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