def _load_model_with_nms(self, test_args):
""" This is mostly copied fomr retinanet.py """
backbone_name = test_args.get('DETECTOR', 'backbone')
print(backbone_name)
print(test_args.get('DETECTOR', 'detector_model_path'))
model = keras.models.load_model(
str(test_args.get('DETECTOR', 'detector_model_path')),
custom_objects=backbone(backbone_name).custom_objects)
# compute the anchors
features = [
model.get_layer(name).output
for name in ['P3', 'P4', 'P5', 'P6', 'P7']
]
anchors = build_anchors(AnchorParameters.default, features)
# we expect the anchors, regression and classification values as first
# output
print(len(model.outputs))
regression = model.outputs[0]
classification = model.outputs[1]
print(classification.shape[1])
print(regression.shape)
# "other" can be any additional output from custom submodels,
# by default this will be []
other = model.outputs[2:]
# apply predicted regression to anchors
boxes = layers.RegressBoxes(name='boxes')([anchors, regression])
boxes = layers.ClipBoxes(name='clipped_boxes')(
[model.inputs[0], boxes])
# filter detections (apply NMS / score threshold / select top-k)
#detections = layers.FilterDetections(
# nms=True,
# name='filtered_detections',
# nms_threshold = test_args.getfloat('DETECTOR','nms_threshold'),
# score_threshold = test_args.getfloat('DETECTOR','det_threshold'),
# max_detections = test_args.getint('DETECTOR', 'max_detections')
# )([boxes, classification] + other)
detections = layers.filter_detections.filter_detections(
boxes=boxes,
classification=classification,
other=other,
nms=True,
nms_threshold=test_args.getfloat('DETECTOR', 'nms_threshold'),
score_threshold=test_args.getfloat('DETECTOR', 'det_threshold'),
max_detections=test_args.getint('DETECTOR', 'max_detections'))
outputs = detections
# construct the model
return keras.models.Model(inputs=model.inputs,
outputs=outputs,
name='retinanet-bbox')
def retinanet_bbox(model=None,
anchor_parameters=AnchorParameters.default,
nms=True,
nms_threshold=0.5,
class_specific_filter=True,
name='retinanet-bbox',
**kwargs):
""" Construct a RetinaNet model on top of a backbone and adds convenience functions to output boxes directly.
This model uses the minimum retinanet model and appends a few layers to compute boxes within the graph.
These layers include applying the regression values to the anchors and performing NMS.
Args
model : RetinaNet model to append bbox layers to. If None, it will create a RetinaNet model using **kwargs.
anchor_parameters : Struct containing configuration for anchor generation (sizes, strides, ratios, scales).
nms : Whether to use non-maximum suppression for the filtering step.
class_specific_filter : Whether to use class specific filtering or filter for the best scoring class only.
name : Name of the model.
*kwargs : Additional kwargs to pass to the minimal retinanet model.
Returns
A keras.models.Model which takes an image as input and outputs the detections on the image.
The order is defined as follows:
```
[
boxes, scores, labels, other[0], other[1], ...
]
```
"""
if model is None:
model = retinanet(num_anchors=anchor_parameters.num_anchors(),
**kwargs)
# compute the anchors
features = [
model.get_layer(p_name).output
for p_name in ['P3', 'P4', 'P5', 'P6', 'P7']
]
anchors = __build_anchors(anchor_parameters, features)
# we expect the anchors, regression and classification values as first output
regression = model.outputs[0]
classification = model.outputs[1]
# "other" can be any additional output from custom submodels, by default this will be []
other = model.outputs[2:]
# apply predicted regression to anchors
boxes = layers.RegressBoxes(name='boxes')([anchors, regression])
boxes = layers.ClipBoxes(name='clipped_boxes')([model.inputs[0], boxes])
# filter detections (apply NMS / score threshold / select top-k)
detections = layers.FilterDetections(
nms=nms,
nms_threshold=nms_threshold,
class_specific_filter=class_specific_filter,
name='filtered_detections')([boxes, classification] + other)
outputs = detections
# construct the model
return keras.models.Model(inputs=model.inputs, outputs=outputs, name=name)