def test_region_infer_do_softmax(self):
graph = build_graph(
nodes_attributes, [('node_1', 'region'), ('region', 'node_3'),
('node_3', 'op_output')], {
'node_3': {
'shape': None
},
'node_1': {
'shape': np.array([1, 227, 227, 3])
},
'region': {
'do_softmax': 0,
'end_axis': -1,
'axis': 1,
'classes': 80,
'coords': 4,
'mask': np.array([6, 7, 8]),
**layout_attrs()
}
})
graph.graph['layout'] = 'NHWC'
reorg_node = Node(graph, 'region')
RegionYoloOp.regionyolo_infer(reorg_node)
exp_shape = np.array([1, 227, 227, (80 + 4 + 1) * 3])
res_shape = graph.node['node_3']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
def extract(cls, node):
proto_layer = node.pb
param = proto_layer.region_yolo_param
flatten_param = proto_layer.flatten_param
axis = flatten_param.axis
end_axis = flatten_param.end_axis
coords = param.coords
classes = param.classes
num = param.num
update_attrs = {
'coords': coords,
'classes': classes,
'num': num,
'do_softmax': int(param.do_softmax),
'anchors': np.array(param.anchors),
'mask': np.array(param.mask)
}
flatten_attrs = {'axis': axis, 'end_axis': end_axis}
mapping_rule = merge_attrs(param, update_attrs)
mapping_rule.update(flatten_attrs)
mapping_rule.update(layout_attrs())
# update the attributes of the node
RegionYoloOp.update_node_stat(node, mapping_rule)
return cls.enabled
def test_region_infer_dynamic_flatten(self):
graph = build_graph(
nodes_attributes, [('node_1', 'region'), ('region', 'node_3'),
('node_3', 'op_output')],
{
'node_3': {
'shape': None,
'value': None
},
'node_1': {
'shape': shape_array(
[1, dynamic_dimension_value, 227, 227])
},
'region': {
'end_axis': 1,
'axis': 0,
'do_softmax': 1,
**layout_attrs()
}
})
graph.graph['layout'] = 'NCHW'
reorg_node = Node(graph, 'region')
RegionYoloOp.regionyolo_infer(reorg_node)
exp_shape = shape_array([dynamic_dimension_value, 227, 227])
res_shape = graph.node['node_3']['shape']
self.assertTrue(strict_compare_tensors(exp_shape, res_shape))
def test_region_infer_flatten(self):
graph = build_graph(
nodes_attributes, [('node_1', 'region'), ('region', 'node_3'),
('node_3', 'op_output')], {
'node_3': {
'shape': None,
'value': None
},
'node_1': {
'shape': np.array([1, 3, 227, 227])
},
'region': {
'end_axis': 1,
'axis': 0,
'do_softmax': 1,
**layout_attrs()
}
})
graph.graph['layout'] = 'NCHW'
reorg_node = Node(graph, 'region')
RegionYoloOp.regionyolo_infer(reorg_node)
exp_shape = np.array([1 * 3, 227, 227])
res_shape = graph.node['node_3']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
def transform_graph(self, graph: Graph, replacement_descriptions):
op_outputs = [n for n, d in graph.nodes(data=True) if 'op' in d and d['op'] == 'Result']
for op_output in op_outputs:
last_node = Node(graph, op_output).in_node(0)
op_params = dict(name=last_node.id + '/YoloRegion', axis=1, end_axis=-1)
op_params.update(replacement_descriptions)
region_layer = RegionYoloOp(graph, op_params)
region_layer_node = region_layer.create_node([last_node])
# here we remove 'axis' from 'dim_attrs' to avoid permutation from axis = 1 to axis = 2
region_layer_node.dim_attrs.remove('axis')
Result(graph).create_node([region_layer_node])
graph.remove_node(op_output)
def transform_graph(self, graph: Graph, replacement_descriptions):
graph.remove_nodes_from(graph.get_nodes_with_attributes(op='Result'))
for i, input_node_name in enumerate(
replacement_descriptions['entry_points']):
if input_node_name not in graph.nodes():
raise Error(
'TensorFlow YOLO V3 conversion mechanism was enabled. '
'Entry points "{}" were provided in the configuration file. '
'Entry points are nodes that feed YOLO Region layers. '
'Node with name {} doesn\'t exist in the graph. '
'Refer to documentation about converting YOLO models for more information.'
.format(
', '.join(replacement_descriptions['entry_points']),
input_node_name))
last_node = Node(graph, input_node_name).in_node(0)
op_params = dict(name=last_node.id + '/YoloRegion',
axis=1,
end_axis=-1,
do_softmax=0)
op_params.update(replacement_descriptions)
if 'masks' in op_params:
op_params['mask'] = op_params['masks'][i]
del op_params['masks']
region_layer_node = RegionYoloOp(graph, op_params).create_node(
[last_node])
# TODO: do we need change axis for further permutation
region_layer_node.dim_attrs.remove('axis')
Result(graph, {
'name': region_layer_node.id + '/Result'
}).create_node([region_layer_node])