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cdc700bb
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体验新版 GitCode,发现更多精彩内容 >>
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cdc700bb
编写于
10月 31, 2017
作者:
Q
Qiao Longfei
提交者:
GitHub
10月 31, 2017
浏览文件
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浏览文件
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电子邮件补丁
差异文件
add resnet (#5206)
* add resnet * optimize code
上级
a186b53d
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
152 addition
and
6 deletion
+152
-6
python/paddle/v2/framework/layers.py
python/paddle/v2/framework/layers.py
+3
-2
python/paddle/v2/framework/tests/test_image_classification_layer.py
...dle/v2/framework/tests/test_image_classification_layer.py
+23
-0
python/paddle/v2/framework/tests/test_image_classification_train.py
...dle/v2/framework/tests/test_image_classification_train.py
+126
-4
未找到文件。
python/paddle/v2/framework/layers.py
浏览文件 @
cdc700bb
...
@@ -5,7 +5,7 @@ import re
...
@@ -5,7 +5,7 @@ import re
__all__
=
[
__all__
=
[
'fc'
,
'data'
,
'cross_entropy'
,
'conv2d'
,
'pool2d'
,
'embedding'
,
'concat'
,
'fc'
,
'data'
,
'cross_entropy'
,
'conv2d'
,
'pool2d'
,
'embedding'
,
'concat'
,
'StaticRNN'
,
'cast'
'StaticRNN'
,
'cast'
,
'batch_norm'
]
]
...
@@ -150,7 +150,7 @@ def _create_op_func_(op_type):
...
@@ -150,7 +150,7 @@ def _create_op_func_(op_type):
outputs
[
name
]
=
[
helper
.
create_tmp_variable
(
dtype
=
dtype
)]
outputs
[
name
]
=
[
helper
.
create_tmp_variable
(
dtype
=
dtype
)]
helper
.
append_op
(
helper
.
append_op
(
type
=
op_type
,
inputs
=
inputs
,
outputs
=
outputs
,
attrs
=
kwargs
)
type
=
op_type
,
inputs
=
inputs
,
outputs
=
outputs
,
attrs
=
kwargs
)
return
out
return
helper
.
append_activation
(
out
)
func
.
__name__
=
op_type
func
.
__name__
=
op_type
globals
()[
op_type
]
=
func
globals
()[
op_type
]
=
func
...
@@ -160,6 +160,7 @@ def _create_op_func_(op_type):
...
@@ -160,6 +160,7 @@ def _create_op_func_(op_type):
_create_op_func_
(
'mean'
)
_create_op_func_
(
'mean'
)
_create_op_func_
(
'mul'
)
_create_op_func_
(
'mul'
)
_create_op_func_
(
'elementwise_add'
)
_create_op_func_
(
'dropout'
)
_create_op_func_
(
'dropout'
)
_create_op_func_
(
'reshape'
)
_create_op_func_
(
'reshape'
)
...
...
python/paddle/v2/framework/tests/test_image_classification_layer.py
浏览文件 @
cdc700bb
...
@@ -70,6 +70,29 @@ class TestLayer(unittest.TestCase):
...
@@ -70,6 +70,29 @@ class TestLayer(unittest.TestCase):
# print str(program)
# print str(program)
def
test_elementwise_add_with_act
(
self
):
program
=
Program
()
init_program
=
Program
()
image1
=
layers
.
data
(
name
=
'pixel1'
,
shape
=
[
3
,
48
,
48
],
data_type
=
'float32'
,
program
=
program
,
init_program
=
init_program
)
image2
=
layers
.
data
(
name
=
'pixel2'
,
shape
=
[
3
,
48
,
48
],
data_type
=
'float32'
,
program
=
program
,
init_program
=
init_program
)
out
=
layers
.
elementwise_add
(
x
=
image1
,
y
=
image2
,
act
=
'relu'
,
program
=
program
,
init_program
=
init_program
)
# print(program)
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
unittest
.
main
()
unittest
.
main
()
python/paddle/v2/framework/tests/test_image_classification_train.py
浏览文件 @
cdc700bb
...
@@ -10,6 +10,120 @@ from paddle.v2.framework.executor import Executor
...
@@ -10,6 +10,120 @@ from paddle.v2.framework.executor import Executor
import
numpy
as
np
import
numpy
as
np
def
resnet_cifar10
(
input
,
depth
=
32
,
program
=
None
,
init_program
=
None
):
def
conv_bn_layer
(
input
,
ch_out
,
filter_size
,
stride
,
padding
,
act
=
'relu'
,
program
=
None
,
init_program
=
None
):
tmp
=
layers
.
conv2d
(
input
=
input
,
filter_size
=
filter_size
,
num_filters
=
ch_out
,
stride
=
stride
,
padding
=
padding
,
act
=
None
,
bias_attr
=
False
,
program
=
program
,
init_program
=
init_program
)
return
layers
.
batch_norm
(
input
=
tmp
,
act
=
act
,
program
=
program
,
init_program
=
init_program
)
def
shortcut
(
input
,
ch_in
,
ch_out
,
stride
,
program
,
init_program
):
if
ch_in
!=
ch_out
:
return
conv_bn_layer
(
input
,
ch_out
,
1
,
stride
,
0
,
None
,
program
,
init_program
)
else
:
return
input
def
basicblock
(
input
,
ch_in
,
ch_out
,
stride
,
program
=
program
,
init_program
=
init_program
):
tmp
=
conv_bn_layer
(
input
,
ch_out
,
3
,
stride
,
1
,
program
=
program
,
init_program
=
init_program
)
tmp
=
conv_bn_layer
(
tmp
,
ch_out
,
3
,
1
,
1
,
act
=
None
,
program
=
program
,
init_program
=
init_program
)
short
=
shortcut
(
input
,
ch_in
,
ch_out
,
stride
,
program
,
init_program
)
return
layers
.
elementwise_add
(
x
=
tmp
,
y
=
short
,
act
=
'relu'
,
program
=
program
,
init_program
=
init_program
)
def
layer_warp
(
block_func
,
input
,
ch_in
,
ch_out
,
count
,
stride
,
program
,
init_program
):
tmp
=
block_func
(
input
,
ch_in
,
ch_out
,
stride
,
program
,
init_program
)
for
i
in
range
(
1
,
count
):
tmp
=
block_func
(
tmp
,
ch_out
,
ch_out
,
1
,
program
,
init_program
)
return
tmp
assert
(
depth
-
2
)
%
6
==
0
n
=
(
depth
-
2
)
/
6
conv1
=
conv_bn_layer
(
input
=
input
,
ch_out
=
16
,
filter_size
=
3
,
stride
=
1
,
padding
=
1
,
program
=
program
,
init_program
=
init_program
)
res1
=
layer_warp
(
basicblock
,
conv1
,
16
,
16
,
n
,
1
,
program
=
program
,
init_program
=
init_program
)
res2
=
layer_warp
(
basicblock
,
res1
,
16
,
32
,
n
,
2
,
program
=
program
,
init_program
=
init_program
)
res3
=
layer_warp
(
basicblock
,
res2
,
32
,
64
,
n
,
2
,
program
=
program
,
init_program
=
init_program
)
pool
=
layers
.
pool2d
(
input
=
res3
,
pool_size
=
8
,
pool_type
=
'avg'
,
pool_stride
=
1
,
program
=
program
,
init_program
=
init_program
)
return
pool
def
vgg16_bn_drop
(
input
,
program
,
init_program
):
def
vgg16_bn_drop
(
input
,
program
,
init_program
):
def
conv_block
(
input
,
def
conv_block
(
input
,
num_filter
,
num_filter
,
...
@@ -75,8 +189,16 @@ label = layers.data(
...
@@ -75,8 +189,16 @@ label = layers.data(
data_type
=
'int64'
,
data_type
=
'int64'
,
program
=
program
,
program
=
program
,
init_program
=
init_program
)
init_program
=
init_program
)
vgg_net
=
vgg16_bn_drop
(
images
,
program
,
init_program
)
predict
=
layers
.
fc
(
input
=
vgg_net
,
# Add neural network config
# option 1. resnet
net
=
resnet_cifar10
(
images
,
32
,
program
,
init_program
)
# option 2. vgg
# net = vgg16_bn_drop(images, program, init_program)
# print(program)
predict
=
layers
.
fc
(
input
=
net
,
size
=
classdim
,
size
=
classdim
,
act
=
'softmax'
,
act
=
'softmax'
,
program
=
program
,
program
=
program
,
...
@@ -123,8 +245,8 @@ for pass_id in range(PASS_NUM):
...
@@ -123,8 +245,8 @@ for pass_id in range(PASS_NUM):
fetch_list
=
[
avg_cost
])
fetch_list
=
[
avg_cost
])
loss
=
np
.
array
(
outs
[
0
])
loss
=
np
.
array
(
outs
[
0
])
#
print("pass_id:" + str(pass_id) + " batch_id:" + str(batch_id) +
print
(
"pass_id:"
+
str
(
pass_id
)
+
" batch_id:"
+
str
(
batch_id
)
+
#
" loss:" + str(loss))
" loss:"
+
str
(
loss
))
batch_id
=
batch_id
+
1
batch_id
=
batch_id
+
1
if
batch_id
>
1
:
if
batch_id
>
1
:
...
...
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