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 tf_func
from onnx_tf.handlers.handler import partial_support
from onnx_tf.handlers.handler import ps_description
from .control_flow_mixin import ComparisonMixin
@onnx_op("Equal")
@tf_func(tf.equal)
@partial_support(True)
@ps_description(
"Equal inputs in uint16/uint32/uint64 " + "are not supported in Tensorflow."
)
class Equal(ComparisonMixin, BackendHandler):
@classmethod
def args_check(cls, node, **kwargs):
supported_dtype = [
tf.bfloat16, tf.half, tf.float32, tf.float64, tf.uint8, tf.int8,
tf.int16, tf.int32, tf.int64, tf.complex64, tf.quint8, tf.qint8,
tf.qint32, tf.string, tf.bool, tf.complex128
]
x = kwargs["tensor_dict"][node.inputs[0]]
if x.dtype not in supported_dtype:
exception.OP_UNSUPPORTED_EXCEPT(
"Equal inputs in " + str(x.dtype) + " which", "Tensorflow")
@classmethod
def version_1(cls, node, **kwargs):
return cls.limited_broadcast(node, **kwargs)
@classmethod
def version_7(cls, node, **kwargs):
return [cls.make_tensor_from_onnx_node(node, **kwargs)]
@classmethod
def version_11(cls, node, **kwargs):
return [cls.make_tensor_from_onnx_node(node, **kwargs)]
