def _build_param_ops(self, onnx_graph, ugraph, op_types_cnt,
tensor_names_map):
"""Find all tensors in initialization list in onnx_graph, normally constants
Note that this method will update op_types_cnt and tensor_names_map **inplace**
"""
# find Const ops
params_dict = {}
# FIXME: avoid using internal api of other library
dict_items = _onnx_initializer_to_input_dict_items(
onnx_graph.initializer)
for name, tf_tensor in dict_items:
params_dict[name] = AttrValueConverter.GenericType(
value_name='value',
value=TensorProtoConverter.get_generic_value(
tf_tensor.op.get_attr('value')))
# build Const ops
for tensor_name, tensor_value in params_dict.items():
cnt = op_types_cnt['Const']
node_name = self._format_node_name(tensor_name, 'Const', cnt)
op_types_cnt['Const'] += 1
tensor_names_map[tensor_name] = TensorInfo(
name=self._format_tensor_name('', node_name, 0),
op_name=node_name,
dtype=tensor_value.value.dtype,
shape=list(tensor_value.value.np_array.shape),
ugraph=ugraph)
OperationInfo(name=node_name,
input_tensors=[],
output_tensors=[tensor_names_map[tensor_name]],
op_type='Const',
lib_name='onnx',
ugraph=ugraph,
op_attr={'value': tensor_value})
def parse(self, txt_file, output_nodes=None):
graph_name, _ = os.path.splitext(
os.path.basename(txt_file)
)
if output_nodes is None:
output_nodes = []
add_all_nodes = not output_nodes
ugraph = uTensorGraph(name=graph_name, output_nodes=output_nodes, lib_name='txtlib')
with open(txt_file, 'r') as fid:
for line in fid:
try:
op_name, value = line.split(' ', maxsplit=1)
except Exception:
raise ValueError('invalid line: {}'.format(line))
value = np.array(eval(value))
out_tensor = TensorInfo(
'{}:0'.format(op_name),
op_name,
dtype=value.dtype,
shape=list(value.shape),
ugraph=ugraph
)
op_info = OperationInfo(
name=op_name,
lib_name='txtlib',
ugraph=ugraph,
input_tensors=[],
output_tensors=[out_tensor],
op_type='Const',
op_attr={
"value": AttrValueConverter.GenericType(
value_name="tensor",
value=GenericTensorConverterMixin.GenericType(
np_array=value
),
)
}
)
if add_all_nodes:
ugraph.output_nodes.append(op_name)
topologic_order_graph(ugraph)
return ugraph
def _build_param_ops(self, fb_model, ugraph, tensor_names_map):
"""Const tensors are identified by buffer_index == 0. These tensors are converted to Const Op and added to ugraph
"""
subgraph = self._get_tflm_get_subgraph(fb_model)
for idx in range(0, subgraph.TensorsLength()):
tensor = subgraph.Tensors(idx)
buffer_index = tensor.Buffer()
# buffer_index == 0 if intermediate
if buffer_index == 0:
continue
node_name = re.sub(r':\d+', '',
tensor_names_map[idx].name) + "_Const"
dtype = tensor_names_map[idx].dtype
buffer_array = fb_model.Buffers(buffer_index).DataAsNumpy()
if isinstance(buffer_array, int):
continue # somehow, sometimes, the buffer contains no data, likely to be an intermediate tensor
buffer_content = fb_model.Buffers(buffer_index).DataAsNumpy().view(
dtype).reshape(tensor_names_map[idx].shape)
OperationInfo(
name=node_name,
input_tensors=[],
output_tensors=[tensor_names_map[idx]],
op_type="Const",
lib_name="tflm",
ugraph=ugraph,
op_attr={
"value":
AttrValueConverter.GenericType(
value_name="tensor",
value=GenericTensorConverterMixin.GenericType(
np_array=buffer_content),
)
},
)
self._set_tensor_node(idx, node_name, tensor_names_map)
def test_placeholder_value(placeholder_attr_value):
generic = AttrValueConverter.get_generic_value(placeholder_attr_value)
assert isinstance(generic, AttrValueConverter.__utensor_generic_type__)
tf_proto = AttrValueConverter.get_tf_value(generic)
assert isinstance(tf_proto, AttrValueConverter.__tfproto_type__)