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