def test_simplernms_infer_no_shape(self):
graph = build_graph(nodes_attributes,
[('SimplerNMS_1', 'node_1')],
{'node_1': {'is_output': True, 'shape': None},
'SimplerNMS_1': {'feat_stride': 12, 'post_nms_topn': 150, 'scale': [1, 2, 3]}
})
simplernms_node = Node(graph, 'SimplerNMS_1')
SimplerNMSOp.simplernms_infer(simplernms_node)
self.assertIsNone(graph.node['node_1']['shape'])
def test_simplernms_infer_ideal(self):
graph = build_graph(nodes_attributes,
[('SimplerNMS_1', 'node_1')],
{'node_1': {'is_output': True, 'shape': None},
'SimplerNMS_1': {'feat_stride': 16, 'post_nms_topn': 150, 'scale': [1, 2, 3]}
})
simplernms_node = Node(graph, 'SimplerNMS_1')
SimplerNMSOp.simplernms_infer(simplernms_node)
exp_shape = np.array([150, 5])
res_shape = graph.node['node_1']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
self.assertEqual(simplernms_node.scale, ['1', '2', '3'])
def extract(cls, node):
proto_layer = node.pb
param = proto_layer.simpler_nms_param
update_attrs = {
'cls_threshold': param.cls_threshold,
'max_num_proposals': param.max_num_proposals,
'iou_threshold': param.iou_threshold,
'min_bbox_size': param.min_bbox_size,
'feat_stride': param.feat_stride,
'pre_nms_topn': param.pre_nms_topn,
'post_nms_topn': param.post_nms_topn,
'scale': param.scale,
}
mapping_rule = merge_attrs(param, update_attrs)
# update the attributes of the node
SimplerNMSOp.update_node_stat(node, mapping_rule)
return cls.enabled