实现转换器的两种方法

有两种方法可以编写转换器。第一种方法不太冗长，更容易理解（参见 k_means.py）。另一种方法非常冗长（参见 ada_boost.py 获取示例）。

第一种方法在 实现新的转换器 中使用。此示例展示了第二种方法，通常在其他转换器库中使用。它更加冗长。

自定义模型

它基本上复制了示例 :ref:`l-plot-custom-converter 中的内容。

from skl2onnx.common.data_types import guess_proto_type
from onnxconverter_common.onnx_ops import apply_sub
from onnxruntime import InferenceSession
from skl2onnx import update_registered_converter
from skl2onnx import to_onnx
import numpy
from sklearn.base import TransformerMixin, BaseEstimator
from sklearn.datasets import load_iris


class DecorrelateTransformer(TransformerMixin, BaseEstimator):
    """
    Decorrelates correlated gaussian features.

    :param alpha: avoids non inversible matrices
        by adding *alpha* identity matrix

    *Attributes*

    * `self.mean_`: average
    * `self.coef_`: square root of the coveriance matrix
    """

    def __init__(self, alpha=0.0):
        BaseEstimator.__init__(self)
        TransformerMixin.__init__(self)
        self.alpha = alpha

    def fit(self, X, y=None, sample_weights=None):
        if sample_weights is not None:
            raise NotImplementedError("sample_weights != None is not implemented.")
        self.mean_ = numpy.mean(X, axis=0, keepdims=True)
        X = X - self.mean_
        V = X.T @ X / X.shape[0]
        if self.alpha != 0:
            V += numpy.identity(V.shape[0]) * self.alpha
        L, P = numpy.linalg.eig(V)
        Linv = L ** (-0.5)
        diag = numpy.diag(Linv)
        root = P @ diag @ P.transpose()
        self.coef_ = root
        return self

    def transform(self, X):
        return (X - self.mean_) @ self.coef_


data = load_iris()
X = data.data

dec = DecorrelateTransformer()
dec.fit(X)
pred = dec.transform(X[:5])
print(pred)

[[ 0.0167562   0.52111756 -1.24946737 -0.56194325]
 [-0.0727878  -0.80853732 -1.43841018 -0.37441392]
 [-0.69971891 -0.09950908 -1.2138161  -0.3499275 ]
 [-1.13063404 -0.13540568 -0.79087008 -0.73938966]
 [-0.35790036  0.91900236 -1.04034399 -0.6509266 ]]

转换为 ONNX

形状计算器不会更改。

def decorrelate_transformer_shape_calculator(operator):
    op = operator.raw_operator
    input_type = operator.inputs[0].type.__class__
    # The shape may be unknown. *get_first_dimension*
    # returns the appropriate value, None in most cases
    # meaning the transformer can process any batch of observations.
    input_dim = operator.inputs[0].get_first_dimension()
    output_type = input_type([input_dim, op.coef_.shape[1]])
    operator.outputs[0].type = output_type

转换器不同。

def decorrelate_transformer_converter(scope, operator, container):
    op = operator.raw_operator
    out = operator.outputs

    # We retrieve the unique input.
    X = operator.inputs[0]

    # In most case, computation happen in floats.
    # But it might be with double. ONNX is very strict
    # about types, every constant should have the same
    # type as the input.
    proto_dtype = guess_proto_type(X.type)

    mean_name = scope.get_unique_variable_name("mean")
    container.add_initializer(
        mean_name, proto_dtype, op.mean_.shape, list(op.mean_.ravel())
    )

    coef_name = scope.get_unique_variable_name("coef")
    container.add_initializer(
        coef_name, proto_dtype, op.coef_.shape, list(op.coef_.ravel())
    )

    op_name = scope.get_unique_operator_name("sub")
    sub_name = scope.get_unique_variable_name("sub")
    # This function is defined in package onnxconverter_common.
    # Most common operators can be added to the graph with
    # these functions. It handles the case when specifications
    # changed accross opsets (a parameter becomes an input
    # for example).
    apply_sub(
        scope, [X.full_name, mean_name], sub_name, container, operator_name=op_name
    )

    op_name = scope.get_unique_operator_name("matmul")
    container.add_node("MatMul", [sub_name, coef_name], out[0].full_name, name=op_name)

我们需要让 skl2onnx 了解新的转换器。

update_registered_converter(
    DecorrelateTransformer,
    "SklearnDecorrelateTransformer",
    decorrelate_transformer_shape_calculator,
    decorrelate_transformer_converter,
)


onx = to_onnx(dec, X.astype(numpy.float32))

sess = InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"])

exp = dec.transform(X.astype(numpy.float32))
got = sess.run(None, {"X": X.astype(numpy.float32)})[0]


def diff(p1, p2):
    p1 = p1.ravel()
    p2 = p2.ravel()
    d = numpy.abs(p2 - p1)
    return d.max(), (d / numpy.abs(p1)).max()


print(diff(exp, got))

(6.04657619085458e-07, 0.0002951417065406967)

让我们检查它是否也适用于双精度浮点数。

onx = to_onnx(dec, X.astype(numpy.float64))

sess = InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"])

exp = dec.transform(X.astype(numpy.float64))
got = sess.run(None, {"X": X.astype(numpy.float64)})[0]
print(diff(exp, got))

(0.0, 0.0)

正如预期，双精度浮点数的差异更小。