hasattr(self.pipeline, 'predict'):
raise Exception(
'pipeline in model bundle must have predict method')
self.scene = self.pipeline.config.dataset.validation_scenes[0]
if not hasattr(self.scene.raster_source, 'uris'):
raise Exception(
'raster_source in model bundle must have uris as field')
if not hasattr(self.scene.label_store, 'uri'):
raise Exception(
'label_store in model bundle must have uri as field')
for t in self.scene.raster_source.transformers:
t.update_root(bundle_dir)
if self.update_stats:
stats_analyzer = StatsAnalyzerConfig(
output_uri=join(bundle_dir, 'stats.json'))
self.pipeline.config.analyzers = [stats_analyzer]
self.scene.label_source = None
self.scene.aoi_uris = None
self.pipeline.config.dataset.train_scenes = [self.scene]
self.pipeline.config.dataset.validation_scenes = [self.scene]
self.pipeline.config.dataset.test_scenes = None
self.pipeline.config.train_uri = bundle_dir
if channel_order is not None:
self.scene.raster_source.channel_order = channel_order