def _run_pipeline(cfg, runner, tmp_dir, splits=1, commands=None):
cfg.update()
cfg.recursive_validate_config()
# This is to run the validation again to check any fields that may have changed
# after the Config was constructed, possibly by the update method.
build_config(cfg.dict())
cfg_json_uri = cfg.get_config_uri()
save_pipeline_config(cfg, cfg_json_uri)
pipeline = cfg.build(tmp_dir)
if not commands:
commands = pipeline.commands
runner.run(cfg_json_uri, pipeline, commands, num_splits=splits)
def from_model_bundle(model_bundle_uri: str, tmp_dir: str):
"""Create a Learner from a model bundle."""
model_bundle_path = download_if_needed(model_bundle_uri, tmp_dir)
model_bundle_dir = join(tmp_dir, 'model-bundle')
unzip(model_bundle_path, model_bundle_dir)
config_path = join(model_bundle_dir, 'pipeline-config.json')
model_path = join(model_bundle_dir, 'model.pth')
config_dict = file_to_json(config_path)
config_dict = upgrade_config(config_dict)
cfg = build_config(config_dict)
hub_dir = join(model_bundle_dir, MODULES_DIRNAME)
model_def_path = None
loss_def_path = None
# retrieve existing model definition, if available
ext_cfg = cfg.learner.model.external_def
if ext_cfg is not None:
model_def_path = get_hubconf_dir_from_cfg(ext_cfg, parent=hub_dir)
log.info(
f'Using model definition found in bundle: {model_def_path}')
# retrieve existing loss function definition, if available
ext_cfg = cfg.learner.solver.external_loss_def
if ext_cfg is not None:
loss_def_path = get_hubconf_dir_from_cfg(ext_cfg, parent=hub_dir)
log.info(f'Using loss definition found in bundle: {loss_def_path}')
return cfg.learner.build(tmp_dir=tmp_dir,
model_path=model_path,
model_def_path=model_def_path,
loss_def_path=loss_def_path)
def _test_config_upgrader(self, OldCfgType, NewCfgType, upgrader,
curr_version):
old_cfg = OldCfgType()
old_cfg_dict = old_cfg.dict()
for i in range(curr_version):
old_cfg_dict = upgrader(old_cfg_dict, version=i)
new_cfg = build_config(old_cfg_dict)
self.assertTrue(isinstance(new_cfg, NewCfgType))
def from_model_bundle(model_bundle_uri: str, tmp_dir: str):
"""Create a Learner from a model bundle."""
model_bundle_path = download_if_needed(model_bundle_uri, tmp_dir)
model_bundle_dir = join(tmp_dir, 'model-bundle')
unzip(model_bundle_path, model_bundle_dir)
config_path = join(model_bundle_dir, 'pipeline-config.json')
model_path = join(model_bundle_dir, 'model.pth')
config_dict = file_to_json(config_path)
config_dict = upgrade_config(config_dict)
cfg = build_config(config_dict)
return cfg.learner.build(tmp_dir, model_path=model_path)
def test_to_from(self):
cfg = CConfig(al=[AConfig(), ASub1Config(),
ASub2Config()],
bl=[BConfig()],
a=ASub1Config(),
b=BConfig(),
plugin_versions=self.plugin_versions,
root_uri=None,
rv_config=None)
exp_dict = {
'plugin_versions':
self.plugin_versions,
'root_uri':
None,
'rv_config':
None,
'type_hint':
'c',
'a': {
'type_hint': 'asub1',
'x': 'x',
'y': 'y'
},
'al': [{
'type_hint': 'a',
'x': 'x'
}, {
'type_hint': 'asub1',
'x': 'x',
'y': 'y'
}, {
'type_hint': 'asub2',
'x': 'x',
'y': 'y'
}],
'b': {
'type_hint': 'b',
'x': 'x'
},
'bl': [{
'type_hint': 'b',
'x': 'x'
}],
'x':
'x'
}
self.assertDictEqual(cfg.dict(), exp_dict)
self.assertEqual(build_config(exp_dict), cfg)
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
