def _test(self, is_random=False):
stats_uri = os.path.join(self.tmp_dir.name, 'stats.json')
scenes = []
raster_sources = []
imgs = []
sample_prob = 0.5
for i in range(3):
rs = MockRasterSource([0, 1, 2], 3)
img = np.zeros((600, 600, 3))
img[:, :, 0] = 1 + i
img[:, :, 1] = 2 + i
img[:, :, 2] = 3 + i
if not is_random:
img[300:, 300:, :] = np.nan
imgs.append(img)
rs.set_raster(img)
raster_sources.append(rs)
scenes.append(Scene(str(i), rs))
channel_vals = list(map(lambda x: np.expand_dims(x, axis=0), imgs))
channel_vals = np.concatenate(channel_vals, axis=0)
channel_vals = np.transpose(channel_vals, [3, 0, 1, 2])
channel_vals = np.reshape(channel_vals, (3, -1))
exp_means = np.nanmean(channel_vals, axis=1)
exp_stds = np.nanstd(channel_vals, axis=1)
analyzer_cfg = StatsAnalyzerConfig(output_uri=stats_uri,
sample_prob=None)
if is_random:
analyzer_cfg = StatsAnalyzerConfig(output_uri=stats_uri,
sample_prob=sample_prob)
analyzer = analyzer_cfg.build()
analyzer.process(scenes, self.tmp_dir.name)
stats = RasterStats.load(stats_uri)
np.testing.assert_array_almost_equal(stats.means, exp_means, decimal=3)
np.testing.assert_array_almost_equal(stats.stds, exp_stds, decimal=3)
if is_random:
for rs in raster_sources:
width = rs.get_extent().get_width()
height = rs.get_extent().get_height()
exp_num_chips = round(
((width * height) / (chip_sz**2)) * sample_prob)
self.assertEqual(rs.mock._get_chip.call_count, exp_num_chips)
def _insert_analyzers(self):
has_stats_transformer = False
for s in self.dataset.get_all_scenes():
for t in s.raster_source.transformers:
if isinstance(t, StatsTransformerConfig):
has_stats_transformer = True
has_stats_analyzer = False
for a in self.analyzers:
if isinstance(a, StatsAnalyzerConfig):
has_stats_analyzer = True
break
if has_stats_transformer and not has_stats_analyzer:
self.analyzers.append(StatsAnalyzerConfig())
def _insert_analyzers(self):
# Inserts StatsAnalyzer if it's needed because a RasterSource has a
# StatsTransformer, but there isn't a StatsAnalyzer in the list of Analyzers.
has_stats_transformer = False
for s in self.dataset.get_all_scenes():
for t in s.raster_source.transformers:
if isinstance(t, StatsTransformerConfig):
has_stats_transformer = True
has_stats_analyzer = False
for a in self.analyzers:
if isinstance(a, StatsAnalyzerConfig):
has_stats_analyzer = True
break
if has_stats_transformer and not has_stats_analyzer:
self.analyzers.append(StatsAnalyzerConfig())
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
if channel_order is not None:
self.scene.raster_source.channel_order = channel_order