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体验新版 GitCode,发现更多精彩内容 >>
提交
1b1f305b
编写于
1月 31, 2018
作者:
Y
Yang Yu
浏览文件
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电子邮件补丁
差异文件
Make image_classification as a normal python unittest
上级
c091dbdf
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
89 addition
and
58 deletion
+89
-58
python/paddle/v2/fluid/tests/book/CMakeLists.txt
python/paddle/v2/fluid/tests/book/CMakeLists.txt
+1
-3
python/paddle/v2/fluid/tests/book/test_image_classification_train.py
...le/v2/fluid/tests/book/test_image_classification_train.py
+88
-55
未找到文件。
python/paddle/v2/fluid/tests/book/CMakeLists.txt
浏览文件 @
1b1f305b
file
(
GLOB TEST_OPS RELATIVE
"
${
CMAKE_CURRENT_SOURCE_DIR
}
"
"test_*.py"
)
string
(
REPLACE
".py"
""
TEST_OPS
"
${
TEST_OPS
}
"
)
list
(
REMOVE_ITEM TEST_OPS test_image_classification_train test_recognize_digits
)
py_test
(
test_image_classification_train_resnet SRCS test_image_classification_train.py ARGS resnet
)
py_test
(
test_image_classification_train_vgg SRCS test_image_classification_train.py ARGS vgg
)
list
(
REMOVE_ITEM TEST_OPS test_recognize_digits
)
py_test
(
test_recognize_digits_mlp_cpu
SRCS test_recognize_digits.py
ARGS mlp
)
...
...
python/paddle/v2/fluid/tests/book/test_image_classification_train.py
浏览文件 @
1b1f305b
...
...
@@ -14,10 +14,10 @@
from
__future__
import
print_function
import
sys
import
paddle.v2
as
paddle
import
paddle.v2.fluid
as
fluid
import
unittest
import
contextlib
def
resnet_cifar10
(
input
,
depth
=
32
):
...
...
@@ -89,56 +89,89 @@ def vgg16_bn_drop(input):
return
fc2
classdim
=
10
data_shape
=
[
3
,
32
,
32
]
images
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
data_shape
,
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
net_type
=
"vgg"
if
len
(
sys
.
argv
)
>=
2
:
net_type
=
sys
.
argv
[
1
]
if
net_type
==
"vgg"
:
print
(
"train vgg net"
)
net
=
vgg16_bn_drop
(
images
)
elif
net_type
==
"resnet"
:
print
(
"train resnet"
)
net
=
resnet_cifar10
(
images
,
32
)
else
:
raise
ValueError
(
"%s network is not supported"
%
net_type
)
predict
=
fluid
.
layers
.
fc
(
input
=
net
,
size
=
classdim
,
act
=
'softmax'
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
opts
=
optimizer
.
minimize
(
avg_cost
)
accuracy
=
fluid
.
evaluator
.
Accuracy
(
input
=
predict
,
label
=
label
)
BATCH_SIZE
=
128
PASS_NUM
=
1
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
cifar
.
train10
(),
buf_size
=
128
*
10
),
batch_size
=
BATCH_SIZE
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
feeder
=
fluid
.
DataFeeder
(
place
=
place
,
feed_list
=
[
images
,
label
])
exe
.
run
(
fluid
.
default_startup_program
())
for
pass_id
in
range
(
PASS_NUM
):
accuracy
.
reset
(
exe
)
for
data
in
train_reader
():
loss
,
acc
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
]
+
accuracy
.
metrics
)
pass_acc
=
accuracy
.
eval
(
exe
)
print
(
"loss:"
+
str
(
loss
)
+
" acc:"
+
str
(
acc
)
+
" pass_acc:"
+
str
(
pass_acc
))
# this model is slow, so if we can train two mini batch, we think it works properly.
exit
(
0
)
exit
(
1
)
def
main
(
net_type
,
use_cuda
):
if
use_cuda
and
not
fluid
.
core
.
is_compiled_with_cuda
():
return
classdim
=
10
data_shape
=
[
3
,
32
,
32
]
images
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
data_shape
,
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
if
net_type
==
"vgg"
:
print
(
"train vgg net"
)
net
=
vgg16_bn_drop
(
images
)
elif
net_type
==
"resnet"
:
print
(
"train resnet"
)
net
=
resnet_cifar10
(
images
,
32
)
else
:
raise
ValueError
(
"%s network is not supported"
%
net_type
)
predict
=
fluid
.
layers
.
fc
(
input
=
net
,
size
=
classdim
,
act
=
'softmax'
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
optimizer
.
minimize
(
avg_cost
)
accuracy
=
fluid
.
evaluator
.
Accuracy
(
input
=
predict
,
label
=
label
)
BATCH_SIZE
=
128
PASS_NUM
=
1
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
cifar
.
train10
(),
buf_size
=
128
*
10
),
batch_size
=
BATCH_SIZE
)
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
feeder
=
fluid
.
DataFeeder
(
place
=
place
,
feed_list
=
[
images
,
label
])
exe
.
run
(
fluid
.
default_startup_program
())
loss
=
0.0
for
pass_id
in
range
(
PASS_NUM
):
accuracy
.
reset
(
exe
)
for
data
in
train_reader
():
loss
,
acc
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
]
+
accuracy
.
metrics
)
pass_acc
=
accuracy
.
eval
(
exe
)
print
(
"loss:"
+
str
(
loss
)
+
" acc:"
+
str
(
acc
)
+
" pass_acc:"
+
str
(
pass_acc
))
return
raise
AssertionError
(
"Image classification loss is too large, {0:2.2}"
.
format
(
loss
))
class
TestImageClassification
(
unittest
.
TestCase
):
def
test_vgg_cuda
(
self
):
with
self
.
scope_prog_guard
():
main
(
'vgg'
,
use_cuda
=
True
)
def
test_resnet_cuda
(
self
):
with
self
.
scope_prog_guard
():
main
(
'resnet'
,
use_cuda
=
True
)
def
test_vgg_cpu
(
self
):
with
self
.
scope_prog_guard
():
main
(
'vgg'
,
use_cuda
=
False
)
def
test_resnet_cpu
(
self
):
with
self
.
scope_prog_guard
():
main
(
'resnet'
,
use_cuda
=
False
)
@
contextlib
.
contextmanager
def
scope_prog_guard
(
self
):
prog
=
fluid
.
Program
()
startup_prog
=
fluid
.
Program
()
scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
scope
):
with
fluid
.
program_guard
(
prog
,
startup_prog
):
yield
if
__name__
==
'__main__'
:
unittest
.
main
()
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