File size: 2,578 Bytes
b5fdc16 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import annotations
from typing import Any
import onnx
def normalize_domain(d: str) -> str:
return "" if d == "ai.onnx" else d
def is_onnx_domain(d: str) -> bool:
return normalize_domain(d) == ""
def is_onnx_op(node: onnx.NodeProto, op_type: str) -> bool:
return is_onnx_domain(node.domain) and node.op_type == op_type
def is_control_flow_op(node: onnx.NodeProto) -> bool:
return any(attr.HasField("g") or len(attr.graphs) > 0 for attr in node.attribute)
def get_node_attr_value(node: onnx.NodeProto, attr_name: str, default: Any) -> Any:
matching = [x for x in node.attribute if x.name == attr_name]
if len(matching) > 1:
raise ValueError(f"Node has multiple attributes with name {attr_name}")
if len(matching) < 1:
return default
return onnx.helper.get_attribute_value(matching[0])
def get_initializer_type(initializer: onnx.TensorProto) -> onnx.TypeProto:
type = onnx.TypeProto()
type.tensor_type.elem_type = initializer.data_type
dims = type.tensor_type.shape.dim
for dim in initializer.dims:
dims.add().dim_value = dim
return type
def get_constant_node_value(node: onnx.NodeProto, name: str) -> onnx.TensorProto | None:
if (
node.op_type != "Constant"
or node.domain not in {"", "ai.onnx"}
or len(node.attribute) != 1
):
return None
attr = node.attribute[0]
if attr.ref_attr_name:
return None
attr_name = attr.name
value = onnx.helper.get_attribute_value(attr)
if isinstance(value, onnx.TensorProto):
# Two names exist in this case: we use tensorproto as is (with original name)
return value
shape: list[int]
if attr_name == "value_int":
dtype = onnx.TensorProto.INT64
shape = []
value = [value]
elif attr_name == "value_float":
dtype = onnx.TensorProto.FLOAT
shape = []
value = [value]
elif attr_name == "value_string":
dtype = onnx.TensorProto.STRING
shape = []
value = [value]
elif attr_name == "value_ints":
dtype = onnx.TensorProto.INT64
shape = [len(value)]
elif attr_name == "value_floats":
dtype = onnx.TensorProto.FLOAT
shape = [len(value)]
elif attr_name == "value_strings":
dtype = onnx.TensorProto.STRING
shape = [len(value)]
else:
return None # sparse tensors not handled
return onnx.helper.make_tensor(name, dtype, shape, value)
|