def predict(predict_package, image_uri, output_uri, update_stats,
channel_order, export_config):
"""Make predictions on the image at IMAGE_URI
using PREDICT_PACKAGE and store the
prediciton output at OUTPUT_URI.
"""
if channel_order is not None:
channel_order = [
int(channel_ind) for channel_ind in channel_order.split(' ')
]
with RVConfig.get_tmp_dir() as tmp_dir:
predictor = rv.Predictor(predict_package, tmp_dir, update_stats,
channel_order)
predictor.predict(image_uri, output_uri, export_config)
def test_prediction_package_validation(experiment, test, temp_dir, image_uri):
console_info('Checking predict command validation...')
errors = []
pp = experiment.task.predict_package_uri
predict = rv.Predictor(pp, temp_dir, channel_order=[0, 1, 7]).predict
try:
predict(image_uri, 'x.txt')
e = TestError(test,
('Predictor should have raised exception due to invalid '
'channel_order, but did not.'),
'in experiment {}'.format(experiment.id))
errors.append(e)
except ValueError:
pass
return errors
def test_prediction_package_results(experiment, test, temp_dir, scenes,
scenes_to_uris):
console_info('Checking predict package produces same results...')
errors = []
pp = experiment.task.predict_package_uri
predict = rv.Predictor(pp, temp_dir).predict
for scene_config in scenes:
# Need to write out labels and read them back,
# otherwise the floating point precision direct box
# coordinates will not match those from the PREDICT
# command, which are rounded to pixel coordinates
# via pyproj logic (in the case of rasterio crs transformer.
predictor_label_store_uri = os.path.join(
temp_dir, test.lower(), 'predictor/{}'.format(scene_config.id))
uri = scenes_to_uris[scene_config.id]
predict(uri, predictor_label_store_uri)
scene = scene_config.create_scene(experiment.task, temp_dir)
scene_labels = scene.prediction_label_store.get_labels()
extent = scene.raster_source.get_extent()
crs_transformer = scene.raster_source.get_crs_transformer()
predictor_label_store = scene_config.label_store \
.for_prediction(
predictor_label_store_uri) \
.create_store(
experiment.task,
extent,
crs_transformer,
temp_dir)
from rastervision.data import ActivateMixin
with ActivateMixin.compose(scene, predictor_label_store):
if not predictor_label_store.get_labels() == scene_labels:
e = TestError(
test, ('Predictor did not produce the same labels '
'as the Predict command'),
'for scene {} in experiment {}'.format(
scene_config.id, experiment.id))
errors.append(e)
return errors
