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7fbed0ce
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体验新版 GitCode,发现更多精彩内容 >>
提交
7fbed0ce
编写于
7月 25, 2020
作者:
M
mindspore-ci-bot
提交者:
Gitee
7月 25, 2020
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差异文件
!3456 modify resnet50 on imagenet to improve the performance and accuracy
Merge pull request !3456 from guoqi/master
上级
669a8969
63a4b770
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
12 addition
and
48 deletion
+12
-48
model_zoo/official/cv/resnet/eval.py
model_zoo/official/cv/resnet/eval.py
+2
-2
model_zoo/official/cv/resnet/src/crossentropy.py
model_zoo/official/cv/resnet/src/crossentropy.py
+0
-39
model_zoo/official/cv/resnet/src/dataset.py
model_zoo/official/cv/resnet/src/dataset.py
+1
-1
model_zoo/official/cv/resnet/train.py
model_zoo/official/cv/resnet/train.py
+9
-6
未找到文件。
model_zoo/official/cv/resnet/eval.py
浏览文件 @
7fbed0ce
...
...
@@ -22,7 +22,6 @@ from mindspore import dataset as de
from
mindspore.nn.loss
import
SoftmaxCrossEntropyWithLogits
from
mindspore.train.model
import
Model
from
mindspore.train.serialization
import
load_checkpoint
,
load_param_into_net
from
src.crossentropy
import
CrossEntropy
parser
=
argparse
.
ArgumentParser
(
description
=
'Image classification'
)
parser
.
add_argument
(
'--net'
,
type
=
str
,
default
=
None
,
help
=
'Resnet Model, either resnet50 or resnet101'
)
...
...
@@ -78,7 +77,8 @@ if __name__ == '__main__':
if
args_opt
.
dataset
==
"imagenet2012"
:
if
not
config
.
use_label_smooth
:
config
.
label_smooth_factor
=
0.0
loss
=
CrossEntropy
(
smooth_factor
=
config
.
label_smooth_factor
,
num_classes
=
config
.
class_num
)
loss
=
SoftmaxCrossEntropyWithLogits
(
sparse
=
True
,
reduction
=
"mean"
,
smooth_factor
=
config
.
label_smooth_factor
,
num_classes
=
config
.
class_num
)
else
:
loss
=
SoftmaxCrossEntropyWithLogits
(
sparse
=
True
,
reduction
=
'mean'
)
...
...
model_zoo/official/cv/resnet/src/crossentropy.py
已删除
100755 → 0
浏览文件 @
669a8969
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""define loss function for network"""
from
mindspore.nn.loss.loss
import
_Loss
from
mindspore.ops
import
operations
as
P
from
mindspore.ops
import
functional
as
F
from
mindspore
import
Tensor
from
mindspore.common
import
dtype
as
mstype
import
mindspore.nn
as
nn
class
CrossEntropy
(
_Loss
):
"""the redefined loss function with SoftmaxCrossEntropyWithLogits"""
def
__init__
(
self
,
smooth_factor
=
0.
,
num_classes
=
1001
):
super
(
CrossEntropy
,
self
).
__init__
()
self
.
onehot
=
P
.
OneHot
()
self
.
on_value
=
Tensor
(
1.0
-
smooth_factor
,
mstype
.
float32
)
self
.
off_value
=
Tensor
(
1.0
*
smooth_factor
/
(
num_classes
-
1
),
mstype
.
float32
)
self
.
ce
=
nn
.
SoftmaxCrossEntropyWithLogits
()
self
.
mean
=
P
.
ReduceMean
(
False
)
def
construct
(
self
,
logit
,
label
):
one_hot_label
=
self
.
onehot
(
label
,
F
.
shape
(
logit
)[
1
],
self
.
on_value
,
self
.
off_value
)
loss
=
self
.
ce
(
logit
,
one_hot_label
)
loss
=
self
.
mean
(
loss
,
0
)
return
loss
model_zoo/official/cv/resnet/src/dataset.py
浏览文件 @
7fbed0ce
...
...
@@ -121,7 +121,7 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
else
:
trans
=
[
C
.
Decode
(),
C
.
Resize
(
(
256
,
256
)
),
C
.
Resize
(
256
),
C
.
CenterCrop
(
image_size
),
C
.
Normalize
(
mean
=
mean
,
std
=
std
),
C
.
HWC2CHW
()
...
...
model_zoo/official/cv/resnet/train.py
浏览文件 @
7fbed0ce
...
...
@@ -31,7 +31,6 @@ from mindspore.communication.management import init, get_rank, get_group_size
import
mindspore.nn
as
nn
import
mindspore.common.initializer
as
weight_init
from
src.lr_generator
import
get_lr
,
warmup_cosine_annealing_lr
from
src.crossentropy
import
CrossEntropy
parser
=
argparse
.
ArgumentParser
(
description
=
'Image classification'
)
parser
.
add_argument
(
'--net'
,
type
=
str
,
default
=
None
,
help
=
'Resnet Model, either resnet50 or resnet101'
)
...
...
@@ -75,7 +74,7 @@ if __name__ == '__main__':
context
.
set_auto_parallel_context
(
device_num
=
args_opt
.
device_num
,
parallel_mode
=
ParallelMode
.
DATA_PARALLEL
,
mirror_mean
=
True
)
if
args_opt
.
net
==
"resnet50"
:
auto_parallel_context
().
set_all_reduce_fusion_split_indices
([
107
,
160
])
auto_parallel_context
().
set_all_reduce_fusion_split_indices
([
85
,
160
])
else
:
auto_parallel_context
().
set_all_reduce_fusion_split_indices
([
180
,
313
])
init
()
...
...
@@ -128,15 +127,19 @@ if __name__ == '__main__':
lr
=
Tensor
(
lr
)
# define opt
opt
=
Momentum
(
filter
(
lambda
x
:
x
.
requires_grad
,
net
.
get_parameters
()),
lr
,
config
.
momentum
,
config
.
weight_decay
,
config
.
loss_scale
)
decayed_params
=
list
(
filter
(
lambda
x
:
'beta'
not
in
x
.
name
and
'gamma'
not
in
x
.
name
and
'bias'
not
in
x
.
name
,
net
.
trainalbe_params
()))
no_decayed_params
=
[
param
for
param
in
net
.
trainalbe_params
()
if
param
not
in
decayed_params
]
group_params
=
[{
'params'
:
decayed_params
,
'weight_decay'
:
config
.
weight_decay
},
{
'params'
:
no_decayed_params
},
{
'order_params'
:
net
.
trainalbe_params
()}]
opt
=
Momentum
(
group_params
,
lr
,
config
.
momentum
,
loss_scale
=
config
.
loss_scale
)
# define loss, model
if
target
==
"Ascend"
:
if
args_opt
.
dataset
==
"imagenet2012"
:
if
not
config
.
use_label_smooth
:
config
.
label_smooth_factor
=
0.0
loss
=
CrossEntropy
(
smooth_factor
=
config
.
label_smooth_factor
,
num_classes
=
config
.
class_num
)
loss
=
SoftmaxCrossEntropyWithLogits
(
sparse
=
True
,
reduction
=
"mean"
,
smooth_factor
=
config
.
label_smooth_factor
,
num_classes
=
config
.
class_num
)
else
:
loss
=
SoftmaxCrossEntropyWithLogits
(
sparse
=
True
,
reduction
=
'mean'
)
loss_scale
=
FixedLossScaleManager
(
config
.
loss_scale
,
drop_overflow_update
=
False
)
...
...
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