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体验新版 GitCode,发现更多精彩内容 >>
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36027490
编写于
8月 05, 2020
作者:
C
Chen Weihang
提交者:
GitHub
8月 05, 2020
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差异文件
Verify correctness of jit.save/jit.load - part 1 (#25915)
上级
82374dc1
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
408 addition
and
94 deletion
+408
-94
python/paddle/fluid/tests/unittests/dygraph_to_static/test_bert.py
...ddle/fluid/tests/unittests/dygraph_to_static/test_bert.py
+87
-6
python/paddle/fluid/tests/unittests/dygraph_to_static/test_bmn.py
...addle/fluid/tests/unittests/dygraph_to_static/test_bmn.py
+19
-1
python/paddle/fluid/tests/unittests/dygraph_to_static/test_lac.py
...addle/fluid/tests/unittests/dygraph_to_static/test_lac.py
+15
-0
python/paddle/fluid/tests/unittests/dygraph_to_static/test_mobile_net.py
...luid/tests/unittests/dygraph_to_static/test_mobile_net.py
+79
-6
python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet.py
...le/fluid/tests/unittests/dygraph_to_static/test_resnet.py
+142
-81
python/paddle/fluid/tests/unittests/dygraph_to_static/test_se_resnet.py
...fluid/tests/unittests/dygraph_to_static/test_se_resnet.py
+66
-0
未找到文件。
python/paddle/fluid/tests/unittests/dygraph_to_static/test_bert.py
浏览文件 @
36027490
...
...
@@ -18,6 +18,7 @@ import unittest
import
numpy
as
np
import
paddle.fluid
as
fluid
from
paddle.fluid.dygraph.dygraph_to_static
import
ProgramTranslator
from
paddle.fluid.dygraph.io
import
VARIABLE_FILENAME
from
bert_dygraph_model
import
PretrainModelLayer
from
bert_utils
import
get_bert_config
,
get_feed_data_reader
...
...
@@ -28,9 +29,11 @@ place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda() else fluid.CPUPlace(
SEED
=
2020
STEP_NUM
=
10
PRINT_STEP
=
2
MODEL_SAVE_PATH
=
"./bert.inference.model"
DY_STATE_DICT_SAVE_PATH
=
"./bert.dygraph"
def
train
(
bert_config
,
data_reader
):
def
train
(
bert_config
,
data_reader
,
to_static
):
with
fluid
.
dygraph
.
guard
(
place
):
fluid
.
default_main_program
().
random_seed
=
SEED
fluid
.
default_startup_program
().
random_seed
=
SEED
...
...
@@ -79,18 +82,74 @@ def train(bert_config, data_reader):
step_idx
+=
1
if
step_idx
==
STEP_NUM
:
if
to_static
:
fluid
.
dygraph
.
jit
.
save
(
bert
,
MODEL_SAVE_PATH
)
else
:
fluid
.
dygraph
.
save_dygraph
(
bert
.
state_dict
(),
DY_STATE_DICT_SAVE_PATH
)
break
return
loss
,
ppl
def
train_dygraph
(
bert_config
,
data_reader
):
program_translator
.
enable
(
False
)
return
train
(
bert_config
,
data_reader
)
return
train
(
bert_config
,
data_reader
,
False
)
def
train_static
(
bert_config
,
data_reader
):
program_translator
.
enable
(
True
)
return
train
(
bert_config
,
data_reader
)
return
train
(
bert_config
,
data_reader
,
True
)
def
predict_static
(
data
):
exe
=
fluid
.
Executor
(
place
)
# load inference model
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
MODEL_SAVE_PATH
,
executor
=
exe
,
params_filename
=
VARIABLE_FILENAME
)
pred_res
=
exe
.
run
(
inference_program
,
feed
=
dict
(
zip
(
feed_target_names
,
data
)),
fetch_list
=
fetch_targets
)
return
pred_res
def
predict_dygraph
(
bert_config
,
data
):
program_translator
.
enable
(
False
)
with
fluid
.
dygraph
.
guard
(
place
):
bert
=
PretrainModelLayer
(
config
=
bert_config
,
weight_sharing
=
False
,
use_fp16
=
False
)
model_dict
,
_
=
fluid
.
dygraph
.
load_dygraph
(
DY_STATE_DICT_SAVE_PATH
)
bert
.
set_dict
(
model_dict
)
bert
.
eval
()
input_vars
=
[
fluid
.
dygraph
.
to_variable
(
x
)
for
x
in
data
]
src_ids
,
pos_ids
,
sent_ids
,
input_mask
,
mask_label
,
mask_pos
,
labels
=
input_vars
pred_res
=
bert
(
src_ids
=
src_ids
,
position_ids
=
pos_ids
,
sentence_ids
=
sent_ids
,
input_mask
=
input_mask
,
mask_label
=
mask_label
,
mask_pos
=
mask_pos
,
labels
=
labels
)
pred_res
=
[
var
.
numpy
()
for
var
in
pred_res
]
return
pred_res
def
predict_dygraph_jit
(
data
):
with
fluid
.
dygraph
.
guard
(
place
):
bert
=
fluid
.
dygraph
.
jit
.
load
(
MODEL_SAVE_PATH
)
bert
.
eval
()
src_ids
,
pos_ids
,
sent_ids
,
input_mask
,
mask_label
,
mask_pos
,
labels
=
data
pred_res
=
bert
(
src_ids
,
pos_ids
,
sent_ids
,
input_mask
,
mask_label
,
mask_pos
,
labels
)
pred_res
=
[
var
.
numpy
()
for
var
in
pred_res
]
return
pred_res
class
TestBert
(
unittest
.
TestCase
):
...
...
@@ -104,14 +163,36 @@ class TestBert(unittest.TestCase):
dygraph_loss
,
dygraph_ppl
=
train_dygraph
(
self
.
bert_config
,
self
.
data_reader
)
self
.
assertTrue
(
np
.
allclose
(
static_loss
,
static
_loss
),
msg
=
"static_loss: {}
\n
static
_loss: {}"
.
format
(
static_loss
,
dygraph_loss
))
np
.
allclose
(
static_loss
,
dygraph
_loss
),
msg
=
"static_loss: {}
\n
dygraph
_loss: {}"
.
format
(
static_loss
,
dygraph_loss
))
self
.
assertTrue
(
np
.
allclose
(
static_ppl
,
dygraph_ppl
),
msg
=
"static_ppl: {}
\n
dygraph_ppl: {}"
.
format
(
static_ppl
,
dygraph_ppl
))
self
.
verify_predict
()
def
verify_predict
(
self
):
for
data
in
self
.
data_reader
.
data_generator
()():
dygraph_pred_res
=
predict_dygraph
(
self
.
bert_config
,
data
)
static_pred_res
=
predict_static
(
data
)
dygraph_jit_pred_res
=
predict_dygraph_jit
(
data
)
for
dy_res
,
st_res
,
dy_jit_res
in
zip
(
dygraph_pred_res
,
static_pred_res
,
dygraph_jit_pred_res
):
self
.
assertTrue
(
np
.
allclose
(
st_res
,
dy_res
),
"dygraph_res: {},
\n
static_res: {}"
.
format
(
dy_res
[
~
np
.
isclose
(
st_res
,
dy_res
)],
st_res
[
~
np
.
isclose
(
st_res
,
dy_res
)]))
self
.
assertTrue
(
np
.
allclose
(
st_res
,
dy_jit_res
),
"dygraph_jit_res: {},
\n
static_res: {}"
.
format
(
dy_jit_res
[
~
np
.
isclose
(
st_res
,
dy_jit_res
)],
st_res
[
~
np
.
isclose
(
st_res
,
dy_jit_res
)]))
break
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/dygraph_to_static/test_bmn.py
浏览文件 @
36027490
...
...
@@ -692,13 +692,20 @@ class TestTrain(unittest.TestCase):
video_data
=
np
.
array
([
item
[
0
]
for
item
in
data
]).
astype
(
DATATYPE
)
static_pred_res
=
self
.
predict_static
(
video_data
)
dygraph_pred_res
=
self
.
predict_dygraph
(
video_data
)
dygraph_jit_pred_res
=
self
.
predict_dygraph_jit
(
video_data
)
for
dy_res
,
st_res
in
zip
(
dygraph_pred_res
,
static_pred_res
):
for
dy_res
,
st_res
,
dy_jit_res
in
zip
(
dygraph_pred_res
,
static_pred_res
,
dygraph_jit_pred_res
):
self
.
assertTrue
(
np
.
allclose
(
st_res
,
dy_res
),
"dygraph_res: {},
\n
static_res: {}"
.
format
(
dy_res
[
~
np
.
isclose
(
st_res
,
dy_res
)],
st_res
[
~
np
.
isclose
(
st_res
,
dy_res
)]))
self
.
assertTrue
(
np
.
allclose
(
st_res
,
dy_jit_res
),
"dygraph_jit_res: {},
\n
static_res: {}"
.
format
(
dy_jit_res
[
~
np
.
isclose
(
st_res
,
dy_jit_res
)],
st_res
[
~
np
.
isclose
(
st_res
,
dy_jit_res
)]))
break
def
predict_dygraph
(
self
,
data
):
...
...
@@ -731,6 +738,17 @@ class TestTrain(unittest.TestCase):
return
pred_res
def
predict_dygraph_jit
(
self
,
data
):
with
fluid
.
dygraph
.
guard
(
self
.
place
):
bmn
=
fluid
.
dygraph
.
jit
.
load
(
self
.
args
.
infer_dir
)
bmn
.
eval
()
x
=
to_variable
(
data
)
pred_res
=
bmn
(
x
)
pred_res
=
[
var
.
numpy
()
for
var
in
pred_res
]
return
pred_res
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/dygraph_to_static/test_lac.py
浏览文件 @
36027490
...
...
@@ -535,9 +535,14 @@ class TestLACModel(unittest.TestCase):
batch
=
[
np
.
vstack
(
var
)
for
var
in
zip
(
*
batch
)]
dy_pre
=
self
.
predict_dygraph
(
batch
)
st_pre
=
self
.
predict_static
(
batch
)
dy_jit_pre
=
self
.
predict_dygraph_jit
(
batch
)
self
.
assertTrue
(
np
.
allclose
(
dy_pre
,
st_pre
),
msg
=
"dy_pre:
\n
{}
\n
, st_pre:
\n
{}."
.
format
(
dy_pre
,
st_pre
))
self
.
assertTrue
(
np
.
allclose
(
dy_jit_pre
,
st_pre
),
msg
=
"dy_jit_pre:
\n
{}
\n
, st_pre:
\n
{}."
.
format
(
dy_jit_pre
,
st_pre
))
def
predict_dygraph
(
self
,
batch
):
words
,
targets
,
length
=
batch
...
...
@@ -576,6 +581,16 @@ class TestLACModel(unittest.TestCase):
fetch_list
=
fetch_targets
)
return
pred_res
[
0
]
def
predict_dygraph_jit
(
self
,
batch
):
words
,
targets
,
length
=
batch
with
fluid
.
dygraph
.
guard
(
self
.
place
):
model
=
fluid
.
dygraph
.
jit
.
load
(
self
.
args
.
model_save_dir
)
model
.
eval
()
pred_res
=
model
(
to_variable
(
words
),
to_variable
(
length
))
return
pred_res
.
numpy
()
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/dygraph_to_static/test_mobile_net.py
浏览文件 @
36027490
...
...
@@ -19,6 +19,7 @@ from paddle.fluid.initializer import MSRA
from
paddle.fluid.param_attr
import
ParamAttr
from
paddle.fluid.dygraph.nn
import
Conv2D
,
Pool2D
,
BatchNorm
,
Linear
from
paddle.fluid.dygraph
import
declarative
,
ProgramTranslator
from
paddle.fluid.dygraph.io
import
VARIABLE_FILENAME
import
unittest
...
...
@@ -433,14 +434,15 @@ class Args(object):
class_dim
=
50
print_step
=
1
train_step
=
10
place
=
fluid
.
CUDAPlace
(
0
)
if
fluid
.
is_compiled_with_cuda
(
)
else
fluid
.
CPUPlace
()
model_save_path
=
model
+
".inference.model"
dy_state_dict_save_path
=
model
+
".dygraph"
def
train_mobilenet
(
args
,
to_static
):
program_translator
.
enable
(
to_static
)
place
=
fluid
.
CUDAPlace
(
0
)
if
fluid
.
is_compiled_with_cuda
(
)
else
fluid
.
CPUPlace
()
with
fluid
.
dygraph
.
guard
(
place
):
with
fluid
.
dygraph
.
guard
(
args
.
place
):
np
.
random
.
seed
(
SEED
)
fluid
.
default_startup_program
().
random_seed
=
SEED
...
...
@@ -461,7 +463,7 @@ def train_mobilenet(args, to_static):
# 3. reader
train_reader
=
fake_data_reader
(
args
.
batch_size
,
args
.
class_dim
)
train_data_loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
capacity
=
16
)
train_data_loader
.
set_sample_list_generator
(
train_reader
,
place
)
train_data_loader
.
set_sample_list_generator
(
train_reader
)
# 4. train loop
loss_data
=
[]
...
...
@@ -498,17 +500,64 @@ def train_mobilenet(args, to_static):
batch_id
+=
1
t_last
=
time
.
time
()
if
batch_id
>
args
.
train_step
:
if
to_static
:
fluid
.
dygraph
.
jit
.
save
(
net
,
args
.
model_save_path
)
else
:
fluid
.
dygraph
.
save_dygraph
(
net
.
state_dict
(),
args
.
dy_state_dict_save_path
)
break
return
np
.
array
(
loss_data
)
def
predict_static
(
args
,
data
):
exe
=
fluid
.
Executor
(
args
.
place
)
# load inference model
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
args
.
model_save_path
,
executor
=
exe
,
params_filename
=
VARIABLE_FILENAME
)
pred_res
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
data
},
fetch_list
=
fetch_targets
)
return
pred_res
[
0
]
def
predict_dygraph
(
args
,
data
):
program_translator
.
enable
(
False
)
with
fluid
.
dygraph
.
guard
(
args
.
place
):
if
args
.
model
==
"MobileNetV1"
:
model
=
MobileNetV1
(
class_dim
=
args
.
class_dim
,
scale
=
1.0
)
elif
args
.
model
==
"MobileNetV2"
:
model
=
MobileNetV2
(
class_dim
=
args
.
class_dim
,
scale
=
1.0
)
# load dygraph trained parameters
model_dict
,
_
=
fluid
.
load_dygraph
(
args
.
dy_state_dict_save_path
)
model
.
set_dict
(
model_dict
)
model
.
eval
()
pred_res
=
model
(
fluid
.
dygraph
.
to_variable
(
data
))
return
pred_res
.
numpy
()
def
predict_dygraph_jit
(
args
,
data
):
with
fluid
.
dygraph
.
guard
(
args
.
place
):
model
=
fluid
.
dygraph
.
jit
.
load
(
args
.
model_save_path
)
model
.
eval
()
pred_res
=
model
(
data
)
return
pred_res
.
numpy
()
class
TestMobileNet
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
args
=
Args
()
def
train
(
self
,
model_name
,
to_static
):
self
.
args
.
model
=
model_name
self
.
args
.
model_save_path
=
model_name
+
".inference.model"
self
.
args
.
dy_state_dict_save_path
=
model_name
+
".dygraph"
out
=
train_mobilenet
(
self
.
args
,
to_static
)
return
out
...
...
@@ -519,12 +568,36 @@ class TestMobileNet(unittest.TestCase):
np
.
allclose
(
dy_out
,
st_out
),
msg
=
"dy_out: {}, st_out: {}"
.
format
(
dy_out
,
st_out
))
def
test_mobileNet
(
self
):
def
assert_same_predict
(
self
,
model_name
):
self
.
args
.
model
=
model_name
self
.
args
.
model_save_path
=
model_name
+
".inference.model"
self
.
args
.
dy_state_dict_save_path
=
model_name
+
".dygraph"
local_random
=
np
.
random
.
RandomState
(
SEED
)
image
=
local_random
.
random_sample
([
1
,
3
,
224
,
224
]).
astype
(
'float32'
)
dy_pre
=
predict_dygraph
(
self
.
args
,
image
)
st_pre
=
predict_static
(
self
.
args
,
image
)
dy_jit_pre
=
predict_dygraph_jit
(
self
.
args
,
image
)
self
.
assertTrue
(
np
.
allclose
(
dy_pre
,
st_pre
),
msg
=
"dy_pre:
\n
{}
\n
, st_pre:
\n
{}."
.
format
(
dy_pre
,
st_pre
))
self
.
assertTrue
(
np
.
allclose
(
dy_jit_pre
,
st_pre
),
msg
=
"dy_jit_pre:
\n
{}
\n
, st_pre:
\n
{}."
.
format
(
dy_jit_pre
,
st_pre
))
def
test_mobile_net
(
self
):
# MobileNet-V1
self
.
assert_same_loss
(
"MobileNetV1"
)
# MobileNet-V2
self
.
assert_same_loss
(
"MobileNetV2"
)
self
.
verify_predict
()
def
verify_predict
(
self
):
# MobileNet-V1
self
.
assert_same_predict
(
"MobileNetV1"
)
# MobileNet-V2
self
.
assert_same_predict
(
"MobileNetV2"
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/dygraph_to_static/test_resnet.py
浏览文件 @
36027490
...
...
@@ -22,39 +22,33 @@ import numpy as np
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid.dygraph
.jit
import
dygraph_to_static_func
from
paddle.fluid.dygraph
import
declarative
,
ProgramTranslator
from
paddle.fluid.dygraph.nn
import
BatchNorm
,
Conv2D
,
Linear
,
Pool2D
from
paddle.fluid.dygraph.io
import
VARIABLE_FILENAME
SEED
=
2020
IMAGENET1000
=
1281167
base_lr
=
0.1
base_lr
=
0.
00
1
momentum_rate
=
0.9
l2_decay
=
1e-4
batch_size
=
8
epoch_num
=
1
place
=
fluid
.
CUDAPlace
(
0
)
if
fluid
.
is_compiled_with_cuda
()
\
else
fluid
.
CPUPlace
()
MODEL_SAVE_PATH
=
"./resnet.inference.model"
DY_STATE_DICT_SAVE_PATH
=
"./resnet.dygraph"
program_translator
=
ProgramTranslator
()
if
fluid
.
is_compiled_with_cuda
():
fluid
.
set_flags
({
'FLAGS_cudnn_deterministic'
:
True
})
def
optimizer_setting
(
parameter_list
=
None
):
total_images
=
IMAGENET1000
step
=
int
(
math
.
ceil
(
float
(
total_images
)
/
batch_size
))
epochs
=
[
30
,
60
,
90
]
bd
=
[
step
*
e
for
e
in
epochs
]
lr
=
[
base_lr
*
(
0.1
**
i
)
for
i
in
range
(
len
(
bd
)
+
1
)]
if
fluid
.
in_dygraph_mode
():
optimizer
=
fluid
.
optimizer
.
Momentum
(
learning_rate
=
fluid
.
layers
.
piecewise_decay
(
boundaries
=
bd
,
values
=
lr
),
momentum
=
momentum_rate
,
regularization
=
fluid
.
regularizer
.
L2Decay
(
l2_decay
),
parameter_list
=
parameter_list
)
else
:
optimizer
=
fluid
.
optimizer
.
Momentum
(
learning_rate
=
fluid
.
layers
.
piecewise_decay
(
boundaries
=
bd
,
values
=
lr
),
momentum
=
momentum_rate
,
regularization
=
fluid
.
regularizer
.
L2Decay
(
l2_decay
))
optimizer
=
fluid
.
optimizer
.
Momentum
(
learning_rate
=
base_lr
,
momentum
=
momentum_rate
,
regularization
=
fluid
.
regularizer
.
L2Decay
(
l2_decay
),
parameter_list
=
parameter_list
)
return
optimizer
...
...
@@ -189,8 +183,8 @@ class ResNet(fluid.dygraph.Layer):
param_attr
=
fluid
.
param_attr
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Uniform
(
-
stdv
,
stdv
)))
@
d
ygraph_to_static_func
def
forward
(
self
,
inputs
,
label
):
@
d
eclarative
def
forward
(
self
,
inputs
):
y
=
self
.
conv
(
inputs
)
y
=
self
.
pool2d_max
(
y
)
for
bottleneck_block
in
self
.
bottleneck_block_list
:
...
...
@@ -199,77 +193,144 @@ class ResNet(fluid.dygraph.Layer):
y
=
fluid
.
layers
.
reshape
(
y
,
shape
=
[
-
1
,
self
.
pool2d_avg_output
])
pred
=
self
.
out
(
y
)
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
pred
,
label
=
label
)
avg_loss_
=
fluid
.
layers
.
mean
(
x
=
loss
)
acc_top1_
=
fluid
.
layers
.
accuracy
(
input
=
pred
,
label
=
label
,
k
=
1
)
acc_top5_
=
fluid
.
layers
.
accuracy
(
input
=
pred
,
label
=
label
,
k
=
5
)
return
pred
return
pred
,
avg_loss_
,
acc_top1_
,
acc_top5_
def
reader_decorator
(
reader
):
def
__reader__
():
for
item
in
reader
():
img
=
np
.
array
(
item
[
0
]).
astype
(
'float32'
).
reshape
(
3
,
224
,
224
)
label
=
np
.
array
(
item
[
1
]).
astype
(
'int64'
).
reshape
(
1
)
yield
img
,
label
return
__reader__
def
train_resnet_in_static_mode
():
def
train
(
to_static
):
"""
Tests model decorated by `dygraph_to_static_output` in static mode. For users, the model is defined in dygraph mode and trained in static mode.
"""
with
fluid
.
dygraph
.
guard
(
place
):
np
.
random
.
seed
(
SEED
)
fluid
.
default_startup_program
().
random_seed
=
SEED
fluid
.
default_main_program
().
random_seed
=
SEED
train_reader
=
paddle
.
batch
(
reader_decorator
(
paddle
.
dataset
.
flowers
.
train
(
use_xmap
=
False
)),
batch_size
=
batch_size
,
drop_last
=
True
)
data_loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
capacity
=
5
,
iterable
=
True
)
data_loader
.
set_sample_list_generator
(
train_reader
)
resnet
=
ResNet
()
optimizer
=
optimizer_setting
(
parameter_list
=
resnet
.
parameters
())
for
epoch
in
range
(
epoch_num
):
total_loss
=
0.0
total_acc1
=
0.0
total_acc5
=
0.0
total_sample
=
0
for
batch_id
,
data
in
enumerate
(
data_loader
()):
start_time
=
time
.
time
()
img
,
label
=
data
pred
=
resnet
(
img
)
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
pred
,
label
=
label
)
avg_loss
=
fluid
.
layers
.
mean
(
x
=
loss
)
acc_top1
=
fluid
.
layers
.
accuracy
(
input
=
pred
,
label
=
label
,
k
=
1
)
acc_top5
=
fluid
.
layers
.
accuracy
(
input
=
pred
,
label
=
label
,
k
=
5
)
avg_loss
.
backward
()
optimizer
.
minimize
(
avg_loss
)
resnet
.
clear_gradients
()
total_loss
+=
avg_loss
total_acc1
+=
acc_top1
total_acc5
+=
acc_top5
total_sample
+=
1
end_time
=
time
.
time
()
if
batch_id
%
2
==
0
:
print
(
"epoch %d | batch step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, time %f"
%
\
(
epoch
,
batch_id
,
total_loss
.
numpy
()
/
total_sample
,
\
total_acc1
.
numpy
()
/
total_sample
,
total_acc5
.
numpy
()
/
total_sample
,
end_time
-
start_time
))
if
batch_id
==
10
:
if
to_static
:
fluid
.
dygraph
.
jit
.
save
(
resnet
,
MODEL_SAVE_PATH
)
else
:
fluid
.
dygraph
.
save_dygraph
(
resnet
.
state_dict
(),
DY_STATE_DICT_SAVE_PATH
)
# avoid dataloader throw abort signaal
data_loader
.
_reset
()
break
return
total_loss
.
numpy
()
def
predict_dygraph
(
data
):
program_translator
.
enable
(
False
)
with
fluid
.
dygraph
.
guard
(
place
):
resnet
=
ResNet
()
model_dict
,
_
=
fluid
.
dygraph
.
load_dygraph
(
DY_STATE_DICT_SAVE_PATH
)
resnet
.
set_dict
(
model_dict
)
resnet
.
eval
()
pred_res
=
resnet
(
fluid
.
dygraph
.
to_variable
(
data
))
return
pred_res
.
numpy
()
def
predict_static
(
data
):
exe
=
fluid
.
Executor
(
place
)
startup_prog
=
fluid
.
Program
()
main_prog
=
fluid
.
Program
()
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
MODEL_SAVE_PATH
,
executor
=
exe
,
params_filename
=
VARIABLE_FILENAME
)
with
fluid
.
program_guard
(
main_prog
,
startup_prog
):
pred_res
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
data
},
fetch_list
=
fetch_targets
)
img
=
fluid
.
data
(
name
=
"img"
,
shape
=
[
None
,
3
,
224
,
224
],
dtype
=
"float32"
)
label
=
fluid
.
data
(
name
=
"label"
,
shape
=
[
None
,
1
],
dtype
=
"int64"
)
label
.
stop_gradient
=
True
resnet
=
ResNet
()
pred
,
avg_loss_
,
acc_top1_
,
acc_top5_
=
resnet
(
img
,
label
)
optimizer
=
optimizer_setting
(
parameter_list
=
resnet
.
parameters
())
optimizer
.
minimize
(
avg_loss_
)
exe
.
run
(
startup_prog
)
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
flowers
.
train
(
use_xmap
=
False
),
batch_size
=
batch_size
)
for
epoch
in
range
(
epoch_num
):
total_loss
=
0.0
total_acc1
=
0.0
total_acc5
=
0.0
total_sample
=
0
for
batch_id
,
data
in
enumerate
(
train_reader
()):
start_time
=
time
.
time
()
dy_x_data
=
np
.
array
(
[
x
[
0
].
reshape
(
3
,
224
,
224
)
for
x
in
data
]).
astype
(
'float32'
)
if
len
(
np
.
array
([
x
[
1
]
for
x
in
data
]).
astype
(
'int64'
))
!=
batch_size
:
continue
y_data
=
np
.
array
([
x
[
1
]
for
x
in
data
]).
astype
(
'int64'
).
reshape
(
-
1
,
1
)
avg_loss
,
acc_top1
,
acc_top5
=
exe
.
run
(
main_prog
,
feed
=
{
"img"
:
dy_x_data
,
"label"
:
y_data
},
fetch_list
=
[
avg_loss_
,
acc_top1_
,
acc_top5_
])
total_loss
+=
avg_loss
total_acc1
+=
acc_top1
total_acc5
+=
acc_top5
total_sample
+=
1
end_time
=
time
.
time
()
if
batch_id
%
2
==
0
:
print
(
"epoch %d | batch step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, time %f"
%
\
(
epoch
,
batch_id
,
total_loss
/
total_sample
,
\
total_acc1
/
total_sample
,
total_acc5
/
total_sample
,
end_time
-
start_time
))
if
batch_id
==
10
:
break
return
pred_res
[
0
]
def
predict_dygraph_jit
(
data
):
with
fluid
.
dygraph
.
guard
(
place
):
resnet
=
fluid
.
dygraph
.
jit
.
load
(
MODEL_SAVE_PATH
)
resnet
.
eval
()
pred_res
=
resnet
(
data
)
return
pred_res
.
numpy
()
class
TestResnet
(
unittest
.
TestCase
):
def
test_in_static_mode
(
self
):
train_resnet_in_static_mode
()
def
train
(
self
,
to_static
):
program_translator
.
enable
(
to_static
)
return
train
(
to_static
)
def
verify_predict
(
self
):
image
=
np
.
random
.
random
([
1
,
3
,
224
,
224
]).
astype
(
'float32'
)
dy_pre
=
predict_dygraph
(
image
)
st_pre
=
predict_static
(
image
)
dy_jit_pre
=
predict_dygraph_jit
(
image
)
self
.
assertTrue
(
np
.
allclose
(
dy_pre
,
st_pre
),
msg
=
"dy_pre:
\n
{}
\n
, st_pre:
\n
{}."
.
format
(
dy_pre
,
st_pre
))
self
.
assertTrue
(
np
.
allclose
(
dy_jit_pre
,
st_pre
),
msg
=
"dy_jit_pre:
\n
{}
\n
, st_pre:
\n
{}."
.
format
(
dy_jit_pre
,
st_pre
))
def
test_resnet
(
self
):
static_loss
=
self
.
train
(
to_static
=
True
)
dygraph_loss
=
self
.
train
(
to_static
=
False
)
self
.
assertTrue
(
np
.
allclose
(
static_loss
,
dygraph_loss
),
msg
=
"static_loss: {}
\n
dygraph_loss: {}"
.
format
(
static_loss
,
dygraph_loss
))
self
.
verify_predict
()
if
__name__
==
'__main__'
:
...
...
python/paddle/fluid/tests/unittests/dygraph_to_static/test_se_resnet.py
浏览文件 @
36027490
...
...
@@ -24,6 +24,7 @@ from paddle.fluid.dygraph.base import to_variable
from
paddle.fluid.dygraph.nn
import
BatchNorm
,
Conv2D
,
Linear
,
Pool2D
from
paddle.fluid.dygraph
import
declarative
from
paddle.fluid.dygraph
import
ProgramTranslator
from
paddle.fluid.dygraph.io
import
VARIABLE_FILENAME
SEED
=
2020
np
.
random
.
seed
(
SEED
)
...
...
@@ -32,6 +33,8 @@ BATCH_SIZE = 8
EPOCH_NUM
=
1
PRINT_STEP
=
2
STEP_NUM
=
10
MODEL_SAVE_PATH
=
"./se_resnet.inference.model"
DY_STATE_DICT_SAVE_PATH
=
"./se_resnet.dygraph"
place
=
fluid
.
CUDAPlace
(
0
)
if
fluid
.
is_compiled_with_cuda
()
\
else
fluid
.
CPUPlace
()
...
...
@@ -377,11 +380,60 @@ def train(train_reader, to_static):
step_idx
+=
1
if
step_idx
==
STEP_NUM
:
if
to_static
:
configs
=
fluid
.
dygraph
.
jit
.
SaveLoadConfig
()
configs
.
output_spec
=
[
pred
]
fluid
.
dygraph
.
jit
.
save
(
se_resnext
,
MODEL_SAVE_PATH
,
[
img
],
configs
)
else
:
fluid
.
dygraph
.
save_dygraph
(
se_resnext
.
state_dict
(),
DY_STATE_DICT_SAVE_PATH
)
break
return
pred
.
numpy
(),
avg_loss
.
numpy
(),
acc_top1
.
numpy
(),
acc_top5
.
numpy
(
)
def
predict_dygraph
(
data
):
program_translator
=
ProgramTranslator
()
program_translator
.
enable
(
False
)
with
fluid
.
dygraph
.
guard
(
place
):
se_resnext
=
SeResNeXt
()
model_dict
,
_
=
fluid
.
dygraph
.
load_dygraph
(
DY_STATE_DICT_SAVE_PATH
)
se_resnext
.
set_dict
(
model_dict
)
se_resnext
.
eval
()
label
=
np
.
random
.
random
([
1
,
1
]).
astype
(
"int64"
)
img
=
fluid
.
dygraph
.
to_variable
(
data
)
label
=
fluid
.
dygraph
.
to_variable
(
label
)
pred_res
,
_
,
_
,
_
=
se_resnext
(
img
,
label
)
return
pred_res
.
numpy
()
def
predict_static
(
data
):
exe
=
fluid
.
Executor
(
place
)
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
MODEL_SAVE_PATH
,
executor
=
exe
,
params_filename
=
VARIABLE_FILENAME
)
pred_res
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
data
},
fetch_list
=
fetch_targets
)
return
pred_res
[
0
]
def
predict_dygraph_jit
(
data
):
with
fluid
.
dygraph
.
guard
(
place
):
se_resnext
=
fluid
.
dygraph
.
jit
.
load
(
MODEL_SAVE_PATH
)
se_resnext
.
eval
()
pred_res
=
se_resnext
(
data
)
return
pred_res
.
numpy
()
class
TestSeResnet
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
train_reader
=
paddle
.
batch
(
...
...
@@ -390,6 +442,18 @@ class TestSeResnet(unittest.TestCase):
batch_size
=
BATCH_SIZE
,
drop_last
=
True
)
def
verify_predict
(
self
):
image
=
np
.
random
.
random
([
1
,
3
,
224
,
224
]).
astype
(
'float32'
)
dy_pre
=
predict_dygraph
(
image
)
st_pre
=
predict_static
(
image
)
dy_jit_pre
=
predict_dygraph_jit
(
image
)
self
.
assertTrue
(
np
.
allclose
(
dy_pre
,
st_pre
),
msg
=
"dy_pre:
\n
{}
\n
, st_pre:
\n
{}."
.
format
(
dy_pre
,
st_pre
))
self
.
assertTrue
(
np
.
allclose
(
dy_jit_pre
,
st_pre
),
msg
=
"dy_jit_pre:
\n
{}
\n
, st_pre:
\n
{}."
.
format
(
dy_jit_pre
,
st_pre
))
def
test_check_result
(
self
):
pred_1
,
loss_1
,
acc1_1
,
acc5_1
=
train
(
self
.
train_reader
,
to_static
=
False
)
...
...
@@ -409,6 +473,8 @@ class TestSeResnet(unittest.TestCase):
np
.
allclose
(
acc5_1
,
acc5_2
),
msg
=
"static acc5: {}
\n
dygraph acc5: {}"
.
format
(
acc5_1
,
acc5_2
))
self
.
verify_predict
()
if
__name__
==
'__main__'
:
unittest
.
main
()
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