def _test_create_det_records(path):
class_map = {
name: idx + 1
for idx, name in enumerate(
open(os.path.join(
'tests/dummy_data/det/voc/voc2012.names')).read().splitlines())
}
serializer = DatumSerializer('image')
Path(path).mkdir(parents=True, exist_ok=True)
det_gen = image.DetDatumGenerator('tests/dummy_data/det/voc',
gen_config=AttrDict(
has_test_annotations=True,
class_map=class_map))
gen_kwargs = {'image_set': 'ImageSets'}
sparse_features = [
'xmin', 'xmax', 'ymin', 'ymax', 'labels', 'pose', 'is_truncated',
'labels_difficult'
]
writer = TFRecordWriter(det_gen,
serializer,
path,
'train',
2,
sparse_features=sparse_features,
**gen_kwargs)
writer.create_records()
writer = TFRecordWriter(det_gen,
serializer,
path,
'val',
1,
sparse_features=sparse_features,
**gen_kwargs)
writer.create_records()
def setUp(self):
class_map = {
name: idx + 1
for idx, name in enumerate(
open(os.path.join('tests/dummy_data/det/voc/voc2012.names')).read().splitlines())
}
self.tempdir = '/tmp/test/tfrecord_det'
self.serializer = DatumSerializer('image')
Path(self.tempdir).mkdir(parents=True, exist_ok=True)
DET_GEN = image.DetDatumGenerator('tests/dummy_data/det/voc',
gen_config=AttrDict(has_test_annotations=True,
class_map=class_map))
gen_kwargs = {'image_set': 'ImageSets'}
sparse_features = [
'xmin', 'xmax', 'ymin', 'ymax', 'labels', 'pose', 'is_truncated', 'labels_difficult'
]
self.writer = TFRecordWriter(DET_GEN,
self.serializer,
self.tempdir,
'train',
2,
sparse_features=sparse_features,
**gen_kwargs)
self.writer.create_records()
self.parser = DatumParser(self.tempdir)
def setUp(self):
class_map = {
name: idx + 1
for idx, name in enumerate(
open(os.path.join('tests/dummy_data/det/voc/voc2012.names')).
read().splitlines())
}
self.det_gen = image.DetDatumGenerator('tests/dummy_data/det/voc',
gen_config=AttrDict(
has_test_annotations=True,
class_map=class_map))
def get_default_write_configs(
problem_type: str,
label_names_file: Optional[str] = None) -> ConfigBase:
"""Returns default write configs for a problem.
Args:
problem_type: Type of the problem, any one from `datum.problems.types`.
label_names_file: Path to the label name file, required for `IMAGE_DET` problem.
Returns:
A `ConfigBase` config object.
Raises:
ValueError: If label_names_file is not valid, raised only for `IMAGE_DET` problem.
"""
serializer = DatumSerializer(
problem.PROBLEM_PARAMS[problem_type]["serializer"],
datum_name_to_encoder_fn=datum_name_to_encoder)
if problem_type == problem.IMAGE_DET:
if not label_names_file:
raise ValueError(
"label_names_file must be provided for `IMAGE_DET` problem.")
if not Path(label_names_file).is_file():
raise ValueError(
f"Input {label_names_file} does not exist or not a file.")
class_map = {
name: idx + 1
for idx, name in enumerate(
open(os.path.join(label_names_file)).read().splitlines())
}
gen_config = AttrDict(has_test_annotations=True, class_map=class_map)
else:
gen_config = None
generator = partial(problem.PROBLEM_PARAMS[problem_type]["generator"],
gen_config=gen_config)
write_configs = TFRWriteConfigs()
write_configs.serializer = serializer
write_configs.generator = generator
return write_configs
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
from datum.encoder.encoder import datum_name_to_encoder
from datum.generator.image import SegDatumGenerator
from datum.serializer.serializer import DatumSerializer
from datum.utils.common_utils import AttrDict
config = {
'generator':
partial(SegDatumGenerator, gen_config=None),
'sprase_features': [],
'serializer':
DatumSerializer('image', datum_name_to_encoder_fn=datum_name_to_encoder),
'splits': ['train', 'val'],
'num_examples': {
'train': 1,
'val': 1,
}
}
cnf = AttrDict(config)
from functools import partial
from datum.encoder.encoder import datum_name_to_encoder
from datum.generator.image import DetDatumGenerator
from datum.serializer.serializer import DatumSerializer
from datum.utils.common_utils import AttrDict
LABEL_NAMES_FILE = 'tests/dummy_data/det/voc/voc2012.names'
class_map = {
name: idx + 1
for idx, name in enumerate(
open(os.path.join(LABEL_NAMES_FILE)).read().splitlines())
}
config = {
'generator':
partial(DetDatumGenerator,
gen_config=AttrDict(has_test_annotations=True,
class_map=class_map)),
'sprase_features': [],
'serializer':
DatumSerializer('image', datum_name_to_encoder_fn=datum_name_to_encoder),
'splits': ['train', 'val'],
'num_examples': {
'train': 1,
'val': 1,
}
}
cnf = AttrDict(config)