def use_release(self, gpus=1):
"""Use the latest DeepForest model release from github and load model.
Optionally download if release doesn't exist.
Returns:
model (object): A trained keras model
gpus: number of gpus to parallelize, default to 1
"""
# Download latest model from github release
release_tag, self.weights = utilities.use_release()
# load saved model and tag release
self.__release_version__ = release_tag
print("Loading pre-built model: {}".format(release_tag))
if gpus == 1:
with warnings.catch_warnings():
# Suppress compilte warning, not relevant here
warnings.filterwarnings("ignore", category=UserWarning)
self.model = utilities.read_model(self.weights, self.config)
# Convert model
self.prediction_model = convert_model(self.model)
elif gpus > 1:
backbone = models.backbone(self.config["backbone"])
n_classes = len(self.labels.keys())
self.model, self.training_model, self.prediction_model = create_models(
backbone.retinanet,
num_classes=n_classes,
weights=self.weights,
multi_gpu=gpus)
# add to config
self.config["weights"] = self.weights
def __init__(self, weights=None):
self.weights = weights
#Read config file
self.config = utilities.read_config()
#Load model weights if needed
if self.weights is not None:
self.model = utilities.read_model(self.weights, self.config)
else:
self.model = None
def use_release(self):
'''Use the latest DeepForest model release from github and load model. Optionally download if release doesn't exist
Returns:
model (object): A trained keras model
'''
#Download latest model from github release
weight_path = utilities.use_release()
#load weights
self.weights = weight_path
self.model = utilities.read_model(self.weights, self.config)
