def test_cnn_trainset_builder():
builder = CnnTrainsetBuilder(
['tests/fixtures/sen2_20161215_clipped.tif'],
'tests/fixtures/settlements.geojson',
size=128,
step_size=64)
with tempfile.TemporaryDirectory(
prefix='aplatam_test_build_trainset') as tmpdir:
builder.build(tmpdir)
metadata_path = os.path.join(tmpdir, 'metadata.json')
assert os.path.exists(metadata_path)
with open(metadata_path) as f:
metadata = json.load(f)
assert isinstance(metadata, dict)
assert metadata["version"] == __version__
list_argument = [
'size', 'step_size', 'buffer_size', 'rescale_intensity',
'lower_cut', 'upper_cut'
]
for name_argument in list_argument:
assert metadata[name_argument] == getattr(
builder, name_argument)
for dir_a, dir_b in zip(('train', 'validation', 'test'), ('t', 'f')):
dirname = os.path.join(tmpdir, dir_a, dir_b)
assert os.path.exists(dirname), '{} exists'.format(dirname)
assert glob.glob(os.path.join(
dirname, '*.jpg')), '{} contains at least one jpg file'
def main(args):
"""
Main entry point allowing external calls
Args:
args ([str]): command line parameter list
"""
args = parse_args(args)
setup_logging(args.loglevel)
opts = dict(version=__version__,
size=args.size,
step_size=args.step_size,
buffer_size=args.buffer_size,
rescale_intensity=args.rescale_intensity,
lower_cut=args.lower_cut,
upper_cut=args.upper_cut)
_logger.info('Options: %s', opts)
_logger.info('Collect all rasters from %s', args.rasters_dir)
rasters = all_raster_files(args.rasters_dir)
validate_rasters_band_count(rasters)
builder = CnnTrainsetBuilder(rasters, args.vector, **opts)
builder.build(args.output_dir)
_logger.info('Done')
def test_cnn_trainset_builder():
builder = CnnTrainsetBuilder(['tests/fixtures/sen2_20161215_clipped.tif'],
'tests/fixtures/settlements.geojson',
size=128,
step_size=64)
with tempfile.TemporaryDirectory() as tmpdir:
builder.build(tmpdir)
assert_trainset(tmpdir, builder)
def main(args):
"""
Main entry point allowing external calls
Args:
args ([str]): command line parameter list
"""
args = parse_args(args)
setup_logging(args.loglevel)
# Set default output model path, if not set
if args.output_model:
output_model = args.output_model
else:
output_model = os.path.join(args.output_dir, DEFAULT_MODEL_FILENAME)
opts = dict(
size=args.size,
step_size=args.step_size,
buffer_size=args.buffer_size,
rescale_intensity=args.rescale_intensity,
lower_cut=args.lower_cut,
upper_cut=args.upper_cut,
block_size=args.block_size,
test_size=args.test_size,
balancing_multiplier=args.balancing_multiplier,
rasters_contour=args.rasters_contour)
_logger.info('Options: %s', opts)
# Set seed number
if args.seed:
_logger.info('Seed: %d', args.seed)
random.seed(args.seed)
_logger.info('Collect all rasters from %s', args.rasters_dir)
rasters = all_raster_files(args.rasters_dir)
validate_rasters_band_count(rasters)
if not os.path.exists(args.output_dir):
builder = CnnTrainsetBuilder(rasters, args.vector, **opts)
builder.build(args.output_dir)
# Train and save model
train(
output_model,
args.output_dir,
trainable_layers=args.trainable_layers,
batch_size=args.batch_size,
epochs=args.epochs,
size=args.size)
_logger.info('Done')
def test_cnn_trainset_builder_with_no_rescale_intensity():
builder = CnnTrainsetBuilder(['tests/fixtures/sen2_20161215_clipped.tif'],
'tests/fixtures/settlements.geojson',
size=128,
step_size=64,
rescale_intensity=False)
with tempfile.TemporaryDirectory(
prefix='aplatam_test_build_trainset') as tmpdir:
builder.build(tmpdir)
assert_trainset(tmpdir, builder, empty_dirs=True)
def test_cnn_trainset_builder():
builder = CnnTrainsetBuilder(['tests/fixtures/sen2_20161215_clipped.tif'],
'tests/fixtures/settlements.geojson',
size=128,
step_size=64)
with tempfile.TemporaryDirectory(prefix='aplatam_test_') as tmpdir:
builder.build(tmpdir)
metadata_path = os.path.join(tmpdir, 'metadata.json')
with open(metadata_path) as f:
metadata = json.load(f)
assert isinstance(metadata, dict)
assert metadata['version'] == __version__