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5d7e7bc0
编写于
2月 27, 2017
作者:
L
Luo Tao
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
add test_layer for v2
上级
dabdc690
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
115 addition
and
133 deletion
+115
-133
python/paddle/v2/layer.py
python/paddle/v2/layer.py
+0
-126
python/paddle/v2/tests/test_layer.py
python/paddle/v2/tests/test_layer.py
+115
-7
未找到文件。
python/paddle/v2/layer.py
浏览文件 @
5d7e7bc0
...
...
@@ -74,9 +74,6 @@ from paddle.trainer_config_helpers.config_parser_utils import \
from
paddle.trainer_config_helpers.default_decorators
import
wrap_name_default
import
data_type
import
activation
import
attr
import
pooling
__all__
=
[
'parse_network'
,
'data'
,
'fc'
,
'conv_shift'
,
'img_conv'
,
'img_pool'
,
'spp'
,
...
...
@@ -277,126 +274,3 @@ layer_list = [
]
for
l
in
layer_list
:
globals
()[
l
[
0
]]
=
__convert_to_v2__
(
l
[
1
],
l
[
2
])
if
__name__
==
'__main__'
:
pixel
=
data
(
name
=
'pixel'
,
type
=
data_type
.
dense_vector
(
128
))
label
=
data
(
name
=
'label'
,
type
=
data_type
.
integer_value
(
10
))
weight
=
data
(
name
=
'weight'
,
type
=
data_type
.
dense_vector
(
10
))
word
=
data
(
name
=
'word'
,
type
=
data_type
.
integer_value
(
12
))
score
=
data
(
name
=
'score'
,
type
=
data_type
.
dense_vector
(
1
))
hidden
=
fc
(
input
=
pixel
,
size
=
100
,
act
=
activation
.
Sigmoid
(),
param_attr
=
attr
.
Param
(
name
=
'hidden'
))
inference
=
fc
(
input
=
hidden
,
size
=
10
,
act
=
activation
.
Softmax
())
print
parse_network
(
inference
)
# test conv layers
conv1
=
conv_shift
(
a
=
pixel
,
b
=
score
)
conv2
=
img_conv
(
input
=
pixel
,
filter_size
=
1
,
filter_size_y
=
1
,
num_channels
=
8
,
num_filters
=
16
,
act
=
activation
.
Linear
())
print
parse_network
(
conv1
,
conv2
)
# test image pooling layers
maxpool
=
img_pool
(
input
=
conv2
,
pool_size
=
2
,
num_channels
=
16
,
padding
=
1
,
pool_type
=
pooling
.
Max
())
spp
=
spp
(
input
=
conv2
,
pyramid_height
=
2
,
num_channels
=
16
,
pool_type
=
pooling
.
Max
())
maxout
=
maxout
(
input
=
conv2
,
num_channels
=
16
,
groups
=
4
)
print
parse_network
(
maxpool
,
spp
,
maxout
)
# test norm layers
norm1
=
img_cmrnorm
(
input
=
maxpool
,
size
=
5
)
norm2
=
batch_norm
(
input
=
maxpool
)
norm3
=
sum_to_one_norm
(
input
=
maxpool
)
print
parse_network
(
norm1
,
norm2
,
norm3
)
# test recurrent layers
recurrent
=
recurrent
(
input
=
word
)
lstm
=
lstmemory
(
input
=
word
)
gru
=
grumemory
(
input
=
word
)
print
parse_network
(
recurrent
,
lstm
,
gru
)
# test aggregate layers
pool
=
pool
(
input
=
pixel
,
pooling_type
=
pooling
.
Avg
(),
agg_level
=
AggregateLevel
.
EACH_SEQUENCE
)
last_seq
=
last_seq
(
input
=
pixel
)
first_seq
=
first_seq
(
input
=
pixel
)
concat
=
concat
(
input
=
[
last_seq
,
first_seq
])
seq_concat
=
seq_concat
(
a
=
last_seq
,
b
=
first_seq
)
print
parse_network
(
pool
,
last_seq
,
first_seq
,
concat
,
seq_concat
)
# test reshaping layers
block_expand
=
block_expand
(
input
=
maxout
,
num_channels
=
4
,
stride_x
=
1
,
block_x
=
1
)
expand
=
expand
(
input
=
last_seq
,
expand_as
=
pixel
,
expand_level
=
ExpandLevel
.
FROM_TIMESTEP
)
repeat
=
repeat
(
input
=
last_seq
,
num_repeats
=
4
)
reshape
=
seq_reshape
(
input
=
last_seq
,
reshape_size
=
4
)
rotate
=
rotate
(
input
=
pixel
,
height
=
16
,
width
=
49
)
print
parse_network
(
block_expand
,
expand
,
repeat
,
reshape
,
rotate
)
# test math layers
addto
=
addto
(
input
=
[
last_seq
,
first_seq
])
linear_comb
=
linear_comb
(
weights
=
weight
,
vectors
=
hidden
,
size
=
10
)
interpolation
=
interpolation
(
input
=
[
hidden
,
hidden
],
weight
=
score
)
bilinear
=
bilinear_interp
(
input
=
conv2
,
out_size_x
=
4
,
out_size_y
=
4
)
power
=
power
(
input
=
conv1
,
weight
=
score
)
scaling
=
scaling
(
input
=
conv1
,
weight
=
score
)
slope
=
slope_intercept
(
input
=
conv1
)
tensor
=
tensor
(
a
=
last_seq
,
b
=
first_seq
,
size
=
1000
)
cos_sim
=
cos_sim
(
a
=
last_seq
,
b
=
first_seq
)
trans
=
trans
(
input
=
tensor
)
print
parse_network
(
addto
,
linear_comb
,
interpolation
,
bilinear
,
power
,
scaling
,
slope
,
tensor
,
cos_sim
,
trans
)
# test sampling layers
maxid
=
max_id
(
input
=
inference
)
sampling_id
=
sampling_id
(
input
=
inference
)
print
parse_network
(
maxid
,
sampling_id
)
# test slicing and joining layers
pad
=
pad
(
input
=
maxpool
,
pad_c
=
[
2
,
3
],
pad_h
=
[
1
,
2
],
pad_w
=
[
3
,
1
])
print
parse_network
(
pad
)
# test cost layers
cost1
=
classification_cost
(
input
=
inference
,
label
=
label
)
cost2
=
classification_cost
(
input
=
inference
,
label
=
label
,
weight
=
weight
)
cost3
=
cross_entropy_cost
(
input
=
inference
,
label
=
label
)
cost4
=
cross_entropy_with_selfnorm_cost
(
input
=
inference
,
label
=
label
)
cost5
=
regression_cost
(
input
=
inference
,
label
=
label
)
cost6
=
regression_cost
(
input
=
inference
,
label
=
label
,
weight
=
weight
)
cost7
=
multi_binary_label_cross_entropy_cost
(
input
=
inference
,
label
=
label
)
cost8
=
rank_cost
(
left
=
score
,
right
=
score
,
label
=
score
)
cost9
=
lambda_cost
(
input
=
inference
,
score
=
score
)
cost10
=
sum_cost
(
input
=
inference
)
cost11
=
huber_cost
(
input
=
score
,
label
=
label
)
print
parse_network
(
cost3
,
cost4
)
print
parse_network
(
cost5
,
cost6
)
print
parse_network
(
cost7
,
cost8
,
cost9
,
cost10
,
cost11
)
crf
=
crf
(
input
=
inference
,
label
=
label
)
crf_decoding
=
crf_decoding
(
input
=
inference
,
size
=
3
)
ctc
=
ctc
(
input
=
inference
,
label
=
label
)
warp_ctc
=
warp_ctc
(
input
=
pixel
,
label
=
label
)
nce
=
nce
(
input
=
inference
,
label
=
label
,
num_classes
=
3
)
hsigmoid
=
hsigmoid
(
input
=
inference
,
label
=
label
,
num_classes
=
3
)
print
parse_network
(
crf
,
crf_decoding
,
ctc
,
warp_ctc
,
nce
,
hsigmoid
)
# test check layers
eos
=
eos
(
input
=
maxid
,
eos_id
=
5
)
print
parse_network
(
eos
)
python/paddle/v2/tests/test_layer.py
浏览文件 @
5d7e7bc0
...
...
@@ -19,18 +19,106 @@ import paddle.v2.activation as activation
import
paddle.v2.attr
as
attr
import
paddle.v2.data_type
as
data_type
import
paddle.v2.layer
as
layer
import
paddle.v2.pooling
as
pooling
from
paddle.trainer_config_helpers.config_parser_utils
import
\
parse_network_config
as
parse_network
pixel
=
layer
.
data
(
name
=
'pixel'
,
type
=
data_type
.
dense_vector
(
784
))
pixel
=
layer
.
data
(
name
=
'pixel'
,
type
=
data_type
.
dense_vector
(
128
))
label
=
layer
.
data
(
name
=
'label'
,
type
=
data_type
.
integer_value
(
10
))
weight
=
layer
.
data
(
name
=
'weight'
,
type
=
data_type
.
dense_vector
(
10
))
score
=
layer
.
data
(
name
=
'score'
,
type
=
data_type
.
dense_vector
(
1
))
hidden
=
layer
.
fc
(
input
=
pixel
,
size
=
100
,
act
=
activation
.
Sigmoid
(),
param_attr
=
attr
.
Param
(
name
=
'hidden'
))
inference
=
layer
.
fc
(
input
=
hidden
,
size
=
10
,
act
=
activation
.
Softmax
())
conv
=
layer
.
img_conv
(
input
=
pixel
,
filter_size
=
1
,
filter_size_y
=
1
,
num_channels
=
8
,
num_filters
=
16
,
act
=
activation
.
Linear
())
class
ImageLayerTest
(
unittest
.
TestCase
):
def
test_conv_layer
(
self
):
conv_shift
=
layer
.
conv_shift
(
a
=
pixel
,
b
=
score
)
print
layer
.
parse_network
(
conv
,
conv_shift
)
def
test_pooling_layer
(
self
):
maxpool
=
layer
.
img_pool
(
input
=
conv
,
pool_size
=
2
,
num_channels
=
16
,
padding
=
1
,
pool_type
=
pooling
.
Max
())
spp
=
layer
.
spp
(
input
=
conv
,
pyramid_height
=
2
,
num_channels
=
16
,
pool_type
=
pooling
.
Max
())
maxout
=
layer
.
maxout
(
input
=
conv
,
num_channels
=
16
,
groups
=
4
)
print
layer
.
parse_network
(
maxpool
,
spp
,
maxout
)
def
test_norm_layer
(
self
):
norm1
=
layer
.
img_cmrnorm
(
input
=
conv
,
size
=
5
)
norm2
=
layer
.
batch_norm
(
input
=
conv
)
norm3
=
layer
.
sum_to_one_norm
(
input
=
conv
)
print
layer
.
parse_network
(
norm1
,
norm2
,
norm3
)
class
AggregateLayerTest
(
unittest
.
TestCase
):
def
test_aggregate_layer
(
self
):
pool
=
layer
.
pool
(
input
=
pixel
,
pooling_type
=
pooling
.
Avg
(),
agg_level
=
layer
.
AggregateLevel
.
EACH_SEQUENCE
)
last_seq
=
layer
.
last_seq
(
input
=
pixel
)
first_seq
=
layer
.
first_seq
(
input
=
pixel
)
concat
=
layer
.
concat
(
input
=
[
last_seq
,
first_seq
])
seq_concat
=
layer
.
seq_concat
(
a
=
last_seq
,
b
=
first_seq
)
print
layer
.
parse_network
(
pool
,
last_seq
,
first_seq
,
concat
,
seq_concat
)
class
MathLayerTest
(
unittest
.
TestCase
):
def
test_math_layer
(
self
):
addto
=
layer
.
addto
(
input
=
[
pixel
,
pixel
])
linear_comb
=
layer
.
linear_comb
(
weights
=
weight
,
vectors
=
hidden
,
size
=
10
)
interpolation
=
layer
.
interpolation
(
input
=
[
hidden
,
hidden
],
weight
=
score
)
bilinear
=
layer
.
bilinear_interp
(
input
=
conv
,
out_size_x
=
4
,
out_size_y
=
4
)
power
=
layer
.
power
(
input
=
pixel
,
weight
=
score
)
scaling
=
layer
.
scaling
(
input
=
pixel
,
weight
=
score
)
slope
=
layer
.
slope_intercept
(
input
=
pixel
)
tensor
=
layer
.
tensor
(
a
=
pixel
,
b
=
pixel
,
size
=
1000
)
cos_sim
=
layer
.
cos_sim
(
a
=
pixel
,
b
=
pixel
)
trans
=
layer
.
trans
(
input
=
tensor
)
print
layer
.
parse_network
(
addto
,
linear_comb
,
interpolation
,
power
,
scaling
,
slope
,
tensor
,
cos_sim
,
trans
)
class
ReshapeLayerTest
(
unittest
.
TestCase
):
def
test_reshape_layer
(
self
):
block_expand
=
layer
.
block_expand
(
input
=
conv
,
num_channels
=
4
,
stride_x
=
1
,
block_x
=
1
)
expand
=
layer
.
expand
(
input
=
weight
,
expand_as
=
pixel
,
expand_level
=
layer
.
ExpandLevel
.
FROM_TIMESTEP
)
repeat
=
layer
.
repeat
(
input
=
pixel
,
num_repeats
=
4
)
reshape
=
layer
.
seq_reshape
(
input
=
pixel
,
reshape_size
=
4
)
rotate
=
layer
.
rotate
(
input
=
pixel
,
height
=
16
,
width
=
49
)
print
layer
.
parse_network
(
block_expand
,
expand
,
repeat
,
reshape
,
rotate
)
class
RecurrentLayerTest
(
unittest
.
TestCase
):
def
test_recurrent_layer
(
self
):
word
=
layer
.
data
(
name
=
'word'
,
type
=
data_type
.
integer_value
(
12
))
recurrent
=
layer
.
recurrent
(
input
=
word
)
lstm
=
layer
.
lstmemory
(
input
=
word
)
gru
=
layer
.
grumemory
(
input
=
word
)
print
layer
.
parse_network
(
recurrent
,
lstm
,
gru
)
class
CostLayerTest
(
unittest
.
TestCase
):
...
...
@@ -51,12 +139,32 @@ class CostLayerTest(unittest.TestCase):
cost10
=
layer
.
sum_cost
(
input
=
inference
)
cost11
=
layer
.
huber_cost
(
input
=
score
,
label
=
label
)
print
dir
(
layer
)
layer
.
parse_network
(
cost1
,
cost2
)
print
dir
(
layer
)
#print layer.parse_network(cost3, cost4)
#print layer.parse_network(cost5, cost6)
#print layer.parse_network(cost7, cost8, cost9, cost10, cost11)
print
layer
.
parse_network
(
cost1
,
cost2
)
print
layer
.
parse_network
(
cost3
,
cost4
)
print
layer
.
parse_network
(
cost5
,
cost6
)
print
layer
.
parse_network
(
cost7
,
cost8
,
cost9
,
cost10
,
cost11
)
crf
=
layer
.
crf
(
input
=
inference
,
label
=
label
)
crf_decoding
=
layer
.
crf_decoding
(
input
=
inference
,
size
=
3
)
ctc
=
layer
.
ctc
(
input
=
inference
,
label
=
label
)
warp_ctc
=
layer
.
warp_ctc
(
input
=
pixel
,
label
=
label
)
nce
=
layer
.
nce
(
input
=
inference
,
label
=
label
,
num_classes
=
3
)
hsigmoid
=
layer
.
hsigmoid
(
input
=
inference
,
label
=
label
,
num_classes
=
3
)
print
layer
.
parse_network
(
crf
,
crf_decoding
,
ctc
,
warp_ctc
,
nce
,
hsigmoid
)
class
OtherLayerTest
(
unittest
.
TestCase
):
def
test_sampling_layer
(
self
):
maxid
=
layer
.
max_id
(
input
=
inference
)
sampling_id
=
layer
.
sampling_id
(
input
=
inference
)
eos
=
layer
.
eos
(
input
=
maxid
,
eos_id
=
5
)
print
layer
.
parse_network
(
maxid
,
sampling_id
,
eos
)
def
test_slicing_joining_layer
(
self
):
pad
=
layer
.
pad
(
input
=
conv
,
pad_c
=
[
2
,
3
],
pad_h
=
[
1
,
2
],
pad_w
=
[
3
,
1
])
print
layer
.
parse_network
(
pad
)
if
__name__
==
'__main__'
:
...
...
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