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10005abe
编写于
4月 29, 2020
作者:
W
WuHaobo
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polish googlenet
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1 changed file
with
22 addition
and
10 deletion
+22
-10
tools/program.py
tools/program.py
+22
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tools/program.py
浏览文件 @
10005abe
...
@@ -157,7 +157,11 @@ def create_loss(out,
...
@@ -157,7 +157,11 @@ def create_loss(out,
return
loss
(
out
,
target
)
return
loss
(
out
,
target
)
def
create_metric
(
out
,
feeds
,
topk
=
5
,
classes_num
=
1000
,
def
create_metric
(
out
,
feeds
,
architecture
,
topk
=
5
,
classes_num
=
1000
,
use_distillation
=
False
):
use_distillation
=
False
):
"""
"""
Create measures of model accuracy, such as top1 and top5
Create measures of model accuracy, such as top1 and top5
...
@@ -171,16 +175,22 @@ def create_metric(out, feeds, topk=5, classes_num=1000,
...
@@ -171,16 +175,22 @@ def create_metric(out, feeds, topk=5, classes_num=1000,
Returns:
Returns:
fetchs(dict): dict of measures
fetchs(dict): dict of measures
"""
"""
# just need student label to get metrics
if
architecture
[
"name"
]
==
"GoogLeNet"
:
if
use_distillation
:
assert
len
(
out
)
==
3
,
"GoogLeNet should have 3 outputs"
out
=
out
[
1
]
softmax_out
=
out
[
0
]
else
:
# just need student label to get metrics
if
use_distillation
:
out
=
out
[
1
]
softmax_out
=
fluid
.
layers
.
softmax
(
out
,
use_cudnn
=
False
)
fetchs
=
OrderedDict
()
fetchs
=
OrderedDict
()
label
=
feeds
[
'label'
]
# set top1 to fetchs
softmax_out
=
fluid
.
layers
.
softmax
(
out
,
use_cudnn
=
False
)
top1
=
fluid
.
layers
.
accuracy
(
softmax_out
,
label
=
feeds
[
'label'
],
k
=
1
)
top1
=
fluid
.
layers
.
accuracy
(
softmax_out
,
label
=
label
,
k
=
1
)
fetchs
[
'top1'
]
=
(
top1
,
AverageMeter
(
'top1'
,
'.4f'
,
need_avg
=
True
))
fetchs
[
'top1'
]
=
(
top1
,
AverageMeter
(
'top1'
,
'.4f'
,
need_avg
=
True
))
# set topk to fetchs
k
=
min
(
topk
,
classes_num
)
k
=
min
(
topk
,
classes_num
)
topk
=
fluid
.
layers
.
accuracy
(
softmax_out
,
label
=
label
,
k
=
k
)
topk
=
fluid
.
layers
.
accuracy
(
softmax_out
,
label
=
feeds
[
'label'
]
,
k
=
k
)
topk_name
=
'top{}'
.
format
(
k
)
topk_name
=
'top{}'
.
format
(
k
)
fetchs
[
topk_name
]
=
(
topk
,
AverageMeter
(
topk_name
,
'.4f'
,
need_avg
=
True
))
fetchs
[
topk_name
]
=
(
topk
,
AverageMeter
(
topk_name
,
'.4f'
,
need_avg
=
True
))
...
@@ -201,7 +211,8 @@ def create_fetchs(out,
...
@@ -201,7 +211,8 @@ def create_fetchs(out,
Args:
Args:
out(variable): model output variable
out(variable): model output variable
feeds(dict): dict of model input variables(included label)
feeds(dict): dict of model input variables.
If use mix_up, it will not include label.
architecture(dict): architecture information,
architecture(dict): architecture information,
name(such as ResNet50) is needed
name(such as ResNet50) is needed
topk(int): usually top5
topk(int): usually top5
...
@@ -217,7 +228,8 @@ def create_fetchs(out,
...
@@ -217,7 +228,8 @@ def create_fetchs(out,
use_distillation
)
use_distillation
)
fetchs
[
'loss'
]
=
(
loss
,
AverageMeter
(
'loss'
,
'7.4f'
,
need_avg
=
True
))
fetchs
[
'loss'
]
=
(
loss
,
AverageMeter
(
'loss'
,
'7.4f'
,
need_avg
=
True
))
if
not
use_mix
:
if
not
use_mix
:
metric
=
create_metric
(
out
,
feeds
,
topk
,
classes_num
,
use_distillation
)
metric
=
create_metric
(
out
,
feeds
,
architecture
,
topk
,
classes_num
,
use_distillation
)
fetchs
.
update
(
metric
)
fetchs
.
update
(
metric
)
return
fetchs
return
fetchs
...
...
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