class Concatenation(Layer):
def __init__(self, op, op_type, tflite_interpreter):
Layer.__init__(self, op, op_type, tflite_interpreter)
self.tflite_concat_parser = ConcatenationOptions()
self.tflite_concat_parser.Init(self.op.BuiltinOptions().Bytes,
self.op.BuiltinOptions().Pos)
def generate(self):
prev_node_names = self.input_nodes_name.copy()
concat_node_name = self.node_name
concat_node = onnx.helper.make_node(
'Concat',
inputs=prev_node_names,
outputs=[concat_node_name],
axis=utils.channel_last_2_channel_first_axis_mapping(
[self.tflite_concat_parser.Axis()])[0],
name=concat_node_name)
# update tables
self.node_list.append(concat_node)
return self.node_list, self.value_infos, self.weight_node_list
def defuse_activation_function(self):
return defused_activation_node_generator(
activation_function_type=self.tflite_concat_parser.
FusedActivationFunction(),
op=self.op,
tflite_interpreter=self.tflite_interpreter)
def convert_concatenation(self, op):
"""Convert TFLite concatenation"""
try:
from tflite.Operator import Operator
from tflite.ConcatenationOptions import ConcatenationOptions
from tflite.BuiltinOptions import BuiltinOptions
from tflite.ActivationFunctionType import ActivationFunctionType
except ImportError:
raise ImportError("The tflite package must be installed")
assert isinstance(op, Operator)
input_tensors = self.get_input_tensors(op)
assert len(input_tensors) >= 1, "input tensors should greater than 1"
in_exprs = [self.get_expr(input_tensor.tensor_idx) for input_tensor in input_tensors]
output_tensors = self.get_output_tensors(op)
assert len(output_tensors) == 1, "output tensors should be 1"
assert op.BuiltinOptionsType() == BuiltinOptions.ConcatenationOptions
op_options = op.BuiltinOptions()
concatenation_options = ConcatenationOptions()
concatenation_options.Init(op_options.Bytes, op_options.Pos)
concatenation_axis = concatenation_options.Axis()
fused_activation_fn = concatenation_options.FusedActivationFunction()
# with axis in N H W C
out = _op.concatenate(in_exprs, axis=concatenation_axis)
# if we have activation fn
if fused_activation_fn != ActivationFunctionType.NONE:
out = self.convert_fused_activation_function(out, fused_activation_fn)
return out
def convert_concatenation(self, op):
""" convert TFLite concatenation"""
try:
from tflite.Operator import Operator
from tflite.ConcatenationOptions import ConcatenationOptions
from tflite.BuiltinOptions import BuiltinOptions
from tflite.ActivationFunctionType import ActivationFunctionType
except ImportError:
raise ImportError("The tflite package must be installed")
assert isinstance(op, Operator)
input_tensors = self.get_input_tensors(op)
assert len(input_tensors) >= 1, "input tensors should greater than 1"
in_exprs = [
self.get_expr(input_tensor.tensor_idx)
for input_tensor in input_tensors
]
output_tensors = self.get_output_tensors(op)
assert len(output_tensors) == 1, "output tensors should be 1"
assert op.BuiltinOptionsType() == BuiltinOptions.ConcatenationOptions
op_options = op.BuiltinOptions()
concatenation_options = ConcatenationOptions()
concatenation_options.Init(op_options.Bytes, op_options.Pos)
concatenation_axis = concatenation_options.Axis()
fused_activation_fn = concatenation_options.FusedActivationFunction()
input_shape_length = len(input_tensors[0].tensor.ShapeAsNumpy())
# TFLite is N H W C, our layout is N C H W
if input_shape_length <= 4:
axis_convert_map = [0] + list(range(2, input_shape_length)) + [1]
concatenation_axis = axis_convert_map[concatenation_axis]
else:
raise NotImplementedError(
"Not support input shape length {} of concatenatio : ".format(
str(input_shape_length)))
# with axis in N H W C
out = _op.concatenate(in_exprs, axis=concatenation_axis)
# if we have activation fn
if fused_activation_fn != ActivationFunctionType.NONE:
out = self.convert_fused_activation_function(
out, fused_activation_fn)
return out