def build_ort_reducemax(axes, op_version=14): # opset=13, 14, ...
node = OnnxReduceMax('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 validator_classifier_converter(scope, operator, container):
input = operator.inputs[0] # input in ONNX graph
outputs = operator.outputs # outputs in ONNX graph
op = operator.raw_operator # scikit-learn model (mmust be fitted)
opv = container.target_opset
# We reuse existing converter and declare it as local
# operator.
model = op.estimator_
onnx_op = OnnxSubEstimator(model, input, op_version=opv)
rmax = OnnxReduceMax(onnx_op[1], axes=[1], keepdims=0, op_version=opv)
great = OnnxGreater(rmax,
np.array([op.threshold], dtype=np.float32),
op_version=opv)
valid = OnnxCast(great, to=onnx_proto.TensorProto.INT64, op_version=opv)
r1 = OnnxIdentity(onnx_op[0],
output_names=[outputs[0].full_name],
op_version=opv)
r2 = OnnxIdentity(onnx_op[1],
output_names=[outputs[1].full_name],
op_version=opv)
r3 = OnnxIdentity(valid,
output_names=[outputs[2].full_name],
op_version=opv)
r1.add_to(scope, container)
r2.add_to(scope, container)
r3.add_to(scope, container)
def validator_classifier_converter(scope, operator, container):
input0 = operator.inputs[0] # first input in ONNX graph
outputs = operator.outputs # outputs in ONNX graph
op = operator.raw_operator # scikit-learn model (mmust be fitted)
opv = container.target_opset
# The model calls another one. The class `OnnxSubEstimator`
# calls the converter for this operator.
model = op.estimator_
onnx_op = OnnxSubEstimator(model,
input0,
op_version=opv,
options={'zipmap': False})
rmax = OnnxReduceMax(onnx_op[1], axes=[1], keepdims=0, op_version=opv)
great = OnnxGreater(rmax,
np.array([op.threshold], dtype=np.float32),
op_version=opv)
valid = OnnxCast(great, to=onnx_proto.TensorProto.INT64, op_version=opv)
r1 = OnnxIdentity(onnx_op[0],
output_names=[outputs[0].full_name],
op_version=opv)
r2 = OnnxIdentity(onnx_op[1],
output_names=[outputs[1].full_name],
op_version=opv)
r3 = OnnxIdentity(valid,
output_names=[outputs[2].full_name],
op_version=opv)
r1.add_to(scope, container)
r2.add_to(scope, container)
r3.add_to(scope, container)