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91d87ec0
编写于
1月 14, 2019
作者:
M
minqiyang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add unittest for imperative resnet
Fix the bug of static BatchNorm layer
上级
e33427da
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
417 addition
and
5 deletion
+417
-5
python/paddle/fluid/imperative/nn.py
python/paddle/fluid/imperative/nn.py
+142
-3
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+2
-2
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
...on/paddle/fluid/tests/unittests/test_imperative_resnet.py
+273
-0
未找到文件。
python/paddle/fluid/imperative/nn.py
浏览文件 @
91d87ec0
...
...
@@ -27,6 +27,7 @@ __all__ = [
'Conv2D'
,
'Pool2D'
,
'FC'
,
'BatchNorm'
,
]
...
...
@@ -209,14 +210,24 @@ class FC(layers.Layer):
def
__init__
(
self
,
size
,
param_attr
=
None
,
bias_attr
=
None
,
dtype
=
core
.
VarDesc
.
VarType
.
FP32
,
num_flatten_dims
=
1
,
dtype
=
core
.
VarDesc
.
VarType
.
FP32
):
act
=
None
,
is_test
=
False
,
name
=
None
):
super
(
FC
,
self
).
__init__
()
self
.
_size
=
size
self
.
_num_flatten_dims
=
num_flatten_dims
self
.
_dtype
=
dtype
from
..layer_helper
import
LayerHelper
self
.
_helper
=
LayerHelper
(
'FC'
,
param_attr
=
param_attr
)
self
.
_helper
=
LayerHelper
(
'FC'
,
param_attr
=
param_attr
,
bias_attr
=
bias_attr
,
act
=
act
,
name
=
name
)
def
_build_once
(
self
,
input
):
input_shape
=
input
.
shape
...
...
@@ -247,4 +258,132 @@ class FC(layers.Layer):
inputs
=
{
"X"
:
[
tmp
]},
outputs
=
{
"Out"
:
out
},
attrs
=
{
"use_mkldnn"
:
False
})
return
out
pre_activation
=
self
.
_helper
.
append_bias_op
(
pre_bias
,
dim_start
=
num_flatten_dims
)
return
self
.
_helper
.
append_activation
(
pre_activation
)
class
BatchNorm
(
layers
.
Layer
):
def
__init__
(
self
,
num_channels
,
act
=
None
,
is_test
=
False
,
momentum
=
0.9
,
epsilon
=
1e-05
,
param_attr
=
None
,
bias_attr
=
None
,
dtype
=
core
.
VarDesc
.
VarType
.
FP32
,
data_layout
=
'NCHW'
,
in_place
=
False
,
name
=
None
,
moving_mean_name
=
None
,
moving_variance_name
=
None
,
do_model_average_for_mean_and_var
=
False
,
fuse_with_relu
=
False
,
use_global_stats
=
False
):
super
(
BatchNorm
,
self
).
__init__
()
assert
bias_attr
is
not
False
,
"bias_attr should not be False in batch_norm."
from
..layer_helper
import
LayerHelper
self
.
_helper
=
LayerHelper
(
'batch_norm'
,
param_attr
=
param_attr
,
bias_attr
=
bias_attr
,
name
=
name
)
if
dtype
==
core
.
VarDesc
.
VarType
.
FP16
:
self
.
_dtype
=
core
.
VarDesc
.
VarType
.
FP32
else
:
self
.
_dtype
=
dtype
param_shape
=
[
num_channels
]
# create parameter
self
.
_scale
=
self
.
_helper
.
create_parameter
(
attr
=
self
.
_helper
.
param_attr
,
shape
=
param_shape
,
dtype
=
self
.
_dtype
,
default_initializer
=
Constant
(
1.0
))
# setting stop_gradient=True to reduce computation
if
use_global_stats
and
self
.
_helper
.
param_attr
.
learning_rate
==
0.
:
self
.
_scale
.
stop_gradient
=
True
self
.
_bias
=
self
.
_helper
.
create_parameter
(
attr
=
self
.
_helper
.
bias_attr
,
shape
=
param_shape
,
dtype
=
self
.
_dtype
,
is_bias
=
True
)
# setting stop_gradient=True to reduce computation
if
use_global_stats
and
self
.
_helper
.
bias_attr
.
learning_rate
==
0.
:
self
.
_bias
.
stop_gradient
=
True
self
.
_mean
=
self
.
_helper
.
create_parameter
(
attr
=
ParamAttr
(
name
=
moving_mean_name
,
initializer
=
Constant
(
0.0
),
trainable
=
False
,
do_model_average
=
do_model_average_for_mean_and_var
),
shape
=
param_shape
,
dtype
=
self
.
_dtype
)
self
.
_mean
.
stop_gradient
=
True
self
.
_variance
=
self
.
_helper
.
create_parameter
(
attr
=
ParamAttr
(
name
=
moving_variance_name
,
initializer
=
Constant
(
1.0
),
trainable
=
False
,
do_model_average
=
do_model_average_for_mean_and_var
),
shape
=
param_shape
,
dtype
=
self
.
_dtype
)
self
.
_variance
.
stop_gradient
=
True
self
.
_in_place
=
in_place
self
.
_momentum
=
momentum
self
.
_epsilon
=
epsilon
self
.
_is_test
=
is_test
self
.
_fuse_with_relu
=
fuse_with_relu
self
.
_use_global_stats
=
use_global_stats
def
_build_once
(
self
,
input
):
pass
def
forward
(
self
,
input
):
# create output
# mean and mean_out share the same memory
mean_out
=
self
.
_mean
# variance and variance out share the same memory
variance_out
=
self
.
_variance
saved_mean
=
self
.
_helper
.
create_variable_for_type_inference
(
dtype
=
dtype
,
stop_gradient
=
True
)
saved_variance
=
self
.
_helper
.
create_variable_for_type_inference
(
dtype
=
dtype
,
stop_gradient
=
True
)
batch_norm_out
=
input
if
self
.
_in_place
else
self
.
_helper
.
create_variable_for_type_inference
(
dtype
)
self
.
_helper
.
append_op
(
type
=
"batch_norm"
,
inputs
=
{
"X"
:
input
,
"Scale"
:
self
.
_scale
,
"Bias"
:
self
.
_bias
,
"Mean"
:
self
.
_mean
,
"Variance"
:
self
.
_variance
},
outputs
=
{
"Y"
:
batch_norm_out
,
"MeanOut"
:
mean_out
,
"VarianceOut"
:
variance_out
,
"SavedMean"
:
saved_mean
,
"SavedVariance"
:
saved_variance
},
attrs
=
{
"momentum"
:
self
.
_momentum
,
"epsilon"
:
self
.
_epsilon
,
"is_test"
:
self
.
_is_test
,
"use_mkldnn"
:
False
,
"fuse_with_relu"
:
self
.
_fuse_with_relu
,
"use_global_stats"
:
self
.
_use_global_stats
})
return
self
.
_helper
.
append_activation
(
batch_norm_out
)
python/paddle/fluid/layers/nn.py
浏览文件 @
91d87ec0
...
...
@@ -2835,7 +2835,7 @@ def batch_norm(input,
attr
=
helper
.
bias_attr
,
shape
=
param_shape
,
dtype
=
dtype
,
is_bias
=
True
)
# setting stop_gradient=True to reduce computation
if
use_global_stats
and
helper
.
bias_attr
.
learning_rate
==
0.
:
scale
.
stop_gradient
=
True
bias
.
stop_gradient
=
True
mean
=
helper
.
create_parameter
(
attr
=
ParamAttr
(
...
...
@@ -9412,7 +9412,7 @@ def teacher_student_sigmoid_loss(input,
by the previous operator.
label (Variable|list): the ground truth which is a 2-D tensor with
shape [N x 1], where N is the batch size.
soft_max_up_bound (float): if input > soft_max_up_bound, will be bound
soft_max_up_bound (float): if input > soft_max_up_bound, will be bound
soft_max_lower_bound (float): if input < soft_max_lower_bound, will be bound
Returns:
...
...
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
0 → 100644
浏览文件 @
91d87ec0
# Copyright (c) 2018 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
contextlib
import
unittest
import
numpy
as
np
import
six
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
,
BatchNorm
,
FC
from
paddle.fluid.imperative.base
import
to_variable
from
test_imperative_base
import
new_program_scope
train_parameters
=
{
"input_size"
:
[
3
,
224
,
224
],
"input_mean"
:
[
0.485
,
0.456
,
0.406
],
"input_std"
:
[
0.229
,
0.224
,
0.225
],
"learning_strategy"
:
{
"name"
:
"piecewise_decay"
,
"batch_size"
:
256
,
"epochs"
:
[
30
,
60
,
90
],
"steps"
:
[
0.1
,
0.01
,
0.001
,
0.0001
]
}
}
def
optimizer_setting
(
params
):
ls
=
params
[
"learning_strategy"
]
if
ls
[
"name"
]
==
"piecewise_decay"
:
if
"total_images"
not
in
params
:
total_images
=
1281167
else
:
total_images
=
params
[
"total_images"
]
batch_size
=
ls
[
"batch_size"
]
step
=
int
(
total_images
/
batch_size
+
1
)
bd
=
[
step
*
e
for
e
in
ls
[
"epochs"
]]
base_lr
=
params
[
"lr"
]
lr
=
[]
lr
=
[
base_lr
*
(
0.1
**
i
)
for
i
in
range
(
len
(
bd
)
+
1
)]
optimizer
=
fluid
.
optimizer
.
Momentum
(
learning_rate
=
fluid
.
layers
.
piecewise_decay
(
boundaries
=
bd
,
values
=
lr
),
momentum
=
0.9
,
regularization
=
fluid
.
regularizer
.
L2Decay
(
1e-4
))
return
optimizer
class
ConvBNLayer
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
,
num_filters
,
filter_size
,
stride
=
1
,
groups
=
1
,
act
=
None
):
super
(
ConvBNLayer
,
self
).
__init__
()
self
.
_conv
=
Conv2D
(
3
,
num_filters
,
filter_size
,
stride
,
(
filter_size
-
1
)
//
2
,
groups
=
groups
,
act
=
None
,
bias_attr
=
None
)
self
.
_batch_norm
=
BatchNorm
(
num_filters
,
act
=
act
)
def
forward
(
self
,
inputs
):
y
=
self
.
_conv
(
inputs
)
y
=
self
.
_batch_norm
(
y
)
return
y
class
BottleneckBlock
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
,
num_filters
,
stride
,
shortcut
=
False
):
super
(
BottleneckBlock
,
self
).
__init__
()
self
.
conv0
=
ConvBNLayer
(
num_filters
=
num_filters
,
filter_size
=
1
,
act
=
'relu'
)
self
.
conv1
=
ConvBNLayer
(
num_filters
=
num_filters
,
filter_size
=
3
,
stride
=
stride
,
act
=
'relu'
)
self
.
conv2
=
ConvBNLayer
(
num_filters
=
num_filters
*
4
,
filter_size
=
1
,
act
=
None
)
if
shortcut
:
self
.
short
=
ConvBNLayer
(
num_filters
=
num_filters
*
4
,
filter_size
=
1
,
stride
=
stride
)
self
.
shortcut
=
shortcut
def
forward
(
self
,
inputs
):
self
.
conv0
()
self
.
conv1
()
self
.
conv2
()
if
self
.
shortcut
:
self
.
short
()
return
fluid
.
layers
.
elementwise_add
(
x
=
self
.
short
,
y
=
self
.
conv2
,
act
=
'relu'
)
class
ResNet
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
,
layers
=
50
,
class_dim
=
1000
):
self
.
layers
=
layers
supported_layers
=
[
50
,
101
,
152
]
assert
layers
in
supported_layers
,
\
"supported layers are {} but input layer is {}"
.
format
(
supported_layers
,
layers
)
if
layers
==
50
:
depth
=
[
3
,
4
,
6
,
3
]
elif
layers
==
101
:
depth
=
[
3
,
4
,
23
,
3
]
elif
layers
==
152
:
depth
=
[
3
,
8
,
36
,
3
]
num_filters
=
[
64
,
128
,
256
,
512
]
self
.
conv
=
ConvBNLayer
(
num_filters
=
64
,
filter_size
=
7
,
stride
=
2
,
act
=
'relu'
)
self
.
pool2d_max
=
Pool2D
(
pool_size
=
3
,
pool_stride
=
2
,
pool_padding
=
1
,
pool_type
=
'max'
)
self
.
bottleneck_block_list
=
[]
for
block
in
range
(
len
(
depth
)):
shortcut
=
True
for
i
in
range
(
depth
[
block
]):
bottleneck_block
=
BottleneckBlock
(
num_filters
=
num_filters
[
block
],
stride
=
2
if
i
==
0
and
block
!=
0
else
1
,
shortcut
=
shortcut
)
self
.
bottleneck_block_list
.
append
(
bottleneck_block
)
shortcut
=
False
self
.
pool2d_avg
=
Pool2D
(
pool_size
=
7
,
pool_type
=
'avg'
,
global_pooling
=
True
)
import
math
stdv
=
1.0
/
math
.
sqrt
(
2048
*
1.0
)
self
.
out
=
FC
(
size
=
class_dim
,
act
=
'softmax'
,
param_attr
=
fluid
.
param_attr
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Uniform
(
-
stdv
,
stdv
)))
def
forward
(
self
,
inputs
):
y
=
self
.
conv
(
inputs
)
y
=
self
.
pool2d_max
(
y
)
for
bottleneck_block
in
self
.
bottleneck_block_list
:
y
=
bottleneck_block
(
y
)
y
=
self
.
pool2d_avg
(
y
)
y
=
self
.
out
()
return
y
class
TestImperativeResnet
(
unittest
.
TestCase
):
def
test_resnet_cpu_float32
(
self
):
seed
=
90
with
fluid
.
imperative
.
guard
():
fluid
.
default_startup_program
().
random_seed
=
seed
fluid
.
default_main_program
().
random_seed
=
seed
resnet
=
ResNet
()
optimizer
=
optimizer_setting
(
train_parameters
)
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
flowers
.
train
(),
batch_size
=
256
)
dy_param_init_value
=
{}
for
batch_id
,
data
in
enumerate
(
train_reader
()):
if
batch_id
>=
2
:
break
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
)
label
=
to_variable
(
y_data
)
label
.
_stop_gradient
=
True
cost
=
resnet
(
img
)
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
out
,
label
=
label
)
avg_loss
=
fluid
.
layers
.
mean
(
x
=
cost
)
dy_out
=
avg_loss
.
_numpy
()
if
batch_id
==
0
:
for
param
in
fluid
.
default_main_program
().
global_block
(
).
all_parameters
():
dy_param_init_value
[
param
.
name
]
=
param
.
_numpy
()
avg_loss
.
_backward
()
optimizer
.
minimize
(
avg_loss
)
dy_param_value
=
{}
for
param
in
fluid
.
default_main_program
().
global_block
(
).
all_parameters
():
dy_param_value
[
param
.
name
]
=
param
.
_numpy
()
# with new_program_scope():
# fluid.default_startup_program().random_seed = seed
# fluid.default_main_program().random_seed = seed
# exe = fluid.Executor(fluid.CPUPlace())
# # mnist = Conv2D(1, 20, 5)
# mnist = MNIST()
# sgd = SGDOptimizer(learning_rate=1e-3)
# train_reader = paddle.batch(
# paddle.dataset.mnist.train(), batch_size=128)
# 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.reduce_mean(cost)
# sgd.minimize(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():
# static_param_name_list.append(param.name)
# out = exe.run(fluid.default_startup_program(),
# fetch_list=static_param_name_list)
# for i in range(len(static_param_name_list)):
# static_param_init_value[static_param_name_list[i]] = out[i]
# for batch_id, data in enumerate(train_reader()):
# if batch_id >= 2:
# break
# 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])
# fetch_list = [loss.name]
# fetch_list.extend(static_param_name_list)
# out = exe.run(fluid.default_main_program(),
# feed={"pixel": x_data,
# "label": y_data},
# fetch_list=fetch_list)
# static_param_value = {}
# static_out = out[0]
# for i in range(1, len(out)):
# 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()))
# for key, value in six.iteritems(static_param_value):
# self.assertTrue(np.allclose(value.all(), dy_param_value[key].all()))
if
__name__
==
'__main__'
:
unittest
.
main
()
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