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- """
- 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
- """
- import warnings
- from torch.onnx import symbolic_helper
- from torch.onnx import symbolic_opset9 as opset9
- 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 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 opset9.min(g, self, dim_or_y, keepdim)
- for block_listed_op in block_listed_operators:
- vars()[block_listed_op] = symbolic_helper._block_list_in_opset(block_listed_op)
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