120 lines
4.2 KiB
Python
120 lines
4.2 KiB
Python
# Copyright (c) ONNX Project Contributors
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# SPDX-License-Identifier: Apache-2.0
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from __future__ import annotations
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import numpy as np
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from onnx.reference.op_run import OpRun, RefAttrName
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def _check_dtype(val):
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dtype = val.dtype
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if not isinstance(dtype, np.dtype):
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raise TypeError(
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f"Type ({dtype}, {type(dtype)}) is not a numpy type (operator 'Constant')"
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)
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class ConstantCommon(OpRun):
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def _check(self, cst):
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if isinstance(cst, tuple):
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raise TypeError(f"Unexpected type {type(cst)} for a constant.")
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return cst
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class Constant_1(ConstantCommon):
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def __init__(self, onnx_node, run_params):
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ConstantCommon.__init__(self, onnx_node, run_params)
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self.cst = self.value
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_check_dtype(self.cst)
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def _run(self, **overridden_attributes):
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if overridden_attributes and (
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len(overridden_attributes) > 1
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or "value" not in overridden_attributes
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or id(overridden_attributes["value"]) != id(self.value)
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):
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raise RuntimeError(
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"Function attributes are not implemented for opset <= 11. Use opset > 12."
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)
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return (self._check(self.cst),)
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class Constant_9(Constant_1):
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def __init__(self, onnx_node, run_params):
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Constant_1.__init__(self, onnx_node, run_params)
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class Constant_11(ConstantCommon):
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def __init__(self, onnx_node, run_params):
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ConstantCommon.__init__(self, onnx_node, run_params)
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if getattr(self, "sparse_value", None) is None:
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self.cst = self.value
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else:
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self.cst = self.sparse_value
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_check_dtype(self.cst)
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def _run(self, **overridden_attributes):
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if overridden_attributes and (
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len(overridden_attributes) > 1
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or "value" not in overridden_attributes
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or id(overridden_attributes["value"]) != id(self.value)
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):
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raise RuntimeError(
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"Function attributes are not implemented for opset <= 11. Use opset > 12."
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)
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return (self._check(self.cst),)
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class Constant_12(ConstantCommon):
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def __init__(self, onnx_node, run_params):
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ConstantCommon.__init__(self, onnx_node, run_params)
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if hasattr(self, "sparse_value") and self.sparse_value is not None:
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self.cst_name = "sparse_value"
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self.cst = self.sparse_value
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self.cst_convert = lambda v: v
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elif hasattr(self, "value") and self.value is not None:
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self.cst_name = "value"
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self.cst = self.value
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self.cst_convert = lambda v: v
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else:
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for attr, np_dtype in {
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"value_float": np.float32,
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"value_floats": np.float32,
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"value_int": np.int64,
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"value_ints": np.int64,
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"value_string": np.str_,
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"value_strings": np.str_,
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}.items():
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if hasattr(self, attr) and getattr(self, attr) is not None:
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self.cst_name = attr
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v = getattr(self, attr)
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self.cst = (
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v if isinstance(v, RefAttrName) else np.array(v, dtype=np_dtype)
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)
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self.cst_convert = lambda v, np_dtype=np_dtype: np.array(
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v, dtype=np_dtype
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)
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break
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if not hasattr(self, "cst_name"):
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raise AttributeError(
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f"No constant is defined for operator 'Constant', outputs are {onnx_node.output}."
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)
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def _run(self, **overridden_attributes):
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if self.has_linked_attribute:
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if overridden_attributes is None:
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raise RuntimeError(
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f"Attributes are empty, cannot retrieve value for {self.cst!r}."
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)
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if self.cst_name not in overridden_attributes:
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raise RuntimeError(
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f"Cannot find attribute {self.cst_name!r} in {list(overridden_attributes)!r}."
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)
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value = overridden_attributes[self.cst_name]
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if isinstance(value, np.ndarray):
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return (value,)
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return (self.cst_convert(value),)
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return (self._check(self.cst),)
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