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体验新版 GitCode,发现更多精彩内容 >>
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a424ab49
编写于
3月 11, 2019
作者:
M
minqiyang
浏览文件
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差异文件
Change CMakeFiles
test=develop
上级
feb39028
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
41 addition
and
95 deletion
+41
-95
python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+2
-2
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
+39
-93
未找到文件。
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
a424ab49
...
...
@@ -76,7 +76,7 @@ list(REMOVE_ITEM TEST_OPS test_image_classification_resnet)
list
(
REMOVE_ITEM TEST_OPS test_bilinear_interp_op
)
list
(
REMOVE_ITEM TEST_OPS test_nearest_interp_op
)
list
(
REMOVE_ITEM TEST_OPS test_imperative_resnet
)
list
(
REMOVE_ITEM TEST_OPS test_imperative_
optimizer
)
list
(
REMOVE_ITEM TEST_OPS test_imperative_
mnist
)
list
(
REMOVE_ITEM TEST_OPS test_ir_memory_optimize_transformer
)
foreach
(
TEST_OP
${
TEST_OPS
}
)
py_test_modules
(
${
TEST_OP
}
MODULES
${
TEST_OP
}
)
...
...
@@ -87,7 +87,7 @@ py_test_modules(test_bilinear_interp_op MODULES test_bilinear_interp_op SERIAL)
py_test_modules
(
test_nearest_interp_op MODULES test_nearest_interp_op SERIAL
)
py_test_modules
(
test_imperative_resnet MODULES test_imperative_resnet ENVS
FLAGS_cudnn_deterministic=1
)
py_test_modules
(
test_imperative_
optimizer MODULES test_imperative_optimizer
ENVS
py_test_modules
(
test_imperative_
mnist MODULES test_imperative_mnist
ENVS
FLAGS_cudnn_deterministic=1
)
if
(
WITH_DISTRIBUTE
)
py_test_modules
(
test_dist_train MODULES test_dist_train SERIAL
)
...
...
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
浏览文件 @
a424ab49
...
...
@@ -12,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
print_function
import
contextlib
import
unittest
import
numpy
as
np
...
...
@@ -21,112 +23,56 @@ import paddle
import
paddle.fluid
as
fluid
from
paddle.fluid
import
core
from
paddle.fluid.optimizer
import
SGDOptimizer
from
paddle.fluid.imperative.nn
import
Conv2D
,
Pool2D
,
FC
from
paddle.fluid.imperative.nn
import
FC
from
paddle.fluid.imperative.base
import
to_variable
from
test_imperative_base
import
new_program_scope
class
SimpleImgConvPool
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
,
num_channels
,
num_filters
,
filter_size
,
pool_size
,
pool_stride
,
pool_padding
=
0
,
pool_type
=
'max'
,
global_pooling
=
False
,
conv_stride
=
1
,
conv_padding
=
0
,
conv_dilation
=
1
,
conv_groups
=
1
,
act
=
None
,
use_cudnn
=
False
,
param_attr
=
None
,
bias_attr
=
None
):
super
(
SimpleImgConvPool
,
self
).
__init__
()
self
.
_conv2d
=
Conv2D
(
num_channels
=
num_channels
,
num_filters
=
num_filters
,
filter_size
=
filter_size
,
stride
=
conv_stride
,
padding
=
conv_padding
,
dilation
=
conv_dilation
,
groups
=
conv_groups
,
param_attr
=
None
,
bias_attr
=
None
,
use_cudnn
=
use_cudnn
)
self
.
_pool2d
=
Pool2D
(
pool_size
=
pool_size
,
pool_type
=
pool_type
,
pool_stride
=
pool_stride
,
pool_padding
=
pool_padding
,
global_pooling
=
global_pooling
,
use_cudnn
=
use_cudnn
)
def
forward
(
self
,
inputs
):
x
=
self
.
_conv2d
(
inputs
)
x
=
self
.
_pool2d
(
x
)
return
x
class
MNIST
(
fluid
.
imperative
.
Layer
):
class
MLP
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
,
param_attr
=
None
,
bias_attr
=
None
):
super
(
MNIST
,
self
).
__init__
()
self
.
_simple_img_conv_pool_1
=
SimpleImgConvPool
(
1
,
20
,
5
,
2
,
2
,
act
=
"relu"
)
self
.
_fc1
=
FC
(
10
)
self
.
_fc2
=
FC
(
10
)
self
.
_simple_img_conv_pool_2
=
SimpleImgConvPool
(
20
,
50
,
5
,
2
,
2
,
act
=
"relu"
)
def
forward
(
self
,
inputs
):
y
=
self
.
_fc1
(
inputs
)
y
=
self
.
_fc2
(
y
)
return
y
pool_2_shape
=
50
*
8
*
8
SIZE
=
10
scale
=
(
2.0
/
(
pool_2_shape
**
2
*
SIZE
))
**
0.5
self
.
_fc
=
FC
(
10
,
param_attr
=
fluid
.
param_attr
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NormalInitializer
(
loc
=
0.0
,
scale
=
scale
)))
def
forward
(
self
,
inputs
):
x
=
self
.
_simple_img_conv_pool_1
(
inputs
)
x
=
self
.
_simple_img_conv_pool_2
(
x
)
x
=
self
.
_fc
(
x
)
return
x
class
TestImperativeOptimizerBase
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
batch_num
=
2
def
get_optimizer
(
self
):
self
.
optimizer
=
SGDOptimizer
(
learning_rate
=
1e-3
)
class
TestImperativeMnist
(
unittest
.
TestCase
):
def
test_mnist_cpu_float32
(
self
):
def
test_optimizer_float32
(
self
):
seed
=
90
with
fluid
.
imperative
.
guard
():
fluid
.
default_startup_program
().
random_seed
=
seed
fluid
.
default_main_program
().
random_seed
=
seed
m
nist
=
MNIST
()
s
gd
=
SGDOptimizer
(
learning_rate
=
1e-3
)
m
lp
=
MLP
()
s
elf
.
get_optimizer
(
)
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
128
)
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
128
,
drop_last
=
True
)
dy_param_init_value
=
{}
for
batch_id
,
data
in
enumerate
(
train_reader
()):
if
batch_id
>=
2
:
if
batch_id
>=
self
.
batch_num
:
break
x_data
=
np
.
array
(
dy_
x_data
=
np
.
array
(
[
x
[
0
].
reshape
(
1
,
28
,
28
)
for
x
in
data
]).
astype
(
'float32'
)
y_data
=
np
.
array
([
x
[
1
]
for
x
in
data
]).
astype
(
'int64'
).
reshape
(
128
,
1
)
img
=
to_variable
(
x_data
)
img
=
to_variable
(
dy_
x_data
)
label
=
to_variable
(
y_data
)
label
.
_stop_gradient
=
True
cost
=
mnist
(
img
)
loss
=
fluid
.
layers
.
cross_entropy
(
cost
,
label
)
avg_loss
=
fluid
.
layers
.
mean
(
loss
)
cost
=
mlp
(
img
)
avg_loss
=
fluid
.
layers
.
reduce_mean
(
cost
)
dy_out
=
avg_loss
.
_numpy
()
if
batch_id
==
0
:
...
...
@@ -135,7 +81,8 @@ class TestImperativeMnist(unittest.TestCase):
dy_param_init_value
[
param
.
name
]
=
param
.
_numpy
()
avg_loss
.
_backward
()
sgd
.
minimize
(
avg_loss
)
self
.
optimizer
.
minimize
(
avg_loss
)
mlp
.
clear_gradients
()
dy_param_value
=
{}
for
param
in
fluid
.
default_main_program
().
global_block
(
).
all_parameters
():
...
...
@@ -149,23 +96,21 @@ class TestImperativeMnist(unittest.TestCase):
)
if
not
core
.
is_compiled_with_cuda
()
else
fluid
.
CUDAPlace
(
0
))
mnist
=
MNIST
()
s
gd
=
SGDOptimizer
(
learning_rate
=
1e-3
)
s
elf
.
get_optimizer
(
)
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
128
)
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
128
,
drop_last
=
True
)
img
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
[
1
,
28
,
28
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
cost
=
mnist
(
img
)
loss
=
fluid
.
layers
.
cross_entropy
(
cost
,
label
)
avg_loss
=
fluid
.
layers
.
mean
(
loss
)
sgd
.
minimize
(
avg_loss
)
avg_loss
=
fluid
.
layers
.
reduce_mean
(
cost
)
self
.
optimizer
.
minimize
(
avg_loss
)
# initialize params and fetch them
static_param_init_value
=
{}
static_param_name_list
=
[]
for
param
in
fluid
.
default_startup_program
().
global_block
(
).
all_parameters
():
for
param
in
mnist
.
parameters
():
static_param_name_list
.
append
(
param
.
name
)
out
=
exe
.
run
(
fluid
.
default_startup_program
(),
...
...
@@ -175,10 +120,10 @@ class TestImperativeMnist(unittest.TestCase):
static_param_init_value
[
static_param_name_list
[
i
]]
=
out
[
i
]
for
batch_id
,
data
in
enumerate
(
train_reader
()):
if
batch_id
>=
2
:
if
batch_id
>=
self
.
batch_num
:
break
x_data
=
np
.
array
(
static_
x_data
=
np
.
array
(
[
x
[
0
].
reshape
(
1
,
28
,
28
)
for
x
in
data
]).
astype
(
'float32'
)
y_data
=
np
.
array
([
x
[
1
]
for
x
in
data
]).
astype
(
'int64'
).
reshape
(
[
128
,
1
])
...
...
@@ -186,7 +131,7 @@ class TestImperativeMnist(unittest.TestCase):
fetch_list
=
[
avg_loss
.
name
]
fetch_list
.
extend
(
static_param_name_list
)
out
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"pixel"
:
x_data
,
feed
=
{
"pixel"
:
static_
x_data
,
"label"
:
y_data
},
fetch_list
=
fetch_list
)
...
...
@@ -196,11 +141,12 @@ class TestImperativeMnist(unittest.TestCase):
static_param_value
[
static_param_name_list
[
i
-
1
]]
=
out
[
i
]
for
key
,
value
in
six
.
iteritems
(
static_param_init_value
):
self
.
assertTrue
(
np
.
allclose
(
value
.
all
(),
dy_param_init_value
[
key
].
all
()))
self
.
assertTrue
(
np
.
allclose
(
static_out
.
all
(),
dy_out
.
all
()))
self
.
assertTrue
(
np
.
allclose
(
value
,
dy_param_init_value
[
key
]))
self
.
assertTrue
(
np
.
allclose
(
static_out
,
dy_out
))
for
key
,
value
in
six
.
iteritems
(
static_param_value
):
self
.
assertTrue
(
np
.
allclose
(
value
.
all
(),
dy_param_value
[
key
].
all
()
))
self
.
assertTrue
(
np
.
allclose
(
value
,
dy_param_value
[
key
],
atol
=
1e-5
))
if
__name__
==
'__main__'
:
...
...
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