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24f582fd
编写于
3月 30, 2021
作者:
F
Feng Ni
提交者:
GitHub
3月 30, 2021
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差异文件
[doc] add fcos ttfnet coments (#2456)
* add fcos ttfnet coment, update modelzoo * fix ttfnet coment
上级
3b02c71e
变更
12
显示空白变更内容
内联
并排
Showing
12 changed file
with
120 addition
and
80 deletion
+120
-80
configs/fcos/README.md
configs/fcos/README.md
+3
-3
configs/fcos/_base_/fcos_r50_fpn.yml
configs/fcos/_base_/fcos_r50_fpn.yml
+0
-1
configs/ttfnet/README.md
configs/ttfnet/README.md
+1
-1
ppdet/modeling/architectures/fcos.py
ppdet/modeling/architectures/fcos.py
+13
-3
ppdet/modeling/heads/fcos_head.py
ppdet/modeling/heads/fcos_head.py
+21
-9
ppdet/modeling/heads/ttf_head.py
ppdet/modeling/heads/ttf_head.py
+37
-12
ppdet/modeling/layers.py
ppdet/modeling/layers.py
+21
-13
ppdet/modeling/losses/ctfocal_loss.py
ppdet/modeling/losses/ctfocal_loss.py
+5
-5
ppdet/modeling/losses/fcos_loss.py
ppdet/modeling/losses/fcos_loss.py
+13
-26
ppdet/modeling/necks/ttf_fpn.py
ppdet/modeling/necks/ttf_fpn.py
+3
-6
ppdet/modeling/ops.py
ppdet/modeling/ops.py
+0
-1
ppdet/modeling/post_process.py
ppdet/modeling/post_process.py
+3
-0
未找到文件。
configs/fcos/README.md
浏览文件 @
24f582fd
...
@@ -12,9 +12,9 @@ FCOS (Fully Convolutional One-Stage Object Detection) is a fast anchor-free obje
...
@@ -12,9 +12,9 @@ FCOS (Fully Convolutional One-Stage Object Detection) is a fast anchor-free obje
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | 下载 | 配置文件 |
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | 下载 | 配置文件 |
| :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
| :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
| ResNet50-FPN | FCOS | 2 | 1x | ---- | 39.6 |
[
下载链接
](
https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_1x_coco.pdparams
)
|
[
配置文件
](
https://github.com/PaddlePaddle/PaddleDetection/tree/
master/dygraph
/configs/fcos/fcos_r50_fpn_1x_coco.yml
)
|
| ResNet50-FPN | FCOS | 2 | 1x | ---- | 39.6 |
[
下载链接
](
https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_1x_coco.pdparams
)
|
[
配置文件
](
https://github.com/PaddlePaddle/PaddleDetection/tree/
develop
/configs/fcos/fcos_r50_fpn_1x_coco.yml
)
|
| ResNet50-FPN | FCOS+DCN | 2 | 1x | ---- | 44.3 |
[
下载链接
](
https://paddledet.bj.bcebos.com/models/fcos_dcn_r50_fpn_1x_coco.pdparams
)
|
[
配置文件
](
https://github.com/PaddlePaddle/PaddleDetection/tree/
master/dygraph
/configs/fcos/fcos_dcn_r50_fpn_1x_coco.yml
)
|
| ResNet50-FPN | FCOS+DCN | 2 | 1x | ---- | 44.3 |
[
下载链接
](
https://paddledet.bj.bcebos.com/models/fcos_dcn_r50_fpn_1x_coco.pdparams
)
|
[
配置文件
](
https://github.com/PaddlePaddle/PaddleDetection/tree/
develop
/configs/fcos/fcos_dcn_r50_fpn_1x_coco.yml
)
|
| ResNet50-FPN | FCOS+multiscale_train | 2 | 2x | ---- | 41.8 |
[
下载链接
](
https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_multiscale_2x_coco.pdparams
)
|
[
配置文件
](
https://github.com/PaddlePaddle/PaddleDetection/tree/
master/dygraph
/configs/fcos/fcos_r50_fpn_multiscale_2x_coco.yml
)
|
| ResNet50-FPN | FCOS+multiscale_train | 2 | 2x | ---- | 41.8 |
[
下载链接
](
https://paddledet.bj.bcebos.com/models/fcos_r50_fpn_multiscale_2x_coco.pdparams
)
|
[
配置文件
](
https://github.com/PaddlePaddle/PaddleDetection/tree/
develop
/configs/fcos/fcos_r50_fpn_multiscale_2x_coco.yml
)
|
**Notes:**
**Notes:**
...
...
configs/fcos/_base_/fcos_r50_fpn.yml
浏览文件 @
24f582fd
...
@@ -47,7 +47,6 @@ FCOSPostProcess:
...
@@ -47,7 +47,6 @@ FCOSPostProcess:
decode
:
decode
:
name
:
FCOSBox
name
:
FCOSBox
num_classes
:
80
num_classes
:
80
batch_size
:
1
nms
:
nms
:
name
:
MultiClassNMS
name
:
MultiClassNMS
nms_top_k
:
1000
nms_top_k
:
1000
...
...
configs/ttfnet/README.md
浏览文件 @
24f582fd
...
@@ -13,7 +13,7 @@ TTFNet是一种用于实时目标检测且对训练时间友好的网络,对Ce
...
@@ -13,7 +13,7 @@ TTFNet是一种用于实时目标检测且对训练时间友好的网络,对Ce
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | 下载 | 配置文件 |
| 骨架网络 | 网络类型 | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | 下载 | 配置文件 |
| :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
| :-------------- | :------------- | :-----: | :-----: | :------------: | :-----: | :-----------------------------------------------------: | :-----: |
| DarkNet53 | TTFNet | 12 | 1x | ---- | 33.5 |
[
下载链接
](
https://paddledet.bj.bcebos.com/models/ttfnet_darknet53_1x_coco.pdparams
)
|
[
配置文件
](
https://github.com/PaddlePaddle/PaddleDetection/tree/
master/dygraph
/configs/ttfnet/ttfnet_darknet53_1x_coco.yml
)
|
| DarkNet53 | TTFNet | 12 | 1x | ---- | 33.5 |
[
下载链接
](
https://paddledet.bj.bcebos.com/models/ttfnet_darknet53_1x_coco.pdparams
)
|
[
配置文件
](
https://github.com/PaddlePaddle/PaddleDetection/tree/
develop
/configs/ttfnet/ttfnet_darknet53_1x_coco.yml
)
|
## Citations
## Citations
```
```
...
...
ppdet/modeling/architectures/fcos.py
浏览文件 @
24f582fd
...
@@ -25,6 +25,16 @@ __all__ = ['FCOS']
...
@@ -25,6 +25,16 @@ __all__ = ['FCOS']
@
register
@
register
class
FCOS
(
BaseArch
):
class
FCOS
(
BaseArch
):
"""
FCOS network, see https://arxiv.org/abs/1904.01355
Args:
backbone (object): backbone instance
neck (object): 'FPN' instance
fcos_head (object): 'FCOSHead' instance
post_process (object): 'FCOSPostProcess' instance
"""
__category__
=
'architecture'
__category__
=
'architecture'
__inject__
=
[
'fcos_post_process'
]
__inject__
=
[
'fcos_post_process'
]
...
@@ -70,7 +80,7 @@ class FCOS(BaseArch):
...
@@ -70,7 +80,7 @@ class FCOS(BaseArch):
loss
=
{}
loss
=
{}
tag_labels
,
tag_bboxes
,
tag_centerness
=
[],
[],
[]
tag_labels
,
tag_bboxes
,
tag_centerness
=
[],
[],
[]
for
i
in
range
(
len
(
self
.
fcos_head
.
fpn_stride
)):
for
i
in
range
(
len
(
self
.
fcos_head
.
fpn_stride
)):
#
reg_target, labels, scores
, centerness
#
labels, reg_target
, centerness
k_lbl
=
'labels{}'
.
format
(
i
)
k_lbl
=
'labels{}'
.
format
(
i
)
if
k_lbl
in
self
.
inputs
:
if
k_lbl
in
self
.
inputs
:
tag_labels
.
append
(
self
.
inputs
[
k_lbl
])
tag_labels
.
append
(
self
.
inputs
[
k_lbl
])
...
@@ -90,6 +100,6 @@ class FCOS(BaseArch):
...
@@ -90,6 +100,6 @@ class FCOS(BaseArch):
return
loss
return
loss
def
get_pred
(
self
):
def
get_pred
(
self
):
bbox
es
,
bbox_num
=
self
.
_forward
()
bbox
_pred
,
bbox_num
=
self
.
_forward
()
output
=
{
'bbox'
:
bbox
es
,
'bbox_num'
:
bbox_num
}
output
=
{
'bbox'
:
bbox
_pred
,
'bbox_num'
:
bbox_num
}
return
output
return
output
ppdet/modeling/heads/fcos_head.py
浏览文件 @
24f582fd
...
@@ -28,6 +28,10 @@ from ppdet.modeling.layers import ConvNormLayer
...
@@ -28,6 +28,10 @@ from ppdet.modeling.layers import ConvNormLayer
class
ScaleReg
(
nn
.
Layer
):
class
ScaleReg
(
nn
.
Layer
):
"""
Parameter for scaling the regression outputs.
"""
def
__init__
(
self
):
def
__init__
(
self
):
super
(
ScaleReg
,
self
).
__init__
()
super
(
ScaleReg
,
self
).
__init__
()
self
.
scale_reg
=
self
.
create_parameter
(
self
.
scale_reg
=
self
.
create_parameter
(
...
@@ -113,12 +117,13 @@ class FCOSHead(nn.Layer):
...
@@ -113,12 +117,13 @@ class FCOSHead(nn.Layer):
"""
"""
FCOSHead
FCOSHead
Args:
Args:
num_classes(int): Number of classes
fcos_feat (object): Instance of 'FCOSFeat'
fpn_stride(list): The stride of each FPN Layer
num_classes (int): Number of classes
prior_prob(float): Used to set the bias init for the class prediction layer
fpn_stride (list): The stride of each FPN Layer
fcos_loss(object): Instance of 'FCOSLoss'
prior_prob (float): Used to set the bias init for the class prediction layer
norm_reg_targets(bool): Normalization the regression target if true
fcos_loss (object): Instance of 'FCOSLoss'
centerness_on_reg(bool): The prediction of centerness on regression or clssification branch
norm_reg_targets (bool): Normalization the regression target if true
centerness_on_reg (bool): The prediction of centerness on regression or clssification branch
"""
"""
__inject__
=
[
'fcos_feat'
,
'fcos_loss'
]
__inject__
=
[
'fcos_feat'
,
'fcos_loss'
]
__shared__
=
[
'num_classes'
]
__shared__
=
[
'num_classes'
]
...
@@ -199,7 +204,15 @@ class FCOSHead(nn.Layer):
...
@@ -199,7 +204,15 @@ class FCOSHead(nn.Layer):
scale_reg
=
self
.
add_sublayer
(
feat_name
,
ScaleReg
())
scale_reg
=
self
.
add_sublayer
(
feat_name
,
ScaleReg
())
self
.
scales_regs
.
append
(
scale_reg
)
self
.
scales_regs
.
append
(
scale_reg
)
def
_compute_locatioins_by_level
(
self
,
fpn_stride
,
feature
):
def
_compute_locations_by_level
(
self
,
fpn_stride
,
feature
):
"""
Compute locations of anchor points of each FPN layer
Args:
fpn_stride (int): The stride of current FPN feature map
feature (Tensor): Tensor of current FPN feature map
Return:
Anchor points locations of current FPN feature map
"""
shape_fm
=
paddle
.
shape
(
feature
)
shape_fm
=
paddle
.
shape
(
feature
)
shape_fm
.
stop_gradient
=
True
shape_fm
.
stop_gradient
=
True
h
,
w
=
shape_fm
[
2
],
shape_fm
[
3
]
h
,
w
=
shape_fm
[
2
],
shape_fm
[
3
]
...
@@ -247,8 +260,7 @@ class FCOSHead(nn.Layer):
...
@@ -247,8 +260,7 @@ class FCOSHead(nn.Layer):
if
not
is_training
:
if
not
is_training
:
locations_list
=
[]
locations_list
=
[]
for
fpn_stride
,
feature
in
zip
(
self
.
fpn_stride
,
fpn_feats
):
for
fpn_stride
,
feature
in
zip
(
self
.
fpn_stride
,
fpn_feats
):
location
=
self
.
_compute_locatioins_by_level
(
fpn_stride
,
location
=
self
.
_compute_locations_by_level
(
fpn_stride
,
feature
)
feature
)
locations_list
.
append
(
location
)
locations_list
.
append
(
location
)
return
locations_list
,
cls_logits_list
,
bboxes_reg_list
,
centerness_list
return
locations_list
,
cls_logits_list
,
bboxes_reg_list
,
centerness_list
...
...
ppdet/modeling/heads/ttf_head.py
浏览文件 @
24f582fd
...
@@ -24,7 +24,15 @@ import numpy as np
...
@@ -24,7 +24,15 @@ import numpy as np
@
register
@
register
class
HMHead
(
nn
.
Layer
):
class
HMHead
(
nn
.
Layer
):
"""
Args:
ch_in (int): The channel number of input Tensor.
ch_out (int): The channel number of output Tensor.
num_classes (int): Number of classes.
conv_num (int): The convolution number of hm_feat.
Return:
Heatmap head output
"""
__shared__
=
[
'num_classes'
]
__shared__
=
[
'num_classes'
]
def
__init__
(
self
,
ch_in
,
ch_out
=
128
,
num_classes
=
80
,
conv_num
=
2
):
def
__init__
(
self
,
ch_in
,
ch_out
=
128
,
num_classes
=
80
,
conv_num
=
2
):
...
@@ -65,6 +73,15 @@ class HMHead(nn.Layer):
...
@@ -65,6 +73,15 @@ class HMHead(nn.Layer):
@
register
@
register
class
WHHead
(
nn
.
Layer
):
class
WHHead
(
nn
.
Layer
):
"""
Args:
ch_in (int): The channel number of input Tensor.
ch_out (int): The channel number of output Tensor.
conv_num (int): The convolution number of wh_feat.
Return:
Width & Height head output
"""
def
__init__
(
self
,
ch_in
,
ch_out
=
64
,
conv_num
=
2
):
def
__init__
(
self
,
ch_in
,
ch_out
=
64
,
conv_num
=
2
):
super
(
WHHead
,
self
).
__init__
()
super
(
WHHead
,
self
).
__init__
()
head_conv
=
nn
.
Sequential
()
head_conv
=
nn
.
Sequential
()
...
@@ -104,17 +121,22 @@ class TTFHead(nn.Layer):
...
@@ -104,17 +121,22 @@ class TTFHead(nn.Layer):
"""
"""
TTFHead
TTFHead
Args:
Args:
in_channels(int): the channel number of input to TTFHead.
in_channels (int): the channel number of input to TTFHead.
num_classes(int): the number of classes, 80 by default.
num_classes (int): the number of classes, 80 by default.
hm_head_planes(int): the channel number in wh head, 128 by default.
hm_head_planes (int): the channel number in heatmap head,
wh_head_planes(int): the channel number in wh head, 64 by default.
128 by default.
hm_head_conv_num(int): the number of convolution in wh head, 2 by default.
wh_head_planes (int): the channel number in width & height head,
wh_head_conv_num(int): the number of convolution in wh head, 2 by default.
64 by default.
hm_loss(object): Instance of 'CTFocalLoss'.
hm_head_conv_num (int): the number of convolution in heatmap head,
wh_loss(object): Instance of 'GIoULoss'.
2 by default.
wh_offset_base(flaot): the base offset of width and height, 16. by default.
wh_head_conv_num (int): the number of convolution in width & height
down_ratio(int): the actual down_ratio is calculated by base_down_ratio(default 16)
head, 2 by default.
and the number of upsample layers.
hm_loss (object): Instance of 'CTFocalLoss'.
wh_loss (object): Instance of 'GIoULoss'.
wh_offset_base (float): the base offset of width and height,
16.0 by default.
down_ratio (int): the actual down_ratio is calculated by base_down_ratio
(default 16) and the number of upsample layers.
"""
"""
__shared__
=
[
'num_classes'
,
'down_ratio'
]
__shared__
=
[
'num_classes'
,
'down_ratio'
]
...
@@ -154,6 +176,9 @@ class TTFHead(nn.Layer):
...
@@ -154,6 +176,9 @@ class TTFHead(nn.Layer):
return
hm
,
wh
return
hm
,
wh
def
filter_box_by_weight
(
self
,
pred
,
target
,
weight
):
def
filter_box_by_weight
(
self
,
pred
,
target
,
weight
):
"""
Filter out boxes where ttf_reg_weight is 0, only keep positive samples.
"""
index
=
paddle
.
nonzero
(
weight
>
0
)
index
=
paddle
.
nonzero
(
weight
>
0
)
index
.
stop_gradient
=
True
index
.
stop_gradient
=
True
weight
=
paddle
.
gather_nd
(
weight
,
index
)
weight
=
paddle
.
gather_nd
(
weight
,
index
)
...
...
ppdet/modeling/layers.py
浏览文件 @
24f582fd
...
@@ -616,20 +616,20 @@ class AnchorGrid(object):
...
@@ -616,20 +616,20 @@ class AnchorGrid(object):
@
register
@
register
@
serializable
@
serializable
class
FCOSBox
(
object
):
class
FCOSBox
(
object
):
__shared__
=
[
'num_classes'
,
'batch_size'
]
__shared__
=
[
'num_classes'
]
def
__init__
(
self
,
num_classes
=
80
,
batch_size
=
1
):
def
__init__
(
self
,
num_classes
=
80
):
super
(
FCOSBox
,
self
).
__init__
()
super
(
FCOSBox
,
self
).
__init__
()
self
.
num_classes
=
num_classes
self
.
num_classes
=
num_classes
self
.
batch_size
=
batch_size
def
_merge_hw
(
self
,
inputs
,
ch_type
=
"channel_first"
):
def
_merge_hw
(
self
,
inputs
,
ch_type
=
"channel_first"
):
"""
"""
Merge h and w of the feature map into one dimension.
Args:
Args:
inputs (
Variables): Feature map whose H and W will be merged into one dimension
inputs (
Tensor): Tensor of the input feature map
ch_type
(str): channel_first / channel_last
ch_type
(str): "channel_first" or "channel_last" style
Return:
Return:
new_shape (
Variables): The new shape after h and w merged into one dimension
new_shape (
Tensor): The new shape after h and w merged
"""
"""
shape_
=
paddle
.
shape
(
inputs
)
shape_
=
paddle
.
shape
(
inputs
)
bs
,
ch
,
hi
,
wi
=
shape_
[
0
],
shape_
[
1
],
shape_
[
2
],
shape_
[
3
]
bs
,
ch
,
hi
,
wi
=
shape_
[
0
],
shape_
[
1
],
shape_
[
2
],
shape_
[
3
]
...
@@ -647,16 +647,18 @@ class FCOSBox(object):
...
@@ -647,16 +647,18 @@ class FCOSBox(object):
def
_postprocessing_by_level
(
self
,
locations
,
box_cls
,
box_reg
,
box_ctn
,
def
_postprocessing_by_level
(
self
,
locations
,
box_cls
,
box_reg
,
box_ctn
,
scale_factor
):
scale_factor
):
"""
"""
Postprocess each layer of the output with corresponding locations.
Args:
Args:
locations (Variables): anchor points for current layer, [H*W, 2]
locations (Tensor): anchor points for current layer, [H*W, 2]
box_cls (Variables): categories prediction, [N, C, H, W], C is the number of classes
box_cls (Tensor): categories prediction, [N, C, H, W],
box_reg (Variables): bounding box prediction, [N, 4, H, W]
C is the number of classes
box_ctn (Variables): centerness prediction, [N, 1, H, W]
box_reg (Tensor): bounding box prediction, [N, 4, H, W]
scale_factor (Variables): [h_scale, w_scale] for input images
box_ctn (Tensor): centerness prediction, [N, 1, H, W]
scale_factor (Tensor): [h_scale, w_scale] for input images
Return:
Return:
box_cls_ch_last
(Variables
): score for each category, in [N, C, M]
box_cls_ch_last
(Tensor
): score for each category, in [N, C, M]
C is the number of classes and M is the number of anchor points
C is the number of classes and M is the number of anchor points
box_reg_decoding (
Variables
): decoded bounding box, in [N, M, 4]
box_reg_decoding (
Tensor
): decoded bounding box, in [N, M, 4]
last dimension is [x1, y1, x2, y2]
last dimension is [x1, y1, x2, y2]
"""
"""
act_shape_cls
=
self
.
_merge_hw
(
box_cls
)
act_shape_cls
=
self
.
_merge_hw
(
box_cls
)
...
@@ -712,12 +714,18 @@ class TTFBox(object):
...
@@ -712,12 +714,18 @@ class TTFBox(object):
self
.
down_ratio
=
down_ratio
self
.
down_ratio
=
down_ratio
def
_simple_nms
(
self
,
heat
,
kernel
=
3
):
def
_simple_nms
(
self
,
heat
,
kernel
=
3
):
"""
Use maxpool to filter the max score, get local peaks.
"""
pad
=
(
kernel
-
1
)
//
2
pad
=
(
kernel
-
1
)
//
2
hmax
=
F
.
max_pool2d
(
heat
,
kernel
,
stride
=
1
,
padding
=
pad
)
hmax
=
F
.
max_pool2d
(
heat
,
kernel
,
stride
=
1
,
padding
=
pad
)
keep
=
paddle
.
cast
(
hmax
==
heat
,
'float32'
)
keep
=
paddle
.
cast
(
hmax
==
heat
,
'float32'
)
return
heat
*
keep
return
heat
*
keep
def
_topk
(
self
,
scores
):
def
_topk
(
self
,
scores
):
"""
Select top k scores and decode to get xy coordinates.
"""
k
=
self
.
max_per_img
k
=
self
.
max_per_img
shape_fm
=
paddle
.
shape
(
scores
)
shape_fm
=
paddle
.
shape
(
scores
)
shape_fm
.
stop_gradient
=
True
shape_fm
.
stop_gradient
=
True
...
...
ppdet/modeling/losses/ctfocal_loss.py
浏览文件 @
24f582fd
...
@@ -27,7 +27,7 @@ __all__ = ['CTFocalLoss']
...
@@ -27,7 +27,7 @@ __all__ = ['CTFocalLoss']
@
serializable
@
serializable
class
CTFocalLoss
(
object
):
class
CTFocalLoss
(
object
):
"""
"""
CTFocalLoss
CTFocalLoss
: CornerNet & CenterNet Focal Loss
Args:
Args:
loss_weight (float): loss weight
loss_weight (float): loss weight
gamma (float): gamma parameter for Focal Loss
gamma (float): gamma parameter for Focal Loss
...
@@ -41,8 +41,8 @@ class CTFocalLoss(object):
...
@@ -41,8 +41,8 @@ class CTFocalLoss(object):
"""
"""
Calculate the loss
Calculate the loss
Args:
Args:
pred(Tensor): heatmap prediction
pred
(Tensor): heatmap prediction
target(Tensor): target for positive samples
target
(Tensor): target for positive samples
Return:
Return:
ct_focal_loss (Tensor): Focal Loss used in CornerNet & CenterNet.
ct_focal_loss (Tensor): Focal Loss used in CornerNet & CenterNet.
Note that the values in target are in [0, 1] since gaussian is
Note that the values in target are in [0, 1] since gaussian is
...
...
ppdet/modeling/losses/fcos_loss.py
浏览文件 @
24f582fd
...
@@ -20,8 +20,8 @@ import paddle
...
@@ -20,8 +20,8 @@ import paddle
import
paddle.nn
as
nn
import
paddle.nn
as
nn
import
paddle.nn.functional
as
F
import
paddle.nn.functional
as
F
from
ppdet.core.workspace
import
register
from
ppdet.core.workspace
import
register
from
ppdet.modeling
import
ops
INF
=
1e8
__all__
=
[
'FCOSLoss'
]
__all__
=
[
'FCOSLoss'
]
...
@@ -30,33 +30,20 @@ def flatten_tensor(inputs, channel_first=False):
...
@@ -30,33 +30,20 @@ def flatten_tensor(inputs, channel_first=False):
Flatten a Tensor
Flatten a Tensor
Args:
Args:
inputs (Tensor): 4-D Tensor with shape [N, C, H, W] or [N, H, W, C]
inputs (Tensor): 4-D Tensor with shape [N, C, H, W] or [N, H, W, C]
channel_first
(bool): if true the dimension order of
channel_first
(bool): If true the dimension order of Tensor is
Tensor is
[N, C, H, W], otherwise is [N, H, W, C]
[N, C, H, W], otherwise is [N, H, W, C]
Return:
Return:
in
put_channel_last (Tensor): The flattened Tensor in channel_last style
out
put_channel_last (Tensor): The flattened Tensor in channel_last style
"""
"""
if
channel_first
:
if
channel_first
:
input_channel_last
=
paddle
.
transpose
(
inputs
,
perm
=
[
0
,
2
,
3
,
1
])
input_channel_last
=
paddle
.
transpose
(
inputs
,
perm
=
[
0
,
2
,
3
,
1
])
else
:
else
:
input_channel_last
=
inputs
input_channel_last
=
inputs
output_channel_last
=
paddle
.
flatten
(
output_channel_last
=
paddle
.
flatten
(
input_channel_last
,
start_axis
=
0
,
stop_axis
=
2
)
# [N*H*W, C]
input_channel_last
,
start_axis
=
0
,
stop_axis
=
2
)
return
output_channel_last
return
output_channel_last
def
sigmoid_cross_entropy_with_logits_loss
(
inputs
,
label
,
ignore_index
=-
100
,
normalize
=
False
):
output
=
F
.
binary_cross_entropy_with_logits
(
inputs
,
label
,
reduction
=
'none'
)
mask_tensor
=
paddle
.
cast
(
label
!=
ignore_index
,
'float32'
)
output
=
paddle
.
multiply
(
output
,
mask_tensor
)
if
normalize
:
sum_valid_mask
=
paddle
.
sum
(
mask_tensor
)
output
=
output
/
sum_valid_mask
return
output
@
register
@
register
class
FCOSLoss
(
nn
.
Layer
):
class
FCOSLoss
(
nn
.
Layer
):
"""
"""
...
@@ -64,8 +51,8 @@ class FCOSLoss(nn.Layer):
...
@@ -64,8 +51,8 @@ class FCOSLoss(nn.Layer):
Args:
Args:
loss_alpha (float): alpha in focal loss
loss_alpha (float): alpha in focal loss
loss_gamma (float): gamma in focal loss
loss_gamma (float): gamma in focal loss
iou_loss_type(str): location loss type, IoU/GIoU/LINEAR_IoU
iou_loss_type
(str): location loss type, IoU/GIoU/LINEAR_IoU
reg_weights(float): weight for location loss
reg_weights
(float): weight for location loss
"""
"""
def
__init__
(
self
,
def
__init__
(
self
,
...
@@ -226,7 +213,7 @@ class FCOSLoss(nn.Layer):
...
@@ -226,7 +213,7 @@ class FCOSLoss(nn.Layer):
# 3. centerness: sigmoid_cross_entropy_with_logits_loss
# 3. centerness: sigmoid_cross_entropy_with_logits_loss
centerness_flatten
=
paddle
.
squeeze
(
centerness_flatten
,
axis
=-
1
)
centerness_flatten
=
paddle
.
squeeze
(
centerness_flatten
,
axis
=-
1
)
ctn_loss
=
sigmoid_cross_entropy_with_logits_los
s
(
centerness_flatten
,
ctn_loss
=
ops
.
sigmoid_cross_entropy_with_logit
s
(
centerness_flatten
,
tag_center_flatten
)
tag_center_flatten
)
ctn_loss
=
ctn_loss
*
mask_positive_float
/
num_positive_fp32
ctn_loss
=
ctn_loss
*
mask_positive_float
/
num_positive_fp32
...
...
ppdet/modeling/necks/ttf_fpn.py
浏览文件 @
24f582fd
...
@@ -17,7 +17,6 @@ import paddle.nn as nn
...
@@ -17,7 +17,6 @@ import paddle.nn as nn
import
paddle.nn.functional
as
F
import
paddle.nn.functional
as
F
from
paddle
import
ParamAttr
from
paddle
import
ParamAttr
from
paddle.nn.initializer
import
Constant
,
Uniform
,
Normal
from
paddle.nn.initializer
import
Constant
,
Uniform
,
Normal
from
paddle.nn
import
Conv2D
,
ReLU
,
Sequential
from
paddle
import
ParamAttr
from
paddle
import
ParamAttr
from
ppdet.core.workspace
import
register
,
serializable
from
ppdet.core.workspace
import
register
,
serializable
from
paddle.regularizer
import
L2Decay
from
paddle.regularizer
import
L2Decay
...
@@ -28,8 +27,6 @@ from ..shape_spec import ShapeSpec
...
@@ -28,8 +27,6 @@ from ..shape_spec import ShapeSpec
__all__
=
[
'TTFFPN'
]
__all__
=
[
'TTFFPN'
]
__all__
=
[
'TTFFPN'
]
class
Upsample
(
nn
.
Layer
):
class
Upsample
(
nn
.
Layer
):
def
__init__
(
self
,
ch_in
,
ch_out
,
name
=
None
):
def
__init__
(
self
,
ch_in
,
ch_out
,
name
=
None
):
...
@@ -63,7 +60,7 @@ class Upsample(nn.Layer):
...
@@ -63,7 +60,7 @@ class Upsample(nn.Layer):
class
ShortCut
(
nn
.
Layer
):
class
ShortCut
(
nn
.
Layer
):
def
__init__
(
self
,
layer_num
,
ch_out
,
name
=
None
):
def
__init__
(
self
,
layer_num
,
ch_out
,
name
=
None
):
super
(
ShortCut
,
self
).
__init__
()
super
(
ShortCut
,
self
).
__init__
()
shortcut_conv
=
Sequential
()
shortcut_conv
=
nn
.
Sequential
()
ch_in
=
ch_out
*
2
ch_in
=
ch_out
*
2
for
i
in
range
(
layer_num
):
for
i
in
range
(
layer_num
):
fan_out
=
3
*
3
*
ch_out
fan_out
=
3
*
3
*
ch_out
...
@@ -72,7 +69,7 @@ class ShortCut(nn.Layer):
...
@@ -72,7 +69,7 @@ class ShortCut(nn.Layer):
shortcut_name
=
name
+
'.conv.{}'
.
format
(
i
)
shortcut_name
=
name
+
'.conv.{}'
.
format
(
i
)
shortcut_conv
.
add_sublayer
(
shortcut_conv
.
add_sublayer
(
shortcut_name
,
shortcut_name
,
Conv2D
(
nn
.
Conv2D
(
in_channels
=
in_channels
,
in_channels
=
in_channels
,
out_channels
=
ch_out
,
out_channels
=
ch_out
,
kernel_size
=
3
,
kernel_size
=
3
,
...
@@ -81,7 +78,7 @@ class ShortCut(nn.Layer):
...
@@ -81,7 +78,7 @@ class ShortCut(nn.Layer):
bias_attr
=
ParamAttr
(
bias_attr
=
ParamAttr
(
learning_rate
=
2.
,
regularizer
=
L2Decay
(
0.
))))
learning_rate
=
2.
,
regularizer
=
L2Decay
(
0.
))))
if
i
<
layer_num
-
1
:
if
i
<
layer_num
-
1
:
shortcut_conv
.
add_sublayer
(
shortcut_name
+
'.act'
,
ReLU
())
shortcut_conv
.
add_sublayer
(
shortcut_name
+
'.act'
,
nn
.
ReLU
())
self
.
shortcut
=
self
.
add_sublayer
(
'short'
,
shortcut_conv
)
self
.
shortcut
=
self
.
add_sublayer
(
'short'
,
shortcut_conv
)
def
forward
(
self
,
feat
):
def
forward
(
self
,
feat
):
...
...
ppdet/modeling/ops.py
浏览文件 @
24f582fd
...
@@ -1558,7 +1558,6 @@ def sigmoid_cross_entropy_with_logits(input,
...
@@ -1558,7 +1558,6 @@ def sigmoid_cross_entropy_with_logits(input,
output
=
F
.
binary_cross_entropy_with_logits
(
input
,
label
,
reduction
=
'none'
)
output
=
F
.
binary_cross_entropy_with_logits
(
input
,
label
,
reduction
=
'none'
)
mask_tensor
=
paddle
.
cast
(
label
!=
ignore_index
,
'float32'
)
mask_tensor
=
paddle
.
cast
(
label
!=
ignore_index
,
'float32'
)
output
=
paddle
.
multiply
(
output
,
mask_tensor
)
output
=
paddle
.
multiply
(
output
,
mask_tensor
)
output
=
paddle
.
reshape
(
output
,
shape
=
[
output
.
shape
[
0
],
-
1
])
if
normalize
:
if
normalize
:
sum_valid_mask
=
paddle
.
sum
(
mask_tensor
)
sum_valid_mask
=
paddle
.
sum
(
mask_tensor
)
output
=
output
/
sum_valid_mask
output
=
output
/
sum_valid_mask
...
...
ppdet/modeling/post_process.py
浏览文件 @
24f582fd
...
@@ -181,6 +181,9 @@ class FCOSPostProcess(object):
...
@@ -181,6 +181,9 @@ class FCOSPostProcess(object):
self
.
nms
=
nms
self
.
nms
=
nms
def
__call__
(
self
,
fcos_head_outs
,
scale_factor
):
def
__call__
(
self
,
fcos_head_outs
,
scale_factor
):
"""
Decode the bbox and do NMS in FCOS.
"""
locations
,
cls_logits
,
bboxes_reg
,
centerness
=
fcos_head_outs
locations
,
cls_logits
,
bboxes_reg
,
centerness
=
fcos_head_outs
bboxes
,
score
=
self
.
decode
(
locations
,
cls_logits
,
bboxes_reg
,
bboxes
,
score
=
self
.
decode
(
locations
,
cls_logits
,
bboxes_reg
,
centerness
,
scale_factor
)
centerness
,
scale_factor
)
...
...
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