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dccd7ed9
编写于
3月 02, 2021
作者:
W
Wei Shengyu
提交者:
GitHub
3月 02, 2021
浏览文件
操作
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差异文件
Merge pull request #619 from huangxu96/cp_fp16_training
[Cherry-pick]new usage of amp training. (#564)
上级
e02a35ac
1df66418
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
90 addition
and
254 deletion
+90
-254
configs/ResNet/ResNet50_fp16.yaml
configs/ResNet/ResNet50_fp16.yaml
+14
-9
ppcls/data/imaug/operators.py
ppcls/data/imaug/operators.py
+16
-3
ppcls/modeling/architectures/resnet.py
ppcls/modeling/architectures/resnet.py
+12
-8
ppcls/modeling/loss.py
ppcls/modeling/loss.py
+8
-14
ppcls/optimizer/optimizer.py
ppcls/optimizer/optimizer.py
+4
-1
tools/static/dali.py
tools/static/dali.py
+5
-1
tools/static/optimizer.py
tools/static/optimizer.py
+0
-171
tools/static/program.py
tools/static/program.py
+23
-38
tools/static/train.py
tools/static/train.py
+8
-9
未找到文件。
configs/ResNet/ResNet50_fp16.yml
→
configs/ResNet/ResNet50_fp16.y
a
ml
浏览文件 @
dccd7ed9
...
...
@@ -11,21 +11,23 @@ validate: True
valid_interval
:
1
epochs
:
120
topk
:
5
image_shape
:
[
3
,
224
,
224
]
is_distributed
:
True
# mixed precision training
use_amp
:
True
use_pure_fp16
:
False
multi_precision
:
False
scale_loss
:
128.0
use_dynamic_loss_scaling
:
True
data_format
:
"
NCHW"
image_shape
:
[
3
,
224
,
224
]
use_dali
:
True
use_gpu
:
True
data_format
:
"
NHWC"
image_channel
:
&image_channel
4
image_shape
:
[
*image_channel
,
224
,
224
]
use_mix
:
False
ls_epsilon
:
-1
# mixed precision training
AMP
:
scale_loss
:
128.0
use_dynamic_loss_scaling
:
True
use_pure_fp16
:
&use_pure_fp16
True
LEARNING_RATE
:
function
:
'
Piecewise'
params
:
...
...
@@ -37,6 +39,7 @@ OPTIMIZER:
function
:
'
Momentum'
params
:
momentum
:
0.9
multi_precision
:
*use_pure_fp16
regularizer
:
function
:
'
L2'
factor
:
0.000100
...
...
@@ -61,6 +64,8 @@ TRAIN:
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
order
:
'
'
output_fp16
:
*use_pure_fp16
channel_num
:
*image_channel
-
ToCHWImage
:
VALID
:
...
...
ppcls/data/imaug/operators.py
浏览文件 @
dccd7ed9
...
...
@@ -195,14 +195,18 @@ class NormalizeImage(object):
""" normalize image such as substract mean, divide std
"""
def
__init__
(
self
,
scale
=
None
,
mean
=
None
,
std
=
None
,
order
=
'chw'
):
def
__init__
(
self
,
scale
=
None
,
mean
=
None
,
std
=
None
,
order
=
'chw'
,
output_fp16
=
False
,
channel_num
=
3
):
if
isinstance
(
scale
,
str
):
scale
=
eval
(
scale
)
assert
channel_num
in
[
3
,
4
],
"channel number of input image should be set to 3 or 4."
self
.
channel_num
=
channel_num
self
.
output_dtype
=
'float16'
if
output_fp16
else
'float32'
self
.
scale
=
np
.
float32
(
scale
if
scale
is
not
None
else
1.0
/
255.0
)
self
.
order
=
order
mean
=
mean
if
mean
is
not
None
else
[
0.485
,
0.456
,
0.406
]
std
=
std
if
std
is
not
None
else
[
0.229
,
0.224
,
0.225
]
shape
=
(
3
,
1
,
1
)
if
order
==
'chw'
else
(
1
,
1
,
3
)
shape
=
(
3
,
1
,
1
)
if
self
.
order
==
'chw'
else
(
1
,
1
,
3
)
self
.
mean
=
np
.
array
(
mean
).
reshape
(
shape
).
astype
(
'float32'
)
self
.
std
=
np
.
array
(
std
).
reshape
(
shape
).
astype
(
'float32'
)
...
...
@@ -213,7 +217,16 @@ class NormalizeImage(object):
assert
isinstance
(
img
,
np
.
ndarray
),
"invalid input 'img' in NormalizeImage"
return
(
img
.
astype
(
'float32'
)
*
self
.
scale
-
self
.
mean
)
/
self
.
std
img
=
(
img
.
astype
(
'float32'
)
*
self
.
scale
-
self
.
mean
)
/
self
.
std
if
self
.
channel_num
==
4
:
img_h
=
img
.
shape
[
1
]
if
self
.
order
==
'chw'
else
img
.
shape
[
0
]
img_w
=
img
.
shape
[
2
]
if
self
.
order
==
'chw'
else
img
.
shape
[
1
]
pad_zeros
=
np
.
zeros
((
1
,
img_h
,
img_w
))
if
self
.
order
==
'chw'
else
np
.
zeros
((
img_h
,
img_w
,
1
))
img
=
(
np
.
concatenate
((
img
,
pad_zeros
),
axis
=
0
)
if
self
.
order
==
'chw'
else
np
.
concatenate
((
img
,
pad_zeros
),
axis
=
2
))
return
img
.
astype
(
self
.
output_dtype
)
class
ToCHWImage
(
object
):
...
...
ppcls/modeling/architectures/resnet.py
浏览文件 @
dccd7ed9
...
...
@@ -277,14 +277,18 @@ class ResNet(nn.Layer):
bias_attr
=
ParamAttr
(
name
=
"fc_0.b_0"
))
def
forward
(
self
,
inputs
):
y
=
self
.
conv
(
inputs
)
y
=
self
.
pool2d_max
(
y
)
for
block
in
self
.
block_list
:
y
=
block
(
y
)
y
=
self
.
pool2d_avg
(
y
)
y
=
paddle
.
reshape
(
y
,
shape
=
[
-
1
,
self
.
pool2d_avg_channels
])
y
=
self
.
out
(
y
)
return
y
with
paddle
.
static
.
amp
.
fp16_guard
():
if
self
.
data_format
==
"NHWC"
:
inputs
=
paddle
.
tensor
.
transpose
(
inputs
,
[
0
,
2
,
3
,
1
])
inputs
.
stop_gradient
=
True
y
=
self
.
conv
(
inputs
)
y
=
self
.
pool2d_max
(
y
)
for
block
in
self
.
block_list
:
y
=
block
(
y
)
y
=
self
.
pool2d_avg
(
y
)
y
=
paddle
.
reshape
(
y
,
shape
=
[
-
1
,
self
.
pool2d_avg_channels
])
y
=
self
.
out
(
y
)
return
y
def
ResNet18
(
**
args
):
...
...
ppcls/modeling/loss.py
浏览文件 @
dccd7ed9
...
...
@@ -42,17 +42,14 @@ class Loss(object):
soft_target
=
paddle
.
reshape
(
soft_target
,
shape
=
[
-
1
,
self
.
_class_dim
])
return
soft_target
def
_crossentropy
(
self
,
input
,
target
,
use_pure_fp16
=
False
):
def
_crossentropy
(
self
,
input
,
target
):
if
self
.
_label_smoothing
:
target
=
self
.
_labelsmoothing
(
target
)
input
=
-
F
.
log_softmax
(
input
,
axis
=-
1
)
cost
=
paddle
.
sum
(
target
*
input
,
axis
=-
1
)
else
:
cost
=
F
.
cross_entropy
(
input
=
input
,
label
=
target
)
if
use_pure_fp16
:
avg_cost
=
paddle
.
sum
(
cost
)
else
:
avg_cost
=
paddle
.
mean
(
cost
)
avg_cost
=
paddle
.
mean
(
cost
)
return
avg_cost
def
_kldiv
(
self
,
input
,
target
,
name
=
None
):
...
...
@@ -81,8 +78,8 @@ class CELoss(Loss):
def
__init__
(
self
,
class_dim
=
1000
,
epsilon
=
None
):
super
(
CELoss
,
self
).
__init__
(
class_dim
,
epsilon
)
def
__call__
(
self
,
input
,
target
,
use_pure_fp16
=
False
):
cost
=
self
.
_crossentropy
(
input
,
target
,
use_pure_fp16
)
def
__call__
(
self
,
input
,
target
):
cost
=
self
.
_crossentropy
(
input
,
target
)
return
cost
...
...
@@ -94,14 +91,11 @@ class MixCELoss(Loss):
def
__init__
(
self
,
class_dim
=
1000
,
epsilon
=
None
):
super
(
MixCELoss
,
self
).
__init__
(
class_dim
,
epsilon
)
def
__call__
(
self
,
input
,
target0
,
target1
,
lam
,
use_pure_fp16
=
False
):
cost0
=
self
.
_crossentropy
(
input
,
target0
,
use_pure_fp16
)
cost1
=
self
.
_crossentropy
(
input
,
target1
,
use_pure_fp16
)
def
__call__
(
self
,
input
,
target0
,
target1
,
lam
):
cost0
=
self
.
_crossentropy
(
input
,
target0
)
cost1
=
self
.
_crossentropy
(
input
,
target1
)
cost
=
lam
*
cost0
+
(
1.0
-
lam
)
*
cost1
if
use_pure_fp16
:
avg_cost
=
paddle
.
sum
(
cost
)
else
:
avg_cost
=
paddle
.
mean
(
cost
)
avg_cost
=
paddle
.
mean
(
cost
)
return
avg_cost
...
...
ppcls/optimizer/optimizer.py
浏览文件 @
dccd7ed9
...
...
@@ -74,19 +74,22 @@ class Momentum(object):
momentum
,
parameter_list
=
None
,
regularization
=
None
,
multi_precision
=
False
,
**
args
):
super
(
Momentum
,
self
).
__init__
()
self
.
learning_rate
=
learning_rate
self
.
momentum
=
momentum
self
.
parameter_list
=
parameter_list
self
.
regularization
=
regularization
self
.
multi_precision
=
multi_precision
def
__call__
(
self
):
opt
=
paddle
.
optimizer
.
Momentum
(
learning_rate
=
self
.
learning_rate
,
momentum
=
self
.
momentum
,
parameters
=
self
.
parameter_list
,
weight_decay
=
self
.
regularization
)
weight_decay
=
self
.
regularization
,
multi_precision
=
self
.
multi_precision
)
return
opt
...
...
tools/static/dali.py
浏览文件 @
dccd7ed9
...
...
@@ -176,7 +176,11 @@ def build(config, mode='train'):
2
:
types
.
INTERP_CUBIC
,
# cv2.INTER_CUBIC
4
:
types
.
INTERP_LANCZOS3
,
# XXX use LANCZOS3 for cv2.INTER_LANCZOS4
}
output_dtype
=
types
.
FLOAT16
if
config
.
get
(
"use_pure_fp16"
,
False
)
else
types
.
FLOAT
output_dtype
=
(
types
.
FLOAT16
if
'AMP'
in
config
and
config
.
AMP
.
get
(
"use_pure_fp16"
,
False
)
else
types
.
FLOAT
)
assert
interp
in
interp_map
,
"interpolation method not supported by DALI"
interp
=
interp_map
[
interp
]
pad_output
=
False
...
...
tools/static/optimizer.py
已删除
100644 → 0
浏览文件 @
e02a35ac
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from
__future__
import
absolute_import
from
__future__
import
division
from
__future__
import
print_function
import
sys
import
paddle
import
paddle.fluid
as
fluid
import
paddle.regularizer
as
regularizer
__all__
=
[
'OptimizerBuilder'
]
class
L1Decay
(
object
):
"""
L1 Weight Decay Regularization, which encourages the weights to be sparse.
Args:
factor(float): regularization coeff. Default:0.0.
"""
def
__init__
(
self
,
factor
=
0.0
):
super
(
L1Decay
,
self
).
__init__
()
self
.
factor
=
factor
def
__call__
(
self
):
reg
=
regularizer
.
L1Decay
(
self
.
factor
)
return
reg
class
L2Decay
(
object
):
"""
L2 Weight Decay Regularization, which encourages the weights to be sparse.
Args:
factor(float): regularization coeff. Default:0.0.
"""
def
__init__
(
self
,
factor
=
0.0
):
super
(
L2Decay
,
self
).
__init__
()
self
.
factor
=
factor
def
__call__
(
self
):
reg
=
regularizer
.
L2Decay
(
self
.
factor
)
return
reg
class
Momentum
(
object
):
"""
Simple Momentum optimizer with velocity state.
Args:
learning_rate (float|Variable) - The learning rate used to update parameters.
Can be a float value or a Variable with one float value as data element.
momentum (float) - Momentum factor.
regularization (WeightDecayRegularizer, optional) - The strategy of regularization.
"""
def
__init__
(
self
,
learning_rate
,
momentum
,
parameter_list
=
None
,
regularization
=
None
,
config
=
None
,
**
args
):
super
(
Momentum
,
self
).
__init__
()
self
.
learning_rate
=
learning_rate
self
.
momentum
=
momentum
self
.
parameter_list
=
parameter_list
self
.
regularization
=
regularization
self
.
multi_precision
=
config
.
get
(
'multi_precision'
,
False
)
self
.
rescale_grad
=
(
1.0
/
(
config
[
'TRAIN'
][
'batch_size'
]
/
len
(
fluid
.
cuda_places
()))
if
config
.
get
(
'use_pure_fp16'
,
False
)
else
1.0
)
def
__call__
(
self
):
opt
=
fluid
.
contrib
.
optimizer
.
Momentum
(
learning_rate
=
self
.
learning_rate
,
momentum
=
self
.
momentum
,
regularization
=
self
.
regularization
,
multi_precision
=
self
.
multi_precision
,
rescale_grad
=
self
.
rescale_grad
)
return
opt
class
RMSProp
(
object
):
"""
Root Mean Squared Propagation (RMSProp) is an unpublished, adaptive learning rate method.
Args:
learning_rate (float|Variable) - The learning rate used to update parameters.
Can be a float value or a Variable with one float value as data element.
momentum (float) - Momentum factor.
rho (float) - rho value in equation.
epsilon (float) - avoid division by zero, default is 1e-6.
regularization (WeightDecayRegularizer, optional) - The strategy of regularization.
"""
def
__init__
(
self
,
learning_rate
,
momentum
,
rho
=
0.95
,
epsilon
=
1e-6
,
parameter_list
=
None
,
regularization
=
None
,
**
args
):
super
(
RMSProp
,
self
).
__init__
()
self
.
learning_rate
=
learning_rate
self
.
momentum
=
momentum
self
.
rho
=
rho
self
.
epsilon
=
epsilon
self
.
parameter_list
=
parameter_list
self
.
regularization
=
regularization
def
__call__
(
self
):
opt
=
paddle
.
optimizer
.
RMSProp
(
learning_rate
=
self
.
learning_rate
,
momentum
=
self
.
momentum
,
rho
=
self
.
rho
,
epsilon
=
self
.
epsilon
,
parameters
=
self
.
parameter_list
,
weight_decay
=
self
.
regularization
)
return
opt
class
OptimizerBuilder
(
object
):
"""
Build optimizer
Args:
function(str): optimizer name of learning rate
params(dict): parameters used for init the class
regularizer (dict): parameters used for create regularization
"""
def
__init__
(
self
,
config
=
None
,
function
=
'Momentum'
,
params
=
{
'momentum'
:
0.9
},
regularizer
=
None
):
self
.
function
=
function
self
.
params
=
params
self
.
config
=
config
# create regularizer
if
regularizer
is
not
None
:
mod
=
sys
.
modules
[
__name__
]
reg_func
=
regularizer
[
'function'
]
+
'Decay'
del
regularizer
[
'function'
]
reg
=
getattr
(
mod
,
reg_func
)(
**
regularizer
)()
self
.
params
[
'regularization'
]
=
reg
def
__call__
(
self
,
learning_rate
,
parameter_list
=
None
):
mod
=
sys
.
modules
[
__name__
]
opt
=
getattr
(
mod
,
self
.
function
)
return
opt
(
learning_rate
=
learning_rate
,
parameter_list
=
parameter_list
,
config
=
self
.
config
,
**
self
.
params
)()
tools/static/program.py
浏览文件 @
dccd7ed9
...
...
@@ -21,12 +21,10 @@ import time
import
numpy
as
np
from
collections
import
OrderedDict
from
optimizer
import
OptimizerBuilder
from
ppcls.
optimizer
import
OptimizerBuilder
import
paddle
import
paddle.nn.functional
as
F
from
paddle
import
fluid
from
paddle.fluid.contrib.mixed_precision.fp16_utils
import
cast_model_to_fp16
from
ppcls.optimizer.learning_rate
import
LearningRateBuilder
from
ppcls.modeling
import
architectures
...
...
@@ -83,11 +81,9 @@ def create_model(architecture, image, classes_num, config, is_train):
Returns:
out(variable): model output variable
"""
use_pure_fp16
=
config
.
get
(
"use_pure_fp16"
,
False
)
name
=
architecture
[
"name"
]
params
=
architecture
.
get
(
"params"
,
{})
data_format
=
"NCHW"
if
"data_format"
in
config
:
params
[
"data_format"
]
=
config
[
"data_format"
]
data_format
=
config
[
"data_format"
]
...
...
@@ -100,16 +96,8 @@ def create_model(architecture, image, classes_num, config, is_train):
if
"is_test"
in
params
:
params
[
'is_test'
]
=
not
is_train
model
=
architectures
.
__dict__
[
name
](
class_dim
=
classes_num
,
**
params
)
if
use_pure_fp16
and
not
config
.
get
(
"use_dali"
,
False
):
image
=
image
.
astype
(
'float16'
)
if
data_format
==
"NHWC"
:
image
=
paddle
.
tensor
.
transpose
(
image
,
[
0
,
2
,
3
,
1
])
image
.
stop_gradient
=
True
out
=
model
(
image
)
if
config
.
get
(
"use_pure_fp16"
,
False
):
cast_model_to_fp16
(
paddle
.
static
.
default_main_program
())
out
=
out
.
astype
(
'float32'
)
return
out
...
...
@@ -119,8 +107,7 @@ def create_loss(out,
classes_num
=
1000
,
epsilon
=
None
,
use_mix
=
False
,
use_distillation
=
False
,
use_pure_fp16
=
False
):
use_distillation
=
False
):
"""
Create a loss for optimization, such as:
1. CrossEnotry loss
...
...
@@ -137,7 +124,6 @@ def create_loss(out,
classes_num(int): num of classes
epsilon(float): parameter for label smoothing, 0.0 <= epsilon <= 1.0
use_mix(bool): whether to use mix(include mixup, cutmix, fmix)
use_pure_fp16(bool): whether to use pure fp16 data as training parameter
Returns:
loss(variable): loss variable
...
...
@@ -162,10 +148,10 @@ def create_loss(out,
if
use_mix
:
loss
=
MixCELoss
(
class_dim
=
classes_num
,
epsilon
=
epsilon
)
return
loss
(
out
,
feed_y_a
,
feed_y_b
,
feed_lam
,
use_pure_fp16
)
return
loss
(
out
,
feed_y_a
,
feed_y_b
,
feed_lam
)
else
:
loss
=
CELoss
(
class_dim
=
classes_num
,
epsilon
=
epsilon
)
return
loss
(
out
,
target
,
use_pure_fp16
)
return
loss
(
out
,
target
)
def
create_metric
(
out
,
...
...
@@ -239,9 +225,8 @@ def create_fetchs(out,
fetchs(dict): dict of model outputs(included loss and measures)
"""
fetchs
=
OrderedDict
()
use_pure_fp16
=
config
.
get
(
"use_pure_fp16"
,
False
)
loss
=
create_loss
(
out
,
feeds
,
architecture
,
classes_num
,
epsilon
,
use_mix
,
use_distillation
,
use_pure_fp16
)
use_distillation
)
fetchs
[
'loss'
]
=
(
loss
,
AverageMeter
(
'loss'
,
'7.4f'
,
need_avg
=
True
))
if
not
use_mix
:
metric
=
create_metric
(
out
,
feeds
,
architecture
,
topk
,
classes_num
,
...
...
@@ -285,7 +270,7 @@ def create_optimizer(config):
# create optimizer instance
opt_config
=
config
[
'OPTIMIZER'
]
opt
=
OptimizerBuilder
(
config
,
**
opt_config
)
opt
=
OptimizerBuilder
(
**
opt_config
)
return
opt
(
lr
),
lr
...
...
@@ -304,11 +289,11 @@ def create_strategy(config):
exec_strategy
=
paddle
.
static
.
ExecutionStrategy
()
exec_strategy
.
num_threads
=
1
exec_strategy
.
num_iteration_per_drop_scope
=
10000
if
config
.
get
(
'use_pure_fp16'
,
False
)
else
10
exec_strategy
.
num_iteration_per_drop_scope
=
(
10000
if
'AMP'
in
config
and
config
.
AMP
.
get
(
"use_pure_fp16"
,
False
)
else
10
)
fuse_op
=
True
if
'AMP'
in
config
else
False
fuse_op
=
config
.
get
(
'use_amp'
,
False
)
or
config
.
get
(
'use_pure_fp16'
,
False
)
fuse_bn_act_ops
=
config
.
get
(
'fuse_bn_act_ops'
,
fuse_op
)
fuse_elewise_add_act_ops
=
config
.
get
(
'fuse_elewise_add_act_ops'
,
fuse_op
)
fuse_bn_add_act_ops
=
config
.
get
(
'fuse_bn_add_act_ops'
,
fuse_op
)
...
...
@@ -369,14 +354,17 @@ def dist_optimizer(config, optimizer):
def
mixed_precision_optimizer
(
config
,
optimizer
):
use_amp
=
config
.
get
(
'use_amp'
,
False
)
scale_loss
=
config
.
get
(
'scale_loss'
,
1.0
)
use_dynamic_loss_scaling
=
config
.
get
(
'use_dynamic_loss_scaling'
,
False
)
if
use_amp
:
optimizer
=
fluid
.
contrib
.
mixed_precision
.
decorate
(
if
'AMP'
in
config
:
amp_cfg
=
config
.
AMP
if
config
.
AMP
else
dict
()
scale_loss
=
amp_cfg
.
get
(
'scale_loss'
,
1.0
)
use_dynamic_loss_scaling
=
amp_cfg
.
get
(
'use_dynamic_loss_scaling'
,
False
)
use_pure_fp16
=
amp_cfg
.
get
(
'use_pure_fp16'
,
False
)
optimizer
=
paddle
.
static
.
amp
.
decorate
(
optimizer
,
init_loss_scaling
=
scale_loss
,
use_dynamic_loss_scaling
=
use_dynamic_loss_scaling
)
use_dynamic_loss_scaling
=
use_dynamic_loss_scaling
,
use_pure_fp16
=
use_pure_fp16
,
use_fp16_guard
=
True
)
return
optimizer
...
...
@@ -407,15 +395,11 @@ def build(config, main_prog, startup_prog, is_train=True, is_distributed=True):
use_dali
=
config
.
get
(
'use_dali'
,
False
)
use_distillation
=
config
.
get
(
'use_distillation'
)
image_dtype
=
"float32"
if
config
[
"ARCHITECTURE"
][
"name"
]
==
"ResNet50"
and
config
.
get
(
"use_pure_fp16"
,
False
)
\
and
config
.
get
(
"use_dali"
,
False
):
image_dtype
=
"float16"
feeds
=
create_feeds
(
config
.
image_shape
,
use_mix
=
use_mix
,
use_dali
=
use_dali
,
dtype
=
image_dtype
)
dtype
=
"float32"
)
if
use_dali
and
use_mix
:
import
dali
feeds
=
dali
.
mix
(
feeds
,
config
,
is_train
)
...
...
@@ -432,13 +416,14 @@ def build(config, main_prog, startup_prog, is_train=True, is_distributed=True):
config
=
config
,
use_distillation
=
use_distillation
)
lr_scheduler
=
None
optimizer
=
None
if
is_train
:
optimizer
,
lr_scheduler
=
create_optimizer
(
config
)
optimizer
=
mixed_precision_optimizer
(
config
,
optimizer
)
if
is_distributed
:
optimizer
=
dist_optimizer
(
config
,
optimizer
)
optimizer
.
minimize
(
fetchs
[
'loss'
][
0
])
return
fetchs
,
lr_scheduler
,
feeds
return
fetchs
,
lr_scheduler
,
feeds
,
optimizer
def
compile
(
config
,
program
,
loss_name
=
None
,
share_prog
=
None
):
...
...
tools/static/train.py
浏览文件 @
dccd7ed9
...
...
@@ -26,8 +26,6 @@ sys.path.append(os.path.abspath(os.path.join(__dir__, '../../')))
from
sys
import
version_info
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid.contrib.mixed_precision.fp16_utils
import
cast_parameters_to_fp16
from
paddle.distributed
import
fleet
from
ppcls.data
import
Reader
...
...
@@ -67,9 +65,7 @@ def main(args):
# assign the place
use_gpu
=
config
.
get
(
"use_gpu"
,
True
)
# amp related config
use_amp
=
config
.
get
(
'use_amp'
,
False
)
use_pure_fp16
=
config
.
get
(
'use_pure_fp16'
,
False
)
if
use_amp
or
use_pure_fp16
:
if
'AMP'
in
config
:
AMP_RELATED_FLAGS_SETTING
=
{
'FLAGS_cudnn_exhaustive_search'
:
1
,
'FLAGS_conv_workspace_size_limit'
:
1500
,
...
...
@@ -97,7 +93,7 @@ def main(args):
best_top1_acc
=
0.0
# best top1 acc record
train_fetchs
,
lr_scheduler
,
train_feeds
=
program
.
build
(
train_fetchs
,
lr_scheduler
,
train_feeds
,
optimizer
=
program
.
build
(
config
,
train_prog
,
startup_prog
,
...
...
@@ -106,7 +102,7 @@ def main(args):
if
config
.
validate
:
valid_prog
=
paddle
.
static
.
Program
()
valid_fetchs
,
_
,
valid_feeds
=
program
.
build
(
valid_fetchs
,
_
,
valid_feeds
,
_
=
program
.
build
(
config
,
valid_prog
,
startup_prog
,
...
...
@@ -119,11 +115,14 @@ def main(args):
exe
=
paddle
.
static
.
Executor
(
place
)
# Parameter initialization
exe
.
run
(
startup_prog
)
if
config
.
get
(
"use_pure_fp16"
,
False
):
cast_parameters_to_fp16
(
place
,
train_prog
,
fluid
.
global_scope
())
# load pretrained models or checkpoints
init_model
(
config
,
train_prog
,
exe
)
if
'AMP'
in
config
and
config
.
AMP
.
get
(
"use_pure_fp16"
,
False
):
optimizer
.
amp_init
(
place
,
scope
=
paddle
.
static
.
global_scope
(),
test_program
=
valid_prog
if
config
.
validate
else
None
)
if
not
config
.
get
(
"is_distributed"
,
True
)
and
not
use_xpu
:
compiled_train_prog
=
program
.
compile
(
config
,
train_prog
,
loss_name
=
train_fetchs
[
"loss"
][
0
].
name
)
...
...
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