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https://github.com/mit-han-lab/tinyengine.git
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45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
# automatically generated by the FlatBuffers compiler, do not modify
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# namespace: tflite
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import flatbuffers
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from flatbuffers.compat import import_numpy
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np = import_numpy()
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class RNNOptions(object):
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__slots__ = ['_tab']
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@classmethod
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def GetRootAsRNNOptions(cls, buf, offset):
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n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
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x = RNNOptions()
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x.Init(buf, n + offset)
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return x
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@classmethod
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def RNNOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
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return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)
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# RNNOptions
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def Init(self, buf, pos):
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self._tab = flatbuffers.table.Table(buf, pos)
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# RNNOptions
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def FusedActivationFunction(self):
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o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
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if o != 0:
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return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
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return 0
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# RNNOptions
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def AsymmetricQuantizeInputs(self):
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o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
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if o != 0:
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return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
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return False
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def RNNOptionsStart(builder): builder.StartObject(2)
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def RNNOptionsAddFusedActivationFunction(builder, fusedActivationFunction): builder.PrependInt8Slot(0, fusedActivationFunction, 0)
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def RNNOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs): builder.PrependBoolSlot(1, asymmetricQuantizeInputs, 0)
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def RNNOptionsEnd(builder): return builder.EndObject()
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