def extract(cls, node):
proto_layer = node.pb
param = proto_layer.prior_box_param
variance = param.variance
if len(variance) == 0:
variance = [0.1]
update_attrs = {
'width': list(param.width),
'height': list(param.height),
'flip': int(param.flip),
'clip': int(param.clip),
'variance': list(variance),
'img_size': param.img_size,
'img_h': param.img_h,
'img_w': param.img_w,
'step': param.step,
'step_h': param.step_h,
'step_w': param.step_w,
'offset': param.offset,
}
mapping_rule = merge_attrs(param, update_attrs)
mapping_rule.update(layout_attrs())
# update the attributes of the node
PriorBoxClusteredOp.update_node_stat(node, mapping_rule)
return cls.enabled
def extract(cls, node):
variance = onnx_attr(node,
'variance',
'floats',
default=[],
dst_type=lambda x: np.array(x, dtype=np.float32))
if len(variance) == 0:
variance = [0.1]
update_attrs = {
'width':
onnx_attr(node,
'width',
'floats',
dst_type=lambda x: np.array(x, dtype=np.float32)),
'height':
onnx_attr(node,
'height',
'floats',
dst_type=lambda x: np.array(x, dtype=np.float32)),
'flip':
onnx_attr(node, 'flip', 'i', default=0),
'clip':
onnx_attr(node, 'clip', 'i', default=0),
'variance':
list(variance),
'img_size':
onnx_attr(node, 'img_size', 'i', default=0),
'img_h':
onnx_attr(node, 'img_h', 'i', default=0),
'img_w':
onnx_attr(node, 'img_w', 'i', default=0),
'step':
onnx_attr(node, 'step', 'f', default=0.0),
'step_h':
onnx_attr(node, 'step_h', 'f', default=0.0),
'step_w':
onnx_attr(node, 'step_w', 'f', default=0.0),
'offset':
onnx_attr(node, 'offset', 'f', default=0.0),
}
# update the attributes of the node
PriorBoxClusteredOp.update_node_stat(node, update_attrs)
return cls.enabled