forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
symbolic_opset7.py
47 lines (36 loc) · 1.77 KB
/
symbolic_opset7.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
from torch.onnx.symbolic_helper import _black_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
black_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 black_listed_op in black_listed_operators:
vars()[black_listed_op] = _black_list_in_opset(black_listed_op)