def create_coco_data():
""" Create an empty databunch for COCO dataset."""
train_tfms = []
val_tfms = []
ds_tfms = (train_tfms, val_tfms)
class_mapping = coco_class_mapping()
import tempfile
sd = ImageList([],
path=tempfile.NamedTemporaryFile().name,
ignore_empty=True).split_none()
data = sd.label_const(
0,
label_cls=ObjectDetectionCategoryList,
classes=list(class_mapping.values())).transform(ds_tfms).databunch()
data.class_mapping = class_mapping
data.classes = list(class_mapping.values())
data._is_empty = False
data._is_coco = True
data.resize_to = 416
data.chip_size = 416
return data
def from_model(cls, emd_path, data=None):
"""
Creates a YOLOv3 Object Detector from an Esri Model Definition (EMD) file.
===================== ===========================================
**Argument** **Description**
--------------------- -------------------------------------------
emd_path Required string. Path to Esri Model Definition
file.
--------------------- -------------------------------------------
data Required fastai Databunch or None. Returned data
object from `prepare_data` function or None for
inferencing.
===================== ===========================================
:returns: `YOLOv3` Object
"""
if not HAS_FASTAI:
_raise_fastai_import_error(import_exception=import_exception)
emd_path = Path(emd_path)
emd = json.load(open(emd_path))
model_file = Path(emd['ModelFile'])
chip_size = emd["ImageWidth"]
if not model_file.is_absolute():
model_file = emd_path.parent / model_file
class_mapping = {i['Value']: i['Name'] for i in emd['Classes']}
resize_to = emd.get('resize_to')
if isinstance(resize_to, list):
resize_to = (resize_to[0], resize_to[1])
data_passed = True
# Create an image databunch for when loading the model using emd (without training data)
if data is None:
data_passed = False
train_tfms = []
val_tfms = []
ds_tfms = (train_tfms, val_tfms)
with warnings.catch_warnings():
warnings.simplefilter("ignore", UserWarning)
sd = ImageList([],
path=emd_path.parent.parent).split_by_idx([])
data = sd.label_const(
0,
label_cls=ObjectDetectionCategoryList,
classes=list(class_mapping.values())).transform(
ds_tfms).databunch().normalize(imagenet_stats)
data.chip_size = chip_size
data.class_mapping = class_mapping
data.classes = ['background'] + list(class_mapping.values())
data = get_multispectral_data_params_from_emd(data, emd)
# Add 1 for background class
data.c += 1
data._is_empty = True
data.emd_path = emd_path
data.emd = emd
data.resize_to = resize_to
ret = cls(data, **emd['ModelParameters'], pretrained_path=model_file)
if not data_passed:
ret.learn.data.single_ds.classes = ret._data.classes
ret.learn.data.single_ds.y.classes = ret._data.classes
return ret
def from_emd(cls, data, emd_path):
"""
Creates a Single Shot Detector from an Esri Model Definition (EMD) file.
===================== ===========================================
**Argument** **Description**
--------------------- -------------------------------------------
data Required fastai Databunch or None. Returned data
object from `prepare_data` function or None for
inferencing.
--------------------- -------------------------------------------
emd_path Required string. Path to Esri Model Definition
file.
===================== ===========================================
:returns: `SingleShotDetector` Object
"""
emd_path = Path(emd_path)
emd = json.load(open(emd_path))
model_file = Path(emd['ModelFile'])
backbone = emd.get('backbone', 'resnet34')
ssd_version = int(emd.get('SSDVersion', 1))
chip_size = emd["ImageWidth"]
if not model_file.is_absolute():
model_file = emd_path.parent / model_file
class_mapping = {i['Value']: i['Name'] for i in emd['Classes']}
resize_to = emd.get('resize_to')
if isinstance(resize_to, list):
resize_to = (resize_to[0], resize_to[1])
data_passed = True
# Create an image databunch for when loading the model using emd (without training data)
if data is None:
data_passed = False
train_tfms = []
val_tfms = []
ds_tfms = (train_tfms, val_tfms)
with warnings.catch_warnings():
warnings.simplefilter("ignore", UserWarning)
sd = ImageList([], path=tempfile.TemporaryDirectory().name).split_by_idx([])
tempdata = sd.label_const(0, label_cls=SSDObjectCategoryList, classes=list(class_mapping.values())).transform(ds_tfms).databunch().normalize(imagenet_stats)
tempdata.chip_size = chip_size
tempdata.class_mapping = class_mapping
tempdata.classes = ['background'] + list(class_mapping.values())
data = tempdata
data.c += 1 # Add 1 for background class
data.resize_to = resize_to
ssd = cls(data, emd['Grids'], emd['Zooms'], emd['Ratios'], pretrained_path=str(model_file), backbone=backbone, ssd_version=ssd_version)
if not data_passed:
ssd.learn.data.single_ds.classes = ssd._data.classes
ssd.learn.data.single_ds.y.classes = ssd._data.classes
return ssd