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Support export torch.max(input, dim) and torch.min(input, dim) to ONNX #6220

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Merged
ezyang merged 2 commits into from
Apr 3, 2018
Merged

Support export torch.max(input, dim) and torch.min(input, dim) to ONNX #6220

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Support export torch.max(input, dim) and torch.min(input, dim) to ONNX
bddppq Apr 3, 2018
1efb6fe
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bddppq Apr 3, 2018
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44 changes: 38 additions & 6 deletions torch/onnx/symbolic.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
import numbers

import torch
from torch.nn.modules.utils import _single, _pair, _triple
from torch.nn.utils.rnn import PackedSequence
Expand Down Expand Up @@ -562,12 +564,42 @@ def clamp(g, self, min, max):
return g.op("Clip", self, min_f=min, max_f=max)


def max(g, self, other):
return g.op("Max", self, other)


def min(g, self, other):
return g.op("Min", self, other)
# torch.max (same for torch.min) actually has two interfaces smashed together:
# torch.max(x, dim, keepdim) and torch.max(x, y)

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def max(g, self, *args, **kwargs):
dim = kwargs.get("dim", None)
if dim is None and isinstance(args[0], numbers.Number):
dim = args[0]
if dim is not None:
keepdim = kwargs.get("keepdim", False)
# TODO: export it as ReduceMax
return g.op("ATen",
self,
operator_s="max",
dim_i=dim,
keepdim_i=keepdim,
outputs=2)
else:
(other,) = args
return g.op("Max", self, other)


def min(g, self, *args, **kwargs):
dim = kwargs.get("dim", None)
if dim is None and isinstance(args[0], numbers.Number):
dim = args[0]
if dim is not None:
keepdim = kwargs.get("keepdim", False)
# TODO: export it as ReduceMin
return g.op("ATen",
self,
operator_s="min",
dim_i=dim,
keepdim_i=keepdim,
outputs=2)
else:
(other,) = args
return g.op("Min", self, other)


def eq(g, self, other):
Expand Down