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体验新版 GitCode,发现更多精彩内容 >>
提交
bc4af386
编写于
10月 15, 2019
作者:
L
Liufang Sang
提交者:
whs
10月 15, 2019
浏览文件
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电子邮件补丁
差异文件
[PaddleSlim] refine slim reader to support dataloader (#20604)
上级
a4753f3a
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
135 addition
and
6 deletion
+135
-6
python/paddle/fluid/contrib/slim/core/compressor.py
python/paddle/fluid/contrib/slim/core/compressor.py
+5
-5
python/paddle/fluid/contrib/slim/graph/executor.py
python/paddle/fluid/contrib/slim/graph/executor.py
+4
-1
python/paddle/fluid/contrib/slim/tests/test_reader.py
python/paddle/fluid/contrib/slim/tests/test_reader.py
+126
-0
未找到文件。
python/paddle/fluid/contrib/slim/core/compressor.py
浏览文件 @
bc4af386
...
...
@@ -20,7 +20,7 @@ from .... import profiler
from
....
import
scope_guard
from
....data_feeder
import
DataFeeder
from
....log_helper
import
get_logger
from
....reader
import
PyReader
from
....reader
import
DataLoaderBase
from
..graph
import
*
from
.config
import
ConfigFactory
import
numpy
as
np
...
...
@@ -194,8 +194,8 @@ class Context(object):
reader
=
cached_reader
(
reader
,
sampled_rate
,
self
.
cache_path
,
cached_id
)
if
isinstance
(
reader
,
Variable
)
or
(
isinstance
(
reader
,
PyReader
)
and
(
not
reader
.
iterable
)):
if
isinstance
(
reader
,
Variable
)
or
(
isinstance
(
reader
,
DataLoaderBase
)
and
(
not
reader
.
iterable
)):
reader
.
start
()
try
:
while
True
:
...
...
@@ -488,8 +488,8 @@ class Compressor(object):
build_strategy
=
build_strategy
)
if
isinstance
(
context
.
train_reader
,
Variable
)
or
(
isinstance
(
context
.
train_reader
,
PyReader
)
and
(
not
context
.
train_reader
.
iterable
)):
isinstance
(
context
.
train_reader
,
DataLoaderBase
)
and
(
not
context
.
train_reader
.
iterable
)):
context
.
train_reader
.
start
()
try
:
while
True
:
...
...
python/paddle/fluid/contrib/slim/graph/executor.py
浏览文件 @
bc4af386
...
...
@@ -42,7 +42,10 @@ class SlimGraphExecutor(object):
"""
assert
isinstance
(
graph
,
GraphWrapper
)
feed
=
None
if
data
is
not
None
:
if
data
is
not
None
and
isinstance
(
data
[
0
],
dict
):
# return list = False
feed
=
data
elif
data
is
not
None
:
feeder
=
DataFeeder
(
feed_list
=
list
(
graph
.
in_nodes
.
values
()),
place
=
self
.
place
,
...
...
python/paddle/fluid/contrib/slim/tests/test_reader.py
0 → 100644
浏览文件 @
bc4af386
# copyright (c) 2019 paddlepaddle authors. all rights reserved.
#
# 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.
import
os
import
shutil
import
paddle
import
unittest
import
paddle.fluid
as
fluid
from
mobilenet
import
MobileNet
from
paddle.fluid.contrib.slim.core
import
Compressor
from
paddle.fluid.contrib.slim.graph
import
GraphWrapper
class
TestReader
(
unittest
.
TestCase
):
"""
Test API of quantization strategy.
"""
def
set_train_reader
(
self
,
image
,
label
,
place
):
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
128
)
return
train_reader
def
set_val_reader
(
self
,
image
,
label
,
place
):
val_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
test
(),
batch_size
=
128
)
return
val_reader
def
set_feed_list
(
self
,
image
,
label
):
return
[(
'img'
,
image
.
name
),
(
'label'
,
label
.
name
)]
def
quan
(
self
,
config_file
):
if
os
.
path
.
exists
(
'./checkpoints_quan'
):
shutil
.
rmtree
(
'./checkpoints_quan'
)
if
not
fluid
.
core
.
is_compiled_with_cuda
():
return
class_dim
=
10
image_shape
=
[
1
,
28
,
28
]
train_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
val_program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
train_program
,
startup_program
):
with
fluid
.
unique_name
.
guard
():
image
=
fluid
.
layers
.
data
(
name
=
'image'
,
shape
=
image_shape
,
dtype
=
'float32'
)
image
.
stop_gradient
=
False
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
out
=
MobileNet
(
name
=
'quan'
).
net
(
input
=
image
,
class_dim
=
class_dim
)
print
(
"out: {}"
.
format
(
out
.
name
))
acc_top1
=
fluid
.
layers
.
accuracy
(
input
=
out
,
label
=
label
,
k
=
1
)
acc_top5
=
fluid
.
layers
.
accuracy
(
input
=
out
,
label
=
label
,
k
=
5
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
out
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
optimizer
=
fluid
.
optimizer
.
Momentum
(
momentum
=
0.9
,
learning_rate
=
0.01
,
regularization
=
fluid
.
regularizer
.
L2Decay
(
4e-5
))
val_program
=
train_program
.
clone
(
for_test
=
False
)
place
=
fluid
.
CUDAPlace
(
0
)
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup_program
)
val_reader
=
self
.
set_val_reader
(
image
,
label
,
place
)
val_feed_list
=
self
.
set_feed_list
(
image
,
label
)
val_fetch_list
=
[(
'acc_top1'
,
acc_top1
.
name
),
(
'acc_top5'
,
acc_top5
.
name
)]
train_reader
=
self
.
set_train_reader
(
image
,
label
,
place
)
train_feed_list
=
self
.
set_feed_list
(
image
,
label
)
train_fetch_list
=
[(
'loss'
,
avg_cost
.
name
)]
com_pass
=
Compressor
(
place
,
fluid
.
global_scope
(),
train_program
,
train_reader
=
train_reader
,
train_feed_list
=
train_feed_list
,
train_fetch_list
=
train_fetch_list
,
eval_program
=
val_program
,
eval_reader
=
val_reader
,
eval_feed_list
=
val_feed_list
,
eval_fetch_list
=
val_fetch_list
,
train_optimizer
=
optimizer
)
com_pass
.
config
(
config_file
)
eval_graph
=
com_pass
.
run
()
class
TestReader1
(
TestReader
):
def
set_train_reader
(
self
,
image
,
label
,
place
):
loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
feed_list
=
[
image
,
label
],
capacity
=
16
,
iterable
=
True
)
loader
.
set_sample_generator
(
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
128
,
places
=
place
)
return
loader
def
set_val_reader
(
self
,
image
,
label
,
place
):
loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
feed_list
=
[
image
,
label
],
capacity
=
16
,
iterable
=
True
)
loader
.
set_sample_generator
(
paddle
.
dataset
.
mnist
.
test
(),
batch_size
=
128
,
places
=
place
)
return
loader
def
test_compression
(
self
):
self
.
quan
(
"./quantization/compress_1.yaml"
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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