def _create_models(self, backbone_retinanet, num_classes, lr=1e-5):
"""
Creates three models (model, training_model, prediction_model).
Parameters
----------
backbone_retinanet : A function to call to create a retinanet model with a given backbone.
num_classes : The number of classes to train.
Returns
-------
model : The base model.
training_model : The training model. If multi_gpu=0, this is identical to model.
prediction_model : The model wrapped with utility functions to perform object detection
(applies regression values and performs NMS).
"""
anchor_params = None
num_anchors = None
model = backbone_retinanet(num_classes,
num_anchors=num_anchors,
modifier=None)
training_model = model
prediction_model = retinanet_bbox(model=model,
anchor_params=anchor_params)
training_model.compile(
loss={
"regression": losses.smooth_l1(),
"classification": losses.focal()
},
optimizer=keras.optimizers.adam(lr=lr, clipnorm=0.001),
)
return model, training_model, prediction_model
def _create_models(self,
backbone_retinanet,
num_classes,
weights,
freeze_backbone=False,
lr=1e-5):
"""
Creates three models (model, training_model, prediction_model).
Parameters
----------
backbone_retinanet : A function to call to create a retinanet model with a given backbone.
num_classes : The number of classes to train.
weights : The weights to load into the model.
multi_gpu : The number of GPUs to use for training.
freeze_backbone : If True, disables learning for the backbone.
config : Config parameters, None indicates the default configuration.
Returns
-------
model : The base model.
training_model : The training model. If multi_gpu=0, this is identical to model.
prediction_model : The model wrapped with utility functions to perform object detection (applies regression values and performs NMS).
"""
modifier = freeze_model if freeze_backbone else None
anchor_params = None
num_anchors = None
model = self._model_with_weights(backbone_retinanet(
num_classes, num_anchors=num_anchors, modifier=modifier),
weights=weights,
skip_mismatch=True)
training_model = model
prediction_model = retinanet_bbox(model=model,
anchor_params=anchor_params)
training_model.compile(loss={
'regression': losses.smooth_l1(),
'classification': losses.focal()
},
optimizer=keras.optimizers.adam(lr=lr,
clipnorm=0.001))
return model, training_model, prediction_model