71 lines
1.7 KiB
Python
71 lines
1.7 KiB
Python
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"""This file exports ONNX ops for opset 18.
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Note [ONNX Operators that are added/updated in opset 18]
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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https://github.com/onnx/onnx/blob/main/docs/Changelog.md#version-18-of-the-default-onnx-operator-set
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New operators:
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CenterCropPad
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Col2Im
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Mish
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OptionalGetElement
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OptionalHasElement
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Pad
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Resize
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ScatterElements
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ScatterND
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"""
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import functools
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from typing import Sequence
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from torch import _C
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from torch.onnx import symbolic_helper
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from torch.onnx._internal import _beartype, registration
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# EDITING THIS FILE? READ THIS FIRST!
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# see Note [Edit Symbolic Files] in symbolic_helper.py
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__all__ = ["col2im"]
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_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=18)
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@_onnx_symbolic("aten::col2im")
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@symbolic_helper.parse_args("v", "v", "v", "is", "is", "is")
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@_beartype.beartype
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def col2im(
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g,
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input: _C.Value,
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output_size: _C.Value,
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kernel_size: _C.Value,
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dilation: Sequence[int],
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padding: Sequence[int],
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stride: Sequence[int],
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):
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# convert [i0, i1, ..., in] into [i0, i0, i1, i1, ..., in, in]
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adjusted_padding = []
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for pad in padding:
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for _ in range(2):
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adjusted_padding.append(pad)
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num_dimensional_axis = symbolic_helper._get_tensor_sizes(output_size)[0]
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if not adjusted_padding:
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adjusted_padding = [0, 0] * num_dimensional_axis
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if not dilation:
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dilation = [1] * num_dimensional_axis
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if not stride:
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stride = [1] * num_dimensional_axis
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return g.op(
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"Col2Im",
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input,
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output_size,
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kernel_size,
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dilations_i=dilation,
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pads_i=adjusted_padding,
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strides_i=stride,
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)
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