def test_prior_box_infer_ideal(self):
graph = build_graph(
nodes_attributes, [('node_1', 'detection_output_1'),
('node_2', 'detection_output_1'),
('node_3', 'detection_output_1'),
('detection_output_1', 'node_4')],
{
'node_1': {
'shape': np.array([1, 34928])
},
'node_2': {
'shape': np.array([1, 183372])
},
'node_3': {
'shape': np.array([1, 2, 34928])
},
'detection_output_1': {
"background_label_id": "0",
"clip": "1",
"code_type": "caffe.PriorBoxParameter.CENTER_SIZE",
"confidence_threshold": "0.01",
"keep_top_k": "200",
"nms_threshold": "0.5",
"num_classes": "21",
"share_location": "1",
"top_k": "200",
"variance_encoded_in_target": "0"
},
'node_4': {
'shape': np.array([1, 1, 200, 7])
},
})
multi_box_detection_node = Node(graph, 'detection_output_1')
print(multi_box_detection_node)
multi_box_detection_infer(multi_box_detection_node)
exp_shape = np.array([1, 1, 200, 7])
res_shape = graph.node['node_4']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
self.assertEqual(multi_box_detection_node.background_label_id, '0')
self.assertEqual(multi_box_detection_node.clip, '1')
self.assertEqual(multi_box_detection_node.code_type,
'caffe.PriorBoxParameter.CENTER_SIZE')
self.assertEqual(multi_box_detection_node.confidence_threshold, '0.01')
self.assertEqual(multi_box_detection_node.keep_top_k, '200')
self.assertEqual(multi_box_detection_node.nms_threshold, '0.5')
self.assertEqual(multi_box_detection_node.num_classes, 21)
self.assertEqual(multi_box_detection_node.share_location, '1')
self.assertEqual(multi_box_detection_node.top_k, '200')
self.assertEqual(multi_box_detection_node.variance_encoded_in_target,
'0')
def test_do_infer_without_top_k_dynamic_shape(self):
graph = build_graph(
nodes_attributes, [('node_1', 'detection_output_1'),
('node_2', 'detection_output_1'),
('node_3', 'detection_output_1'),
('detection_output_1', 'node_4')],
{
'node_1': {
'shape': np.array([1, 34928])
},
'node_2': {
'shape': shape_array([dynamic_dimension_value, 183372])
},
'node_3': {
'shape': np.array([1, 2, 34928])
},
'detection_output_1': {
"background_label_id": "0",
"clip": "1",
"code_type": "caffe.PriorBoxParameter.CENTER_SIZE",
"confidence_threshold": "0.01",
"keep_top_k": -1,
"nms_threshold": "0.5",
"num_classes": 21,
"share_location": "1",
"top_k": -1,
"variance_encoded_in_target": "0"
},
'node_4': {
'shape': np.array([1, 1, 69856, 7])
},
})
multi_box_detection_node = Node(graph, 'detection_output_1')
multi_box_detection_infer(multi_box_detection_node)
exp_shape = shape_array([1, 1, dynamic_dimension_value, 7])
res_shape = graph.node['node_4']['shape']
self.assertTrue(strict_compare_tensors(exp_shape, res_shape))
self.assertEqual(multi_box_detection_node.background_label_id, '0')
self.assertEqual(multi_box_detection_node.clip, '1')
self.assertEqual(multi_box_detection_node.code_type,
'caffe.PriorBoxParameter.CENTER_SIZE')
self.assertEqual(multi_box_detection_node.confidence_threshold, '0.01')
self.assertEqual(multi_box_detection_node.keep_top_k, 8732)
self.assertEqual(multi_box_detection_node.nms_threshold, '0.5')
self.assertEqual(multi_box_detection_node.num_classes, 21)
self.assertEqual(multi_box_detection_node.share_location, '1')
self.assertEqual(multi_box_detection_node.top_k, -1)
self.assertEqual(multi_box_detection_node.variance_encoded_in_target,
'0')
def test_do_infer_raise_error(self):
graph = build_graph(
nodes_attributes, [('node_1', 'detection_output_1'),
('node_2', 'detection_output_1'),
('node_3', 'detection_output_1'),
('detection_output_1', 'node_4')],
{
'node_1': {
'shape': np.array([1, 34928])
},
'node_2': {
'shape': np.array([1, 183372])
},
'node_3': {
'shape': np.array([1, 3, 34928])
},
'detection_output_1': {
"background_label_id": "0",
"clip": "1",
"code_type": "caffe.PriorBoxParameter.CENTER_SIZE",
"confidence_threshold": "0.01",
"keep_top_k": -1,
"nms_threshold": "0.5",
"num_classes": 21,
"share_location": "1",
"top_k": -1,
"variance_encoded_in_target": 0
},
'node_4': {
'shape': np.array([1, 1, 69856, 7])
},
})
multi_box_detection_node = Node(graph, 'detection_output_1')
with self.assertRaisesRegex(
Error,
'The "-2" dimension of the prior boxes must be 2 but it is "3" for node*'
):
multi_box_detection_infer(multi_box_detection_node)
def test_prior_box_infer_raise_error(self):
graph = build_graph(nodes_attributes,
[('node_1', 'detection_output_1'),
('node_2', 'detection_output_1'),
('node_3', 'detection_output_1'),
('detection_output_1', 'node_4')],
{'node_1': {'shape': np.array([1, 34928])},
'node_2': {'shape': np.array([1, 183372])},
'node_3': {'shape': np.array([1, 3, 34928])},
'detection_output_1': {"background_label_id": "0", "clip": "1",
"code_type": "caffe.PriorBoxParameter.CENTER_SIZE",
"confidence_threshold": "0.01", "keep_top_k": -1,
"nms_threshold": "0.5", "num_classes": "21",
"share_location": "1", "top_k": -1,
"variance_encoded_in_target": 0},
'node_4': {'shape': np.array([1, 1, 69856, 7])},
})
multi_box_detection_node = Node(graph, 'detection_output_1')
self.assertIsNone(multi_box_detection_infer(multi_box_detection_node))
