def parse_options(self):
options = self._flatbuf_op.BuiltinOptions()
o = ResizeBilinearOptions()
o.Init(options.Bytes, options.Pos)
self.align_corners = o.AlignCorners()
self._flatbuf_options_obj = options
self._supported_options = ['align_corners']
def _convert_resize(self, method, op):
"""Generic method to Convert TFLite RESIZE operators"""
try:
from tflite.BuiltinOptions import BuiltinOptions
from tflite.Operator import Operator
from tflite.ResizeBilinearOptions import ResizeBilinearOptions
# ResizeNearestNeighborOptions was added in tflite v1.13
tflite_ver = 1120
if 'ResizeNearestNeighborOptions' in dir(BuiltinOptions):
from tflite.ResizeNearestNeighborOptions import ResizeNearestNeighborOptions
tflite_ver = 1130
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) == 2, "input tensors length should be 2"
# images, 4-D Tensor with shape NHWC.
input_tensor = input_tensors[0]
in_expr = self.get_expr(input_tensor.tensor_idx)
# size - 1-D int32 Tensor of 2 elements: new_height, new_width
target_size = tuple(self.get_tensor_value(input_tensors[1]))
# Options - align_corners (bool)
resize_options = None
align_corners = False
if method == "BILINEAR":
assert op.BuiltinOptionsType(
) == BuiltinOptions.ResizeBilinearOptions
resize_options = ResizeBilinearOptions()
elif tflite_ver >= 1130:
assert op.BuiltinOptionsType(
) == BuiltinOptions.ResizeNearestNeighborOptions
resize_options = ResizeNearestNeighborOptions()
if resize_options is not None:
op_options = op.BuiltinOptions()
resize_options.Init(op_options.Bytes, op_options.Pos)
align_corners = resize_options.AlignCorners()
# Use layout NHWC
out = _op.image.resize(in_expr, target_size, "NHWC", method,
align_corners)
return out
class ResizeBilinear(Layer):
def __init__(self, op, op_type, tflite_interpreter):
Layer.__init__(self, op, op_type, tflite_interpreter)
self.tflite_resize_bilinear_parser = ResizeBilinearOptions()
self.tflite_resize_bilinear_parser.Init(self.op.BuiltinOptions().Bytes,
self.op.BuiltinOptions().Pos)
def generate(self):
node_output_detail = self.tflite_interpreter._get_tensor_details(
self.op.Outputs(0))
node_input_detail = self.tflite_interpreter._get_tensor_details(
self.op.Inputs(0))
# create scale constant node
tensor_input_detail = self.tflite_interpreter._get_tensor_details(
self.op.Inputs(1))
source_width, source_height = node_input_detail['shape'].tolist()[1:3]
target_width, targwt_height = self.tflite_interpreter.get_tensor(
tensor_input_detail['index']).tolist()
source_size = np.array([1.0, 1.0, source_height, source_width],
dtype=np.float)
target_siz = np.array([1.0, 1.0, targwt_height, target_width],
dtype=np.float)
scale_val = target_siz / source_size
scale_constant_node = tflite_utils.create_constant_node(
self.node_name + '_scales', scale_val.shape, scale_val)
constant_info = onnx.helper.make_tensor_value_info(
name=scale_constant_node.name,
elem_type=TensorProto.FLOAT,
shape=scale_val.shape)
self.node_list.append(scale_constant_node)
self.value_infos.append(constant_info)
# create roi constant node
roi_constant_node = tflite_utils.create_constant_node(
self.node_name + 'resize_roi', [], np.array([-1],
dtype=np.float32))
self.node_list.append(roi_constant_node)
previous_onnx_node_names = self.input_nodes_name.copy()
previous_onnx_node_names.extend(
[roi_constant_node.name, scale_constant_node.name])
resize_nearest_neighbor_node = onnx.helper.make_node(
op_type='Resize',
inputs=previous_onnx_node_names,
outputs=[self.node_name],
name=self.node_name,
mode='linear',
coordinate_transformation_mode='align_corners'
if self.tflite_resize_bilinear_parser.AlignCorners() == True else
'half_pixel')
resize_nearest_neighbor_info = onnx.helper.make_tensor_value_info(
name=self.node_name,
elem_type=TensorProto.FLOAT,
shape=tflite_utils.tflite2onnx_shape_map(
node_output_detail['shape'].tolist()))
# update tables
self.node_list.append(resize_nearest_neighbor_node)
self.value_infos.append(resize_nearest_neighbor_info)
return self.node_list, self.value_infos, self.weight_node_list