def test_onnxt_runtime_reduce_sum_square(self):
X = numpy.array([[2, 1], [0, 1]], dtype=float)
onx = OnnxReduceSumSquare('X', output_names=['Y'], keepdims=0)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)})
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(numpy.sum(numpy.square(X)), got['Y'], decimal=6)
onx = OnnxReduceSumSquare('X', output_names=['Y'], axes=1)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)})
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(
numpy.sum(numpy.square(X), axis=1).ravel(), got['Y'].ravel())
onx = OnnxReduceSumSquare('X', output_names=['Y'], axes=1, keepdims=1)
model_def = onx.to_onnx({'X': X.astype(numpy.float32)})
oinf = OnnxInference(model_def)
got = oinf.run({'X': X})
self.assertEqual(list(sorted(got)), ['Y'])
self.assertEqualArray(
numpy.sum(numpy.square(X), axis=1, keepdims=1).ravel(),
got['Y'].ravel())
def build_ort_reducesumsquare(axes, op_version=14): # opset=13, 14, ...
node = OnnxReduceSumSquare('x',
axes=axes,
op_version=op_version,
output_names=['z'])
onx = node.to_onnx(inputs=[('x', FloatTensorType())],
target_opset=op_version)
sess = InferenceSession(onx.SerializeToString())
return lambda x, y: sess.run(None, {'x': x})
def _onnx_square_error(target_opset=None,
dtype=numpy.float32,
weight_name=None):
"""
Returns the ONNX graph for function
:math:`Y = f(X1, X2) = \\lVert X1 - X2 \\rVert ^2` or
:math:`Y = f(X1, X2) = \\lVert X1 - X2 \\rVert ^2 w` if
*weight_name* is not None
.. gdot::
:script: DOT-SECTION
from mlprodict.onnxrt import OnnxInference
from onnxcustom.utils.onnx_function import function_onnx_graph
model_onnx = function_onnx_graph('square_error')
oinf = OnnxInference(model_onnx, inplace=False)
print("DOT-SECTION", oinf.to_dot())
"""
from skl2onnx.algebra.onnx_ops import (OnnxSub, OnnxReduceSumSquare,
OnnxReshape, OnnxReduceSum, OnnxMul)
diff = OnnxSub('X1', 'X2', op_version=target_opset)
if weight_name is None:
res = OnnxReduceSumSquare(diff, op_version=target_opset)
else:
mul = OnnxMul(OnnxMul(diff, diff, op_version=target_opset),
OnnxReshape(weight_name,
numpy.array([-1, 1], dtype=numpy.int64),
op_version=target_opset),
op_version=target_opset)
res = OnnxReduceSum(mul, op_version=target_opset)
res = OnnxReshape(res,
numpy.array([-1], numpy.int64),
op_version=target_opset,
output_names=['Y'])
var_type = dtype_to_var_type(dtype)
varsx = [('X1', var_type([None, None])), ('X2', var_type([None, None]))]
if weight_name is not None:
varsx.append((weight_name, var_type([None])))
onx = res.to_onnx(varsx,
outputs=[('Y', var_type())],
target_opset=target_opset)
if weight_name is not None:
onx = add_initializer(onx, weight_name, numpy.array([1], dtype=dtype))
return onx