def get_config(runner, raw_uri, processed_uri, root_uri, test=False):
train_scene_info = get_scene_info(join(processed_uri, 'train-scenes.csv'))
val_scene_info = get_scene_info(join(processed_uri, 'val-scenes.csv'))
if test:
train_scene_info = train_scene_info[0:1]
val_scene_info = val_scene_info[0:1]
def make_scene(scene_info):
(raster_uri, label_uri) = scene_info
raster_uri = join(raw_uri, raster_uri)
label_uri = join(processed_uri, label_uri)
if test:
crop_uri = join(processed_uri, 'crops',
os.path.basename(raster_uri))
save_image_crop(raster_uri, crop_uri, size=600, min_features=5)
raster_uri = crop_uri
id = os.path.splitext(os.path.basename(raster_uri))[0]
raster_source = RasterioSourceConfig(uris=[raster_uri],
channel_order=[0, 1, 2])
label_source = ObjectDetectionLabelSourceConfig(
vector_source=GeoJSONVectorSourceConfig(
uri=label_uri, default_class_id=0, ignore_crs_field=True))
return SceneConfig(id=id,
raster_source=raster_source,
label_source=label_source)
train_scenes = [make_scene(info) for info in train_scene_info]
val_scenes = [make_scene(info) for info in val_scene_info]
class_config = ClassConfig(names=['vehicle'], colors=['red'])
chip_sz = 300
dataset = DatasetConfig(class_config=class_config,
train_scenes=train_scenes,
validation_scenes=val_scenes)
chip_options = ObjectDetectionChipOptions(neg_ratio=1.0, ioa_thresh=0.8)
predict_options = ObjectDetectionPredictOptions(merge_thresh=0.1,
score_thresh=0.5)
backend = PyTorchObjectDetectionConfig(
model=ObjectDetectionModelConfig(backbone=Backbone.resnet50),
solver=SolverConfig(lr=1e-4,
num_epochs=10,
test_num_epochs=2,
batch_sz=16,
one_cycle=True),
log_tensorboard=True,
run_tensorboard=False,
test_mode=test)
return ObjectDetectionConfig(root_uri=root_uri,
dataset=dataset,
backend=backend,
train_chip_sz=chip_sz,
predict_chip_sz=chip_sz,
chip_options=chip_options,
predict_options=predict_options)
def get_config(runner, test=False, output_dir='output'):
if runner in ['inprocess']:
raw_uri = '/opt/data/raw-data/spacenet-dataset'
processed_uri = '/opt/data/examples/spacenet/rio/processed-data'
root_uri = '/opt/data/examples/spacenet-rio-cc'
else:
raw_uri = 's3://spacenet-dataset/'
processed_uri = 's3://raster-vision-lf-dev/examples/spacenet/rio/processed-data'
root_uri = 's3://raster-vision-lf-dev/examples/spacenet-rio-cc'
root_uri = join(root_uri, output_dir)
debug = False
train_scene_info = get_scene_info(join(processed_uri, 'train-scenes.csv'))
val_scene_info = get_scene_info(join(processed_uri, 'val-scenes.csv'))
log_tensorboard = True
run_tensorboard = True
class_config = ClassConfig(names=['no_building', 'building'],
colors=['black', 'red'])
if test:
debug = True
train_scene_info = train_scene_info[0:1]
val_scene_info = val_scene_info[0:1]
def make_scene(scene_info):
(raster_uri, label_uri) = scene_info
raster_uri = join(raw_uri, raster_uri)
label_uri = join(processed_uri, label_uri)
aoi_uri = join(raw_uri, aoi_path)
if test:
crop_uri = join(processed_uri, 'crops',
os.path.basename(raster_uri))
label_crop_uri = join(processed_uri, 'crops',
os.path.basename(label_uri))
save_image_crop(raster_uri,
crop_uri,
label_uri=label_uri,
label_crop_uri=label_crop_uri,
size=600,
min_features=20,
class_config=class_config)
raster_uri = crop_uri
label_uri = label_crop_uri
id = os.path.splitext(os.path.basename(raster_uri))[0]
raster_source = RasterioSourceConfig(channel_order=[0, 1, 2],
uris=[raster_uri])
label_source = ChipClassificationLabelSourceConfig(
vector_source=GeoJSONVectorSourceConfig(uri=label_uri,
default_class_id=1),
ioa_thresh=0.5,
use_intersection_over_cell=False,
pick_min_class_id=False,
background_class_id=0,
infer_cells=True)
return SceneConfig(id=id,
raster_source=raster_source,
label_source=label_source,
aoi_uris=[aoi_uri])
train_scenes = [make_scene(info) for info in train_scene_info]
val_scenes = [make_scene(info) for info in val_scene_info]
dataset = DatasetConfig(class_config=class_config,
train_scenes=train_scenes,
validation_scenes=val_scenes)
model = ClassificationModelConfig(backbone='resnet50')
solver = SolverConfig(lr=1e-4,
num_epochs=20,
test_num_epochs=3,
batch_sz=32,
one_cycle=True)
backend = PyTorchChipClassificationConfig(model=model,
solver=solver,
log_tensorboard=log_tensorboard,
run_tensorboard=run_tensorboard)
config = ChipClassificationConfig(root_uri=root_uri,
dataset=dataset,
backend=backend,
train_chip_sz=200,
debug=debug)
return config
def get_config(runner, raw_uri, processed_uri, root_uri, test=False):
debug = False
train_scene_info = get_scene_info(join(processed_uri, 'train-scenes.csv'))
val_scene_info = get_scene_info(join(processed_uri, 'val-scenes.csv'))
log_tensorboard = True
run_tensorboard = True
class_config = ClassConfig(names=['no_building', 'building'])
if test:
debug = True
train_scene_info = train_scene_info[0:1]
val_scene_info = val_scene_info[0:1]
def make_scene(scene_info):
(raster_uri, label_uri) = scene_info
raster_uri = join(raw_uri, raster_uri)
label_uri = join(processed_uri, label_uri)
aoi_uri = join(raw_uri, aoi_path)
if test:
crop_uri = join(processed_uri, 'crops',
os.path.basename(raster_uri))
label_crop_uri = join(processed_uri, 'crops',
os.path.basename(label_uri))
save_image_crop(
raster_uri,
crop_uri,
label_uri=label_uri,
label_crop_uri=label_crop_uri,
size=600,
min_features=20,
class_config=class_config)
raster_uri = crop_uri
label_uri = label_crop_uri
id = os.path.splitext(os.path.basename(raster_uri))[0]
raster_source = RasterioSourceConfig(
channel_order=[0, 1, 2], uris=[raster_uri])
label_source = ChipClassificationLabelSourceConfig(
vector_source=GeoJSONVectorSourceConfig(
uri=label_uri, default_class_id=1, ignore_crs_field=True),
ioa_thresh=0.5,
use_intersection_over_cell=False,
pick_min_class_id=False,
background_class_id=0,
infer_cells=True)
return SceneConfig(
id=id,
raster_source=raster_source,
label_source=label_source,
aoi_uris=[aoi_uri])
chip_sz = 200
train_scenes = [make_scene(info) for info in train_scene_info]
val_scenes = [make_scene(info) for info in val_scene_info]
dataset = DatasetConfig(
class_config=class_config,
train_scenes=train_scenes,
validation_scenes=val_scenes)
model = ClassificationModelConfig(backbone=Backbone.resnet50)
solver = SolverConfig(
lr=1e-4, num_epochs=20, test_num_epochs=3, batch_sz=32, one_cycle=True)
backend = PyTorchChipClassificationConfig(
model=model,
solver=solver,
log_tensorboard=log_tensorboard,
run_tensorboard=run_tensorboard,
test_mode=test)
config = ChipClassificationConfig(
root_uri=root_uri,
dataset=dataset,
backend=backend,
train_chip_sz=chip_sz,
predict_chip_sz=chip_sz)
return config