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 = {
'aspect_ratio':
onnx_attr(node,
'aspect_ratio',
'floats',
dst_type=lambda x: np.array(x, dtype=np.float32)),
'min_size':
onnx_attr(node,
'min_size',
'floats',
dst_type=lambda x: np.array(x, dtype=np.float32)),
'max_size':
onnx_attr(node,
'max_size',
'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
PriorBoxOp.update_node_stat(node, update_attrs)
return cls.enabled
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 = {
'aspect_ratio': np.array(param.aspect_ratio),
'min_size': np.array(param.min_size),
'max_size': np.array(param.max_size),
'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,
}
# these params can be omitted in caffe.proto and in param as consequence,
# so check if it is set or set to default
fields = [field[0].name for field in param.ListFields()]
if 'density' in fields:
update_attrs['density'] = np.array(param.density)
if 'fixed_size' in fields:
update_attrs['fixed_size'] = np.array(param.fixed_size)
if 'fixed_ratio' in fields:
update_attrs['fixed_ratio'] = np.array(param.fixed_ratio)
mapping_rule = merge_attrs(param, update_attrs)
mapping_rule.update(layout_attrs())
# update the attributes of the node
PriorBoxOp.update_node_stat(node, mapping_rule)
return cls.enabled
def test_tf_priorbox_flip_infer(self):
graph = build_graph(nodes_attributes,
[
('node_1', 'pb'),
('pb', 'node_3'),
('node_3', 'op_output')
],
{
'node_3': {'shape': None},
'node_1': {'shape': np.array([1, 19, 19, 384])},
'pb': {
'aspect_ratio': np.array([1, 2, 0.5]),
'flip': 1,
'min_size': np.array([1]),
'max_size': np.array([1])
}
})
graph.graph['layout'] = 'NHWC'
pb_node = Node(graph, 'pb')
PriorBoxOp.priorbox_infer(pb_node)
exp_shape = np.array([1, 2, 4 * 19 * 19 * 4])
res_shape = graph.node['node_3']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
def test_caffe_priorbox_density_infer(self):
graph = build_graph(nodes_attributes,
[
('node_1', 'pb'),
('pb', 'node_3')],
{
'node_3': {'is_output': True, 'shape': None},
'node_1': {'shape': np.array([1, 128, 32, 32])},
'pb': {
'aspect_ratio': np.array([1]),
'flip': 1,
'min_size': np.array([]),
'max_size': np.array([]),
'fixed_size': np.array([32, 64, 128]),
'density': np.array([1, 2, 4]),
}
})
graph.graph['layout'] = 'NCHW'
pb_node = Node(graph, 'pb')
PriorBoxOp.priorbox_infer(pb_node)
exp_shape = np.array([1, 2, 4*32*32*21])
res_shape = graph.node['node_3']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
