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体验新版 GitCode,发现更多精彩内容 >>
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16788c58
编写于
9月 14, 2018
作者:
Q
qingqing01
提交者:
GitHub
9月 14, 2018
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from
paddle.fluid.param_attr
import
ParamAttr
from
paddle.fluid.initializer
import
Constant
from
paddle.fluid.regularizer
import
L2Decay
import
paddle.fluid
as
fluid
def
conv_bn_layer
(
input
,
ch_out
,
filter_size
,
stride
,
padding
,
act
=
'relu'
,
name
=
None
):
conv1
=
fluid
.
layers
.
conv2d
(
input
=
input
,
num_filters
=
ch_out
,
filter_size
=
filter_size
,
stride
=
stride
,
padding
=
padding
,
act
=
None
,
param_attr
=
ParamAttr
(
name
=
name
+
"_weights"
),
bias_attr
=
ParamAttr
(
name
=
name
+
"_biases"
),
name
=
name
+
'.conv2d.output.1'
)
if
name
==
"conv1"
:
bn_name
=
"bn_"
+
name
else
:
bn_name
=
"bn"
+
name
[
3
:]
return
fluid
.
layers
.
batch_norm
(
input
=
conv1
,
act
=
act
,
name
=
bn_name
+
'.output.1'
,
param_attr
=
ParamAttr
(
name
=
bn_name
+
'_scale'
),
bias_attr
=
ParamAttr
(
bn_name
+
'_offset'
),
moving_mean_name
=
bn_name
+
'_mean'
,
moving_variance_name
=
bn_name
+
'_variance'
,
is_test
=
True
)
def
conv_affine_layer
(
input
,
ch_out
,
filter_size
,
stride
,
padding
,
act
=
'relu'
,
name
=
None
):
conv
=
fluid
.
layers
.
conv2d
(
input
=
input
,
num_filters
=
ch_out
,
filter_size
=
filter_size
,
stride
=
stride
,
padding
=
padding
,
act
=
None
,
param_attr
=
ParamAttr
(
name
=
name
+
"_weights"
),
bias_attr
=
False
,
name
=
name
+
'.conv2d.output.1'
)
if
name
==
"conv1"
:
bn_name
=
"bn_"
+
name
else
:
bn_name
=
"bn"
+
name
[
3
:]
scale
=
fluid
.
layers
.
create_parameter
(
shape
=
[
conv
.
shape
[
1
]],
dtype
=
conv
.
dtype
,
attr
=
ParamAttr
(
name
=
bn_name
+
'_scale'
,
learning_rate
=
0.
),
default_initializer
=
Constant
(
1.
))
scale
.
stop_gradient
=
True
bias
=
fluid
.
layers
.
create_parameter
(
shape
=
[
conv
.
shape
[
1
]],
dtype
=
conv
.
dtype
,
attr
=
ParamAttr
(
bn_name
+
'_offset'
,
learning_rate
=
0.
),
default_initializer
=
Constant
(
0.
))
bias
.
stop_gradient
=
True
elt_mul
=
fluid
.
layers
.
elementwise_mul
(
x
=
conv
,
y
=
scale
,
axis
=
1
)
out
=
fluid
.
layers
.
elementwise_add
(
x
=
elt_mul
,
y
=
bias
,
axis
=
1
)
if
act
==
'relu'
:
out
=
fluid
.
layers
.
relu
(
x
=
out
)
return
out
def
shortcut
(
input
,
ch_out
,
stride
,
name
):
ch_in
=
input
.
shape
[
1
]
# if args.data_format == 'NCHW' else input.shape[-1]
if
ch_in
!=
ch_out
:
return
conv_affine_layer
(
input
,
ch_out
,
1
,
stride
,
0
,
None
,
name
=
name
)
else
:
return
input
def
basicblock
(
input
,
ch_out
,
stride
,
name
):
short
=
shortcut
(
input
,
ch_out
,
stride
,
name
=
name
)
conv1
=
conv_affine_layer
(
input
,
ch_out
,
3
,
stride
,
1
,
name
=
name
)
conv2
=
conv_affine_layer
(
conv1
,
ch_out
,
3
,
1
,
1
,
act
=
None
,
name
=
name
)
return
fluid
.
layers
.
elementwise_add
(
x
=
short
,
y
=
conv2
,
act
=
'relu'
,
name
=
name
)
def
bottleneck
(
input
,
ch_out
,
stride
,
name
):
short
=
shortcut
(
input
,
ch_out
*
4
,
stride
,
name
=
name
+
"_branch1"
)
conv1
=
conv_affine_layer
(
input
,
ch_out
,
1
,
stride
,
0
,
name
=
name
+
"_branch2a"
)
conv2
=
conv_affine_layer
(
conv1
,
ch_out
,
3
,
1
,
1
,
name
=
name
+
"_branch2b"
)
conv3
=
conv_affine_layer
(
conv2
,
ch_out
*
4
,
1
,
1
,
0
,
act
=
None
,
name
=
name
+
"_branch2c"
)
return
fluid
.
layers
.
elementwise_add
(
x
=
short
,
y
=
conv3
,
act
=
'relu'
,
name
=
name
+
".add.output.5"
)
def
layer_warp
(
block_func
,
input
,
ch_out
,
count
,
stride
,
name
):
res_out
=
block_func
(
input
,
ch_out
,
stride
,
name
=
name
+
"a"
)
for
i
in
range
(
1
,
count
):
res_out
=
block_func
(
res_out
,
ch_out
,
1
,
name
=
name
+
chr
(
ord
(
"a"
)
+
i
))
return
res_out
def
FasterRcnn
(
input
,
depth
,
anchor_sizes
,
variance
,
aspect_ratios
,
gt_box
,
is_crowd
,
gt_label
,
im_info
,
class_nums
,
use_random
):
cfg
=
{
18
:
([
2
,
2
,
2
,
1
],
basicblock
),
34
:
([
3
,
4
,
6
,
3
],
basicblock
),
50
:
([
3
,
4
,
6
,
3
],
bottleneck
),
101
:
([
3
,
4
,
23
,
3
],
bottleneck
),
152
:
([
3
,
8
,
36
,
3
],
bottleneck
)
}
stages
,
block_func
=
cfg
[
depth
]
conv1
=
conv_affine_layer
(
input
,
ch_out
=
64
,
filter_size
=
7
,
stride
=
2
,
padding
=
3
,
name
=
"conv1"
)
pool1
=
fluid
.
layers
.
pool2d
(
input
=
conv1
,
pool_type
=
'max'
,
pool_size
=
3
,
pool_stride
=
2
,
pool_padding
=
1
,
name
=
"pool1.max_pool.output.1"
)
res2
=
layer_warp
(
block_func
,
pool1
,
64
,
stages
[
0
],
1
,
name
=
"res2"
)
res2
.
stop_gradient
=
True
res3
=
layer_warp
(
block_func
,
res2
,
128
,
stages
[
1
],
2
,
name
=
"res3"
)
res4
=
layer_warp
(
block_func
,
res3
,
256
,
stages
[
2
],
2
,
name
=
"res4"
)
#========= RPN ============
# rpn_conv/3*3
rpn_conv
=
fluid
.
layers
.
conv2d
(
input
=
res4
,
num_filters
=
1024
,
filter_size
=
3
,
stride
=
1
,
padding
=
1
,
act
=
'relu'
,
name
=
'conv_rpn'
,
param_attr
=
ParamAttr
(
name
=
"conv_rpn_w"
),
bias_attr
=
ParamAttr
(
name
=
"conv_rpn_b"
,
learning_rate
=
2.
,
regularizer
=
L2Decay
(
0.
)))
anchor
,
var
=
fluid
.
layers
.
anchor_generator
(
input
=
rpn_conv
,
anchor_sizes
=
anchor_sizes
,
aspect_ratios
=
aspect_ratios
,
variance
=
variance
,
stride
=
[
16.0
,
16.0
])
num_anchor
=
anchor
.
shape
[
2
]
rpn_cls_score
=
fluid
.
layers
.
conv2d
(
rpn_conv
,
num_filters
=
num_anchor
,
filter_size
=
1
,
stride
=
1
,
padding
=
0
,
act
=
None
,
name
=
'rpn_cls_score'
,
param_attr
=
ParamAttr
(
name
=
"rpn_cls_logits_w"
),
bias_attr
=
ParamAttr
(
name
=
"rpn_cls_logits_b"
,
learning_rate
=
2.
,
regularizer
=
L2Decay
(
0.
)))
rpn_bbox_pred
=
fluid
.
layers
.
conv2d
(
rpn_conv
,
num_filters
=
4
*
num_anchor
,
filter_size
=
1
,
stride
=
1
,
padding
=
0
,
act
=
None
,
name
=
'rpn_bbox_pred'
,
param_attr
=
ParamAttr
(
name
=
"rpn_bbox_pred_w"
),
bias_attr
=
ParamAttr
(
name
=
"rpn_bbox_pred_b"
,
learning_rate
=
2.
,
regularizer
=
L2Decay
(
0.
)))
rpn_cls_score_prob
=
fluid
.
layers
.
sigmoid
(
rpn_cls_score
,
name
=
'rpn_cls_score_prob'
)
rpn_rois
,
rpn_roi_probs
=
fluid
.
layers
.
generate_proposals
(
scores
=
rpn_cls_score_prob
,
bbox_deltas
=
rpn_bbox_pred
,
im_info
=
im_info
,
anchors
=
anchor
,
variances
=
var
,
pre_nms_top_n
=
12000
,
post_nms_top_n
=
2000
,
nms_thresh
=
0.7
,
min_size
=
0.0
,
eta
=
1.0
)
rois
,
labels_int32
,
bbox_targets
,
bbox_inside_weights
,
\
bbox_outside_weights
=
fluid
.
layers
.
generate_proposal_labels
(
rpn_rois
=
rpn_rois
,
gt_classes
=
gt_label
,
is_crowd
=
is_crowd
,
gt_boxes
=
gt_box
,
im_info
=
im_info
,
batch_size_per_im
=
512
,
fg_fraction
=
0.25
,
fg_thresh
=
0.5
,
bg_thresh_hi
=
0.5
,
bg_thresh_lo
=
0.0
,
bbox_reg_weights
=
[
0.1
,
0.1
,
0.2
,
0.2
],
class_nums
=
class_nums
,
use_random
=
use_random
)
rois
.
stop_gradient
=
True
labels_int32
.
stop_gradient
=
True
bbox_targets
.
stop_gradient
=
True
bbox_inside_weights
.
stop_gradient
=
True
bbox_outside_weights
.
stop_gradient
=
True
pool5
=
fluid
.
layers
.
roi_pool
(
input
=
res4
,
rois
=
rois
,
pooled_height
=
14
,
pooled_width
=
14
,
spatial_scale
=
0.0625
)
res5_2_sum
=
layer_warp
(
block_func
,
pool5
,
512
,
stages
[
3
],
2
,
name
=
"res5"
)
res5_pool
=
fluid
.
layers
.
pool2d
(
res5_2_sum
,
pool_type
=
'avg'
,
pool_size
=
7
,
name
=
'res5_pool'
)
cls_score
=
fluid
.
layers
.
fc
(
input
=
res5_pool
,
size
=
class_nums
,
act
=
None
,
name
=
'cls_score'
,
param_attr
=
ParamAttr
(
name
=
'cls_score_w'
),
bias_attr
=
ParamAttr
(
name
=
'cls_score_b'
,
learning_rate
=
2.
,
regularizer
=
L2Decay
(
0.
)))
bbox_pred
=
fluid
.
layers
.
fc
(
input
=
res5_pool
,
size
=
4
*
class_nums
,
act
=
None
,
name
=
'bbox_pred'
,
param_attr
=
ParamAttr
(
name
=
'bbox_pred_w'
),
bias_attr
=
ParamAttr
(
name
=
'bbox_pred_b'
,
learning_rate
=
2.
,
regularizer
=
L2Decay
(
0.
)))
return
rpn_cls_score
,
rpn_bbox_pred
,
anchor
,
var
,
cls_score
,
\
bbox_pred
,
bbox_targets
,
bbox_inside_weights
,
\
bbox_outside_weights
,
rois
,
labels_int32
def
RPNloss
(
rpn_cls_prob
,
rpn_bbox_pred
,
anchor
,
var
,
gt_box
,
is_crowd
,
im_info
,
use_random
):
rpn_cls_score_reshape
=
fluid
.
layers
.
transpose
(
rpn_cls_prob
,
perm
=
[
0
,
2
,
3
,
1
])
rpn_bbox_pred_reshape
=
fluid
.
layers
.
transpose
(
rpn_bbox_pred
,
perm
=
[
0
,
2
,
3
,
1
])
anchor_reshape
=
fluid
.
layers
.
reshape
(
anchor
,
shape
=
(
-
1
,
4
))
var_reshape
=
fluid
.
layers
.
reshape
(
var
,
shape
=
(
-
1
,
4
))
rpn_cls_score_reshape
=
fluid
.
layers
.
reshape
(
x
=
rpn_cls_score_reshape
,
shape
=
(
0
,
-
1
,
1
))
rpn_bbox_pred_reshape
=
fluid
.
layers
.
reshape
(
x
=
rpn_bbox_pred_reshape
,
shape
=
(
0
,
-
1
,
4
))
score_pred
,
loc_pred
,
score_target
,
loc_target
=
fluid
.
layers
.
rpn_target_assign
(
bbox_pred
=
rpn_bbox_pred_reshape
,
cls_logits
=
rpn_cls_score_reshape
,
anchor_box
=
anchor_reshape
,
anchor_var
=
var_reshape
,
gt_boxes
=
gt_box
,
is_crowd
=
is_crowd
,
im_info
=
im_info
,
rpn_batch_size_per_im
=
256
,
rpn_straddle_thresh
=
0.0
,
rpn_fg_fraction
=
0.5
,
rpn_positive_overlap
=
0.7
,
rpn_negative_overlap
=
0.3
,
use_random
=
use_random
)
score_target
=
fluid
.
layers
.
cast
(
x
=
score_target
,
dtype
=
'float32'
)
rpn_cls_loss
=
fluid
.
layers
.
sigmoid_cross_entropy_with_logits
(
x
=
score_pred
,
label
=
score_target
)
rpn_cls_loss
=
fluid
.
layers
.
reduce_mean
(
rpn_cls_loss
,
name
=
'loss_rpn_cls'
)
rpn_reg_loss
=
fluid
.
layers
.
smooth_l1
(
x
=
loc_pred
,
y
=
loc_target
,
sigma
=
3.0
)
rpn_reg_loss
=
fluid
.
layers
.
reduce_sum
(
rpn_reg_loss
,
name
=
'loss_rpn_bbox'
)
score_shape
=
fluid
.
layers
.
shape
(
score_target
)
score_shape
=
fluid
.
layers
.
cast
(
x
=
score_shape
,
dtype
=
'float32'
)
norm
=
fluid
.
layers
.
reduce_prod
(
score_shape
)
norm
.
stop_gradient
=
True
rpn_reg_loss
=
rpn_reg_loss
/
norm
return
rpn_cls_loss
,
rpn_reg_loss
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