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from torch.onnx.symbolic_helper import _block_list_in_opset
import torch.onnx.symbolic_opset9 as sym_opset9
import warnings
# Note [ONNX operators that are added/updated from opset 7 to opset 8]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# New operators:
# Expand
#
# Updated operators:
# Min, Max, Sum, Mean: supports multidirectional broadcasting.
# MaxPool: added optional indices output.
# Scan
block_listed_operators = [
"scan", "expand", "expand_as", "meshgrid",
"adaptive_max_pool1d", "adaptive_max_pool2d", "adaptive_max_pool3d",
"max_pool1d_with_indices", "max_pool2d_with_indices", "max_pool3d_with_indices"
]
# NOTE: max, min, sum, mean: broadcasting is not supported in opset 7.
# torch.max (same for torch.min) actually has two interfaces smashed together:
# torch.max(x, dim, keepdim) and torch.max(x, y)
def max(g, self, dim_or_y=None, keepdim=None):
# torch.max(input, other)
if keepdim is None and dim_or_y is not None:
warnings.warn("Multidirectional broadcasting is not supported in opset 7. "
"This might cause the onnx model to be incorrect, if inputs to max operators "
"have different shapes")
return sym_opset9.max(g, self, dim_or_y, keepdim)
def min(g, self, dim_or_y=None, keepdim=None):
# torch.min(input, other)
if keepdim is None and dim_or_y is not None:
warnings.warn("Multidirectional broadcasting is not supported in opset 7. "
"This might cause the onnx model to be incorrect, if inputs to min operators "
"have different shapes")
return sym_opset9.min(g, self, dim_or_y, keepdim)
for block_listed_op in block_listed_operators:
vars()[block_listed_op] = _block_list_in_opset(block_listed_op)
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