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8e89111f
编写于
9月 05, 2019
作者:
P
pengmian
浏览文件
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电子邮件补丁
差异文件
add pspnet
上级
52222565
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6
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Showing
6 changed file
with
174 addition
and
20 deletion
+174
-20
configs/pspnet.yaml
configs/pspnet.yaml
+51
-0
pdseg/data_aug.py
pdseg/data_aug.py
+2
-2
pdseg/models/backbone/resnet.py
pdseg/models/backbone/resnet.py
+28
-18
pdseg/models/model_builder.py
pdseg/models/model_builder.py
+1
-0
pdseg/models/modeling/pspnet.py
pdseg/models/modeling/pspnet.py
+86
-0
pdseg/utils/config.py
pdseg/utils/config.py
+6
-0
未找到文件。
configs/pspnet.yaml
0 → 100644
浏览文件 @
8e89111f
EVAL_CROP_SIZE
:
(2049, 1025)
# (width, height), for unpadding rangescaling and stepscaling
TRAIN_CROP_SIZE
:
(713, 713)
# (width, height), for unpadding rangescaling and stepscaling
AUG
:
AUG_METHOD
:
"
stepscaling"
# choice unpadding rangescaling and stepscaling
FIX_RESIZE_SIZE
:
(640, 640)
# (width, height), for unpadding
INF_RESIZE_VALUE
:
500
# for rangescaling
MAX_RESIZE_VALUE
:
600
# for rangescaling
MIN_RESIZE_VALUE
:
400
# for rangescaling
MAX_SCALE_FACTOR
:
2.0
# for stepscaling
MIN_SCALE_FACTOR
:
0.5
# for stepscaling
SCALE_STEP_SIZE
:
0.25
# for stepscaling
MIRROR
:
True
RICH_CROP
:
ENABLE
:
False
ASPECT_RATIO
:
0.33
BLUR
:
True
BLUR_RATIO
:
0.1
FLIP
:
True
FLIP_RATIO
:
0.2
MAX_ROTATION
:
15
MIN_AREA_RATIO
:
0.5
BRIGHTNESS_JITTER_RATIO
:
0.5
CONTRAST_JITTER_RATIO
:
0.5
SATURATION_JITTER_RATIO
:
0.5
BATCH_SIZE
:
4
DATASET
:
DATA_DIR
:
"
./dataset/cityscapes/"
IMAGE_TYPE
:
"
rgb"
# choice rgb or rgba
NUM_CLASSES
:
19
TEST_FILE_LIST
:
"
dataset/cityscapes/val.list"
TRAIN_FILE_LIST
:
"
dataset/cityscapes/train.list"
VAL_FILE_LIST
:
"
dataset/cityscapes/val.list"
IGNORE_INDEX
:
255
FREEZE
:
MODEL_FILENAME
:
"
model"
PARAMS_FILENAME
:
"
params"
MODEL
:
MODEL_NAME
:
"
pspnet"
DEFAULT_NORM_TYPE
:
"
bn"
TEST
:
TEST_MODEL
:
"
pretrained_model/pspnet50_ADE20K/"
#pspnet101_cityscapes
TRAIN
:
MODEL_SAVE_DIR
:
"
snapshots/cityscape_pspnet50/"
PRETRAINED_MODEL
:
u"pretrained_model/pspnet50_ADE20K/"
RESUME
:
False
SNAPSHOT_EPOCH
:
10
SOLVER
:
LR
:
0.001
LR_POLICY
:
"
poly"
OPTIMIZER
:
"
sgd"
NUM_EPOCHS
:
700
pdseg/data_aug.py
浏览文件 @
8e89111f
...
...
@@ -374,7 +374,7 @@ def rand_crop(crop_img, crop_seg, mode=ModelPhase.TRAIN):
Args:
crop_img(numpy.ndarray): 输入图像
crop_seg(numpy.ndarray): 标签图
mode(string): 模式, 默认训练模式,验证或预测
模式时crop尺寸需大于原始图片尺寸, 其他模式无限制
mode(string): 模式, 默认训练模式,验证或预测
、可视化模式时crop尺寸需大于原始图片尺寸
Returns:
裁剪后的图片和标签图
...
...
@@ -391,7 +391,7 @@ def rand_crop(crop_img, crop_seg, mode=ModelPhase.TRAIN):
crop_width
=
cfg
.
EVAL_CROP_SIZE
[
0
]
crop_height
=
cfg
.
EVAL_CROP_SIZE
[
1
]
if
ModelPhase
.
is_eval
(
mode
)
or
ModelPhase
.
is_predict
(
mode
):
if
not
ModelPhase
.
is_train
(
mode
):
if
(
crop_height
<
img_height
or
crop_width
<
img_width
):
raise
Exception
(
"Crop size({},{}) must large than img size({},{}) when in EvalPhase."
...
...
pdseg/models/backbone/resnet.py
浏览文件 @
8e89111f
...
...
@@ -85,7 +85,7 @@ class ResNet():
depth
=
[
3
,
8
,
36
,
3
]
num_filters
=
[
64
,
128
,
256
,
512
]
if
self
.
stem
==
'icnet'
:
if
self
.
stem
==
'icnet'
or
self
.
stem
==
'pspnet'
:
conv
=
self
.
conv_bn_layer
(
input
=
input
,
num_filters
=
int
(
64
*
self
.
scale
),
...
...
@@ -133,20 +133,18 @@ class ResNet():
if
layers
>=
50
:
for
block
in
range
(
len
(
depth
)):
for
i
in
range
(
depth
[
block
]):
if
layers
in
[
101
,
152
]
and
block
==
2
:
if
i
==
0
:
conv_name
=
"res"
+
str
(
block
+
2
)
+
"a"
else
:
conv_name
=
"res"
+
str
(
block
+
2
)
+
"b"
+
str
(
i
)
else
:
conv_name
=
"conv"
+
str
(
block
+
2
)
+
'_'
+
str
(
1
+
i
)
dilation_rate
=
get_dilated_rate
(
dilation_dict
,
block
)
if
self
.
stem
==
'pspnet'
:
stride
=
2
if
i
==
0
and
block
==
1
else
1
else
:
stride
=
2
if
i
==
0
and
block
!=
0
and
dilation_rate
==
1
else
1
conv
=
self
.
bottleneck_block
(
input
=
conv
,
num_filters
=
int
(
num_filters
[
block
]
*
self
.
scale
),
stride
=
2
if
i
==
0
and
block
!=
0
and
dilation_rate
==
1
else
1
,
stride
=
stride
,
name
=
conv_name
,
dilation
=
dilation_rate
)
layer_count
+=
3
...
...
@@ -174,7 +172,7 @@ class ResNet():
else
:
for
block
in
range
(
len
(
depth
)):
for
i
in
range
(
depth
[
block
]):
conv_name
=
"
res
"
+
str
(
block
+
2
)
+
chr
(
97
+
i
)
conv_name
=
"
conv
"
+
str
(
block
+
2
)
+
chr
(
97
+
i
)
conv
=
self
.
basic_block
(
input
=
conv
,
num_filters
=
num_filters
[
block
],
...
...
@@ -215,6 +213,12 @@ class ResNet():
groups
=
1
,
act
=
None
,
name
=
None
):
if
self
.
stem
==
'pspnet'
:
bias_attr
=
ParamAttr
(
name
=
name
+
"/biases"
)
else
:
bias_attr
=
False
conv
=
fluid
.
layers
.
conv2d
(
input
=
input
,
num_filters
=
num_filters
,
...
...
@@ -225,7 +229,7 @@ class ResNet():
groups
=
groups
,
act
=
None
,
param_attr
=
ParamAttr
(
name
=
name
+
"/weights"
),
bias_attr
=
False
,
bias_attr
=
bias_attr
,
name
=
name
+
'.conv2d.output.1'
)
bn_name
=
name
+
'/BatchNorm/'
...
...
@@ -247,12 +251,17 @@ class ResNet():
return
input
def
bottleneck_block
(
self
,
input
,
num_filters
,
stride
,
name
,
dilation
=
1
):
if
self
.
stem
==
'pspnet'
and
self
.
layers
==
101
:
strides
=
[
1
,
stride
]
else
:
strides
=
[
stride
,
1
]
conv0
=
self
.
conv_bn_layer
(
input
=
input
,
num_filters
=
num_filters
,
filter_size
=
1
,
dilation
=
1
,
stride
=
stride
,
stride
=
stride
s
[
0
]
,
act
=
'relu'
,
name
=
name
+
"_branch2a"
)
if
dilation
>
1
:
...
...
@@ -262,6 +271,7 @@ class ResNet():
num_filters
=
num_filters
,
filter_size
=
3
,
dilation
=
dilation
,
stride
=
strides
[
1
],
act
=
'relu'
,
name
=
name
+
"_branch2b"
)
conv2
=
self
.
conv_bn_layer
(
...
...
pdseg/models/model_builder.py
浏览文件 @
8e89111f
...
...
@@ -73,6 +73,7 @@ def map_model_name(model_name):
"unet"
:
"unet.unet"
,
"deeplabv3p"
:
"deeplab.deeplabv3p"
,
"icnet"
:
"icnet.icnet"
,
"pspnet"
:
"pspnet.pspnet"
,
}
if
model_name
in
name_dict
.
keys
():
return
name_dict
[
model_name
]
...
...
pdseg/models/modeling/pspnet.py
0 → 100644
浏览文件 @
8e89111f
# -*- coding: utf-8 -*-
from
__future__
import
absolute_import
from
__future__
import
division
from
__future__
import
print_function
import
math
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid.param_attr
import
ParamAttr
from
models.libs.model_libs
import
scope
,
name_scope
from
models.libs.model_libs
import
avg_pool
,
conv
,
bn
from
models.backbone.resnet
import
ResNet
as
resnet_backbone
from
utils.config
import
cfg
def
get_logit_interp
(
input
,
num_classes
,
out_shape
,
name
=
"logit"
):
param_attr
=
fluid
.
ParamAttr
(
name
=
name
+
'weights'
,
regularizer
=
fluid
.
regularizer
.
L2DecayRegularizer
(
regularization_coeff
=
0.0
),
initializer
=
fluid
.
initializer
.
TruncatedNormal
(
loc
=
0.0
,
scale
=
0.01
))
with
scope
(
name
):
logit
=
conv
(
input
,
num_classes
,
filter_size
=
1
,
param_attr
=
param_attr
,
bias_attr
=
True
,
name
=
name
+
'.conv2d.output.1'
)
logit_interp
=
fluid
.
layers
.
resize_bilinear
(
logit
,
out_shape
=
out_shape
,
name
=
'logit_interp'
)
return
logit_interp
def
psp_module
(
input
,
out_features
):
cat_layers
=
[]
sizes
=
(
1
,
2
,
3
,
6
)
for
size
in
sizes
:
psp_name
=
"psp_conv"
+
str
(
size
)
with
scope
(
psp_name
):
pool
=
fluid
.
layers
.
adaptive_pool2d
(
input
,
pool_size
=
[
size
,
size
],
pool_type
=
'avg'
,
name
=
psp_name
+
'_adapool'
)
data
=
conv
(
pool
,
out_features
,
filter_size
=
1
,
bias_attr
=
True
,
name
=
psp_name
+
'.conv2d.output.1'
)
data_bn
=
bn
(
data
,
act
=
'relu'
)
interp
=
fluid
.
layers
.
resize_bilinear
(
data_bn
,
out_shape
=
input
.
shape
[
2
:],
name
=
psp_name
+
'_interp'
)
cat_layers
.
append
(
interp
)
cat_layers
=
[
input
]
+
cat_layers
[::
-
1
]
cat
=
fluid
.
layers
.
concat
(
cat_layers
,
axis
=
1
,
name
=
'psp_cat'
)
with
scope
(
"psp_conv_end"
):
data
=
conv
(
cat
,
out_features
,
filter_size
=
3
,
padding
=
1
,
bias_attr
=
True
,
name
=
'psp_conv_end.conv2d.output.1'
)
out
=
bn
(
data
,
act
=
'relu'
)
return
out
def
resnet
(
input
):
# PSPNET backbone: resnet, ĬÈresnet50
# end_points: resnetÖֹ²ã
scale
=
cfg
.
MODEL
.
PSPNET
.
DEPTH_MULTIPLIER
layers
=
cfg
.
MODEL
.
PSPNET
.
LAYERS
end_points
=
layers
-
1
dilation_dict
=
{
2
:
2
,
3
:
4
}
model
=
resnet_backbone
(
layers
,
scale
,
stem
=
'pspnet'
)
data
,
_
=
model
.
net
(
input
,
end_points
=
end_points
,
dilation_dict
=
dilation_dict
)
return
data
def
pspnet
(
input
,
num_classes
):
res
=
resnet
(
input
)
psp
=
psp_module
(
res
,
512
)
#dropout = fluid.layers.dropout(psp, dropout_prob=0.1, name="dropout")
logit
=
get_logit_interp
(
psp
,
num_classes
,
input
.
shape
[
2
:])
return
logit
pdseg/utils/config.py
浏览文件 @
8e89111f
...
...
@@ -196,6 +196,12 @@ cfg.MODEL.ICNET.DEPTH_MULTIPLIER = 0.5
# RESNET 层数 设置
cfg
.
MODEL
.
ICNET
.
LAYERS
=
50
########################## PSPNET模型配置 ######################################
# RESNET backbone scale 设置
cfg
.
MODEL
.
PSPNET
.
DEPTH_MULTIPLIER
=
1
# RESNET 层数 设置 50或101
cfg
.
MODEL
.
PSPNET
.
LAYERS
=
50
########################## 预测部署模型配置 ###################################
# 预测保存的模型名称
cfg
.
FREEZE
.
MODEL_FILENAME
=
'__model__'
...
...
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