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体验新版 GitCode,发现更多精彩内容 >>
提交
03fd5f6b
编写于
7月 01, 2017
作者:
Z
Zhaolong Xing
提交者:
GitHub
7月 01, 2017
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差异文件
Merge pull request #2686 from qingqing01/row_conv_fix
Fix bug for flowers dataset and row_conv.
上级
ea641da5
09256815
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
37 addition
and
13 deletion
+37
-13
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+2
-2
python/paddle/trainer_config_helpers/tests/configs/protostr/test_row_conv.protostr
...fig_helpers/tests/configs/protostr/test_row_conv.protostr
+1
-1
python/paddle/v2/dataset/flowers.py
python/paddle/v2/dataset/flowers.py
+12
-6
python/paddle/v2/image.py
python/paddle/v2/image.py
+22
-4
未找到文件。
python/paddle/trainer/config_parser.py
浏览文件 @
03fd5f6b
...
...
@@ -2082,10 +2082,10 @@ class MaxOutLayer(LayerBase):
class
RowConvLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
context_length
,
**
xargs
):
super
(
RowConvLayer
,
self
).
__init__
(
name
,
'
maxout
'
,
0
,
inputs
=
inputs
,
**
xargs
)
name
,
'
row_conv
'
,
0
,
inputs
=
inputs
,
**
xargs
)
config_assert
(
len
(
self
.
inputs
)
==
1
,
'
TransLayer must have one and only one input
'
)
'
row convolution layer must have one and only one input.
'
)
input_layer
=
self
.
get_input_layer
(
0
)
row_conv_conf
=
self
.
config
.
inputs
[
0
].
row_conv_conf
row_conv_conf
.
context_length
=
context_length
...
...
python/paddle/trainer_config_helpers/tests/configs/protostr/test_row_conv.protostr
浏览文件 @
03fd5f6b
...
...
@@ -7,7 +7,7 @@ layers {
}
layers {
name: "__row_conv_layer_0__"
type: "
maxout
"
type: "
row_conv
"
size: 2560
active_type: "relu"
inputs {
...
...
python/paddle/v2/dataset/flowers.py
浏览文件 @
03fd5f6b
...
...
@@ -30,6 +30,7 @@ http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback08.{pdf,ps.gz}.
"""
import
cPickle
import
itertools
import
functools
from
common
import
download
import
tarfile
import
scipy.io
as
scio
...
...
@@ -54,21 +55,26 @@ TEST_FLAG = 'trnid'
VALID_FLAG
=
'valid'
def
default_mapper
(
sample
):
def
default_mapper
(
is_train
,
sample
):
'''
map image bytes data to type needed by model input layer
'''
img
,
label
=
sample
img
=
load_image_bytes
(
img
)
img
=
simple_transform
(
img
,
256
,
224
,
True
)
img
=
simple_transform
(
img
,
256
,
224
,
is_train
,
mean
=
[
103.94
,
116.78
,
123.68
])
return
img
.
flatten
().
astype
(
'float32'
),
label
train_mapper
=
functools
.
partial
(
default_mapper
,
True
)
test_mapper
=
functools
.
partial
(
default_mapper
,
False
)
def
reader_creator
(
data_file
,
label_file
,
setid_file
,
dataset_name
,
mapper
=
default_mapper
,
mapper
,
buffered_size
=
1024
,
use_xmap
=
True
):
'''
...
...
@@ -118,7 +124,7 @@ def reader_creator(data_file,
return
map_readers
(
mapper
,
reader
)
def
train
(
mapper
=
default
_mapper
,
buffered_size
=
1024
,
use_xmap
=
True
):
def
train
(
mapper
=
train
_mapper
,
buffered_size
=
1024
,
use_xmap
=
True
):
'''
Create flowers training set reader.
It returns a reader, each sample in the reader is
...
...
@@ -141,7 +147,7 @@ def train(mapper=default_mapper, buffered_size=1024, use_xmap=True):
buffered_size
,
use_xmap
)
def
test
(
mapper
=
defaul
t_mapper
,
buffered_size
=
1024
,
use_xmap
=
True
):
def
test
(
mapper
=
tes
t_mapper
,
buffered_size
=
1024
,
use_xmap
=
True
):
'''
Create flowers test set reader.
It returns a reader, each sample in the reader is
...
...
@@ -164,7 +170,7 @@ def test(mapper=default_mapper, buffered_size=1024, use_xmap=True):
buffered_size
,
use_xmap
)
def
valid
(
mapper
=
defaul
t_mapper
,
buffered_size
=
1024
,
use_xmap
=
True
):
def
valid
(
mapper
=
tes
t_mapper
,
buffered_size
=
1024
,
use_xmap
=
True
):
'''
Create flowers validation set reader.
It returns a reader, each sample in the reader is
...
...
python/paddle/v2/image.py
浏览文件 @
03fd5f6b
...
...
@@ -262,7 +262,12 @@ def left_right_flip(im):
return
im
[:,
::
-
1
,
:]
def
simple_transform
(
im
,
resize_size
,
crop_size
,
is_train
,
is_color
=
True
):
def
simple_transform
(
im
,
resize_size
,
crop_size
,
is_train
,
is_color
=
True
,
mean
=
None
):
"""
Simply data argumentation for training. These operations include
resizing, croping and flipping.
...
...
@@ -288,7 +293,19 @@ def simple_transform(im, resize_size, crop_size, is_train, is_color=True):
im
=
left_right_flip
(
im
)
else
:
im
=
center_crop
(
im
,
crop_size
)
im
=
to_chw
(
im
)
if
len
(
im
.
shape
)
==
3
:
im
=
to_chw
(
im
)
im
=
im
.
astype
(
'float32'
)
if
mean
is
not
None
:
mean
=
np
.
array
(
mean
,
dtype
=
np
.
float32
)
# mean value, may be one value per channel
if
mean
.
ndim
==
1
:
mean
=
mean
[:,
np
.
newaxis
,
np
.
newaxis
]
else
:
# elementwise mean
assert
len
(
mean
.
shape
)
==
len
(
im
)
im
-=
mean
return
im
...
...
@@ -297,7 +314,8 @@ def load_and_transform(filename,
resize_size
,
crop_size
,
is_train
,
is_color
=
True
):
is_color
=
True
,
mean
=
None
):
"""
Load image from the input file `filename` and transform image for
data argumentation. Please refer to the `simple_transform` interface
...
...
@@ -318,5 +336,5 @@ def load_and_transform(filename,
:type is_train: bool
"""
im
=
load_image
(
filename
)
im
=
simple_transform
(
im
,
resize_size
,
crop_size
,
is_train
,
is_color
)
im
=
simple_transform
(
im
,
resize_size
,
crop_size
,
is_train
,
is_color
,
mean
)
return
im
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