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体验新版 GitCode,发现更多精彩内容 >>
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5ace20fc
编写于
1月 27, 2021
作者:
A
alncat
提交者:
GitHub
1月 27, 2021
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电子邮件补丁
差异文件
modified conv+bn fuse pass to fix wrong mask in mask rcnn (#30704)
上级
824a79d3
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
22 addition
and
8 deletion
+22
-8
paddle/fluid/framework/ir/conv_bn_fuse_pass.cc
paddle/fluid/framework/ir/conv_bn_fuse_pass.cc
+12
-1
paddle/fluid/framework/ir/graph_pattern_detector.cc
paddle/fluid/framework/ir/graph_pattern_detector.cc
+10
-7
未找到文件。
paddle/fluid/framework/ir/conv_bn_fuse_pass.cc
浏览文件 @
5ace20fc
...
...
@@ -95,13 +95,24 @@ void recompute_bias_and_weights(const Scope* scope,
variance_array
+=
epsilon
;
variance_array
=
variance_array
.
sqrt
();
variance_array
=
scale_array
/
variance_array
;
for
(
int
i
=
0
;
i
<
variance_tensor
->
numel
();
i
++
)
{
PADDLE_ENFORCE_EQ
(
isfinite
(
variance_array
[
i
]),
true
,
platform
::
errors
::
InvalidArgument
(
"fuse batch norm variance should be "
"finite. Found nonfinite values!"
));
}
EigenVectorArrayMap
eltwise_y_in_array
(
eltwise_y_in_tensor
->
mutable_data
<
float
>
(
platform
::
CPUPlace
()),
eltwise_y_in_tensor
->
numel
(),
1
);
eltwise_y_in_array
=
((
eltwise_y_in_array
-
mean_array
)
*
variance_array
)
+
bn_bias_array
;
for
(
int
i
=
0
;
i
<
eltwise_y_in_tensor
->
numel
();
i
++
)
{
PADDLE_ENFORCE_EQ
(
isfinite
(
eltwise_y_in_array
[
i
]),
true
,
platform
::
errors
::
InvalidArgument
(
"fused batch norm bias should be "
"finite. Found nonfinite values!"
));
}
// Re-compute weight of conv2d from BN
auto
*
weights
=
scope
->
FindVar
(
conv_weight
->
Name
())
->
GetMutable
<
LoDTensor
>
();
...
...
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @
5ace20fc
...
...
@@ -824,22 +824,25 @@ PDNode *patterns::ConvBN::operator()(paddle::framework::ir::PDNode *conv_input,
auto
*
bn_mean_out_var
=
pattern
->
NewNode
(
bn_mean_out_repr
())
->
AsOutput
()
->
assert_is_op_output
(
"batch_norm"
,
"MeanOut"
);
->
assert_is_op_output
(
"batch_norm"
,
"MeanOut"
)
->
assert_has_n_outputs
(
0
);
auto
*
bn_variance_out_var
=
pattern
->
NewNode
(
bn_variance_out_repr
())
->
AsOutput
()
->
assert_is_op_output
(
"batch_norm"
,
"VarianceOut"
);
->
assert_is_op_output
(
"batch_norm"
,
"VarianceOut"
)
->
assert_has_n_outputs
(
0
);
auto
*
bn_saved_mean_var
=
pattern
->
NewNode
(
bn_saved_mean_repr
()
)
->
AsOutput
(
)
->
assert_is_op_output
(
"batch_norm"
,
"SavedMean"
);
auto
*
bn_saved_mean_var
=
pattern
->
NewNode
(
bn_saved_mean_repr
())
->
AsOutput
(
)
->
assert_is_op_output
(
"batch_norm"
,
"SavedMean"
)
->
assert_has_n_outputs
(
0
);
auto
*
bn_saved_variance_var
=
pattern
->
NewNode
(
bn_saved_variance_repr
())
->
AsOutput
()
->
assert_is_op_output
(
"batch_norm"
,
"SavedVariance"
);
->
assert_is_op_output
(
"batch_norm"
,
"SavedVariance"
)
->
assert_has_n_outputs
(
0
);
conv_op
->
LinksFrom
({
conv_input
,
conv_weight_var
}).
LinksTo
({
conv_out_var
});
...
...
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