import tensorflow as tf
from onnx_tf.common import exception
from onnx_tf.handlers.backend_handler import BackendHandler
from onnx_tf.handlers.handler import onnx_op
from onnx_tf.handlers.handler import partial_support
from onnx_tf.handlers.handler import ps_description
from onnx_tf.handlers.handler import tf_func
@onnx_op("CumSum")
@tf_func(tf.math.cumsum)
@partial_support(True)
@ps_description(
"CumSum inputs in uint32/uint64 " + "are not supported in Tensorflow."
)
class CumSum(BackendHandler):
@classmethod
def args_check(cls, node, **kwargs):
supported_dtype = [
tf.bfloat16, tf.half, tf.float32, tf.float64, tf.uint8, tf.uint16,
tf.int8, tf.int16, tf.int32, tf.int64, tf.complex64, tf.complex128
]
x = kwargs["tensor_dict"][node.inputs[0]]
if x.dtype not in supported_dtype:
exception.OP_UNSUPPORTED_EXCEPT(
"CumSum input in " + str(x.dtype) + " which", "Tensorflow")
@classmethod
def version_11(cls, node, **kwargs):
tensor_dict = kwargs["tensor_dict"]
x = tensor_dict[node.inputs[0]]
inputs = [x]
if len(node.inputs) > 1:
# optional 0-D tensor, range [-rank(x), rank(x)-1]
axis = tensor_dict[node.inputs[1]]
inputs.append(axis)
attrs = {
"exclusive": bool(node.attrs.get("exclusive", 0)),
"reverse": bool(node.attrs.get("reverse", 0))
}
return [cls.make_tensor_from_onnx_node(node, inputs=inputs, attrs=attrs)]
