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fba30199
编写于
11月 21, 2019
作者:
W
wangguanzhong
提交者:
GitHub
11月 21, 2019
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电子邮件补丁
差异文件
change fluid.layers.data to fluid.data (#3966)
上级
c73b6dbd
变更
3
隐藏空白更改
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并排
Showing
3 changed file
with
31 addition
and
29 deletion
+31
-29
PaddleCV/PaddleDetection/docs/EXPORT_MODEL.md
PaddleCV/PaddleDetection/docs/EXPORT_MODEL.md
+4
-2
PaddleCV/PaddleDetection/ppdet/data/data_feed.py
PaddleCV/PaddleDetection/ppdet/data/data_feed.py
+12
-12
PaddleCV/PaddleDetection/ppdet/modeling/model_input.py
PaddleCV/PaddleDetection/ppdet/modeling/model_input.py
+15
-15
未找到文件。
PaddleCV/PaddleDetection/docs/EXPORT_MODEL.md
浏览文件 @
fba30199
...
...
@@ -14,14 +14,14 @@
使用
[
训练/评估/推断
](
GETTING_STARTED_cn.md
)
中训练得到的模型进行试用,脚本如下
```
bash
# 导出FasterRCNN模型
, 模型中data层默认的shape为3x800x1333
# 导出FasterRCNN模型
python tools/export_model.py
-c
configs/faster_rcnn_r50_1x.yml
\
--output_dir
=
./inference_model
\
-o
weights
=
output/faster_rcnn_r50_1x/model_final
\
```
预测模型会导出到
`inference_model/faster_rcnn_r50_1x`
目录下,模型名和参数名分别为
`__model__`
和
`__params__`
。
-
预测模型会导出到
`inference_model/faster_rcnn_r50_1x`
目录下,模型名和参数名分别为
`__model__`
和
`__params__`
。
## 设置导出模型的输入大小
...
...
@@ -46,3 +46,5 @@ python tools/export_model.py -c configs/ssd/ssd_mobilenet_v1_voc.yml \
-o
weights
=
https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_voc.tar
\
SSDTestFeed.image_shape
=[
3,300,300]
```
-
保存FPN系列模型时,需要保证上下采样维度一致,因此image_shape须设置为32的倍数
PaddleCV/PaddleDetection/ppdet/data/data_feed.py
浏览文件 @
fba30199
...
...
@@ -452,7 +452,7 @@ class FasterRCNNTrainFeed(DataFeed):
'image'
,
'im_info'
,
'im_id'
,
'gt_box'
,
'gt_label'
,
'is_crowd'
],
image_shape
=
[
3
,
800
,
1333
],
image_shape
=
[
None
,
3
,
None
,
None
],
sample_transforms
=
[
DecodeImage
(
to_rgb
=
True
),
RandomFlipImage
(
prob
=
0.5
),
...
...
@@ -504,7 +504,7 @@ class FasterRCNNEvalFeed(DataFeed):
COCO_VAL_IMAGE_DIR
).
__dict__
,
fields
=
[
'image'
,
'im_info'
,
'im_id'
,
'im_shape'
,
'gt_box'
,
'gt_label'
,
'is_difficult'
],
image_shape
=
[
3
,
800
,
1333
],
image_shape
=
[
None
,
3
,
None
,
None
],
sample_transforms
=
[
DecodeImage
(
to_rgb
=
True
),
NormalizeImage
(
mean
=
[
0.485
,
0.456
,
0.406
],
...
...
@@ -551,7 +551,7 @@ class FasterRCNNTestFeed(DataFeed):
dataset
=
SimpleDataSet
(
COCO_VAL_ANNOTATION
,
COCO_VAL_IMAGE_DIR
).
__dict__
,
fields
=
[
'image'
,
'im_info'
,
'im_id'
,
'im_shape'
],
image_shape
=
[
3
,
800
,
1333
],
image_shape
=
[
None
,
3
,
None
,
None
],
sample_transforms
=
[
DecodeImage
(
to_rgb
=
True
),
NormalizeImage
(
mean
=
[
0.485
,
0.456
,
0.406
],
...
...
@@ -598,7 +598,7 @@ class MaskRCNNTrainFeed(DataFeed):
'image'
,
'im_info'
,
'im_id'
,
'gt_box'
,
'gt_label'
,
'is_crowd'
,
'gt_mask'
],
image_shape
=
[
3
,
800
,
1333
],
image_shape
=
[
None
,
3
,
None
,
None
],
sample_transforms
=
[
DecodeImage
(
to_rgb
=
True
),
RandomFlipImage
(
prob
=
0.5
,
is_mask_flip
=
True
),
...
...
@@ -644,7 +644,7 @@ class MaskRCNNEvalFeed(DataFeed):
dataset
=
CocoDataSet
(
COCO_VAL_ANNOTATION
,
COCO_VAL_IMAGE_DIR
).
__dict__
,
fields
=
[
'image'
,
'im_info'
,
'im_id'
,
'im_shape'
],
image_shape
=
[
3
,
800
,
1333
],
image_shape
=
[
None
,
3
,
None
,
None
],
sample_transforms
=
[
DecodeImage
(
to_rgb
=
True
),
NormalizeImage
(
mean
=
[
0.485
,
0.456
,
0.406
],
...
...
@@ -696,7 +696,7 @@ class MaskRCNNTestFeed(DataFeed):
dataset
=
SimpleDataSet
(
COCO_VAL_ANNOTATION
,
COCO_VAL_IMAGE_DIR
).
__dict__
,
fields
=
[
'image'
,
'im_info'
,
'im_id'
,
'im_shape'
],
image_shape
=
[
3
,
800
,
1333
],
image_shape
=
[
None
,
3
,
None
,
None
],
sample_transforms
=
[
DecodeImage
(
to_rgb
=
True
),
NormalizeImage
(
...
...
@@ -740,7 +740,7 @@ class SSDTrainFeed(DataFeed):
def
__init__
(
self
,
dataset
=
VocDataSet
().
__dict__
,
fields
=
[
'image'
,
'gt_box'
,
'gt_label'
],
image_shape
=
[
3
,
300
,
300
],
image_shape
=
[
None
,
3
,
300
,
300
],
sample_transforms
=
[
DecodeImage
(
to_rgb
=
True
,
with_mixup
=
False
),
NormalizeBox
(),
...
...
@@ -799,7 +799,7 @@ class SSDEvalFeed(DataFeed):
dataset
=
VocDataSet
(
VOC_VAL_ANNOTATION
).
__dict__
,
fields
=
[
'image'
,
'im_shape'
,
'im_id'
,
'gt_box'
,
'gt_label'
,
'is_difficult'
],
image_shape
=
[
3
,
300
,
300
],
image_shape
=
[
None
,
3
,
300
,
300
],
sample_transforms
=
[
DecodeImage
(
to_rgb
=
True
,
with_mixup
=
False
),
NormalizeBox
(),
...
...
@@ -844,7 +844,7 @@ class SSDTestFeed(DataFeed):
def
__init__
(
self
,
dataset
=
SimpleDataSet
(
VOC_VAL_ANNOTATION
).
__dict__
,
fields
=
[
'image'
,
'im_id'
,
'im_shape'
],
image_shape
=
[
3
,
300
,
300
],
image_shape
=
[
None
,
3
,
300
,
300
],
sample_transforms
=
[
DecodeImage
(
to_rgb
=
True
),
ResizeImage
(
target_size
=
300
,
use_cv2
=
False
,
interp
=
1
),
...
...
@@ -890,7 +890,7 @@ class YoloTrainFeed(DataFeed):
def
__init__
(
self
,
dataset
=
CocoDataSet
().
__dict__
,
fields
=
[
'image'
,
'gt_box'
,
'gt_label'
,
'gt_score'
],
image_shape
=
[
3
,
608
,
608
],
image_shape
=
[
None
,
3
,
608
,
608
],
sample_transforms
=
[
DecodeImage
(
to_rgb
=
True
,
with_mixup
=
True
),
MixupImage
(
alpha
=
1.5
,
beta
=
1.5
),
...
...
@@ -962,7 +962,7 @@ class YoloEvalFeed(DataFeed):
COCO_VAL_IMAGE_DIR
).
__dict__
,
fields
=
[
'image'
,
'im_size'
,
'im_id'
,
'gt_box'
,
'gt_label'
,
'is_difficult'
],
image_shape
=
[
3
,
608
,
608
],
image_shape
=
[
None
,
3
,
608
,
608
],
sample_transforms
=
[
DecodeImage
(
to_rgb
=
True
),
ResizeImage
(
target_size
=
608
,
interp
=
2
),
...
...
@@ -1018,7 +1018,7 @@ class YoloTestFeed(DataFeed):
dataset
=
SimpleDataSet
(
COCO_VAL_ANNOTATION
,
COCO_VAL_IMAGE_DIR
).
__dict__
,
fields
=
[
'image'
,
'im_size'
,
'im_id'
],
image_shape
=
[
3
,
608
,
608
],
image_shape
=
[
None
,
3
,
608
,
608
],
sample_transforms
=
[
DecodeImage
(
to_rgb
=
True
),
ResizeImage
(
target_size
=
608
,
interp
=
2
),
...
...
PaddleCV/PaddleDetection/ppdet/modeling/model_input.py
浏览文件 @
fba30199
...
...
@@ -25,16 +25,16 @@ __all__ = ['create_feed']
# yapf: disable
feed_var_def
=
[
{
'name'
:
'im_info'
,
'shape'
:
[
3
],
'dtype'
:
'float32'
,
'lod_level'
:
0
},
{
'name'
:
'im_id'
,
'shape'
:
[
1
],
'dtype'
:
'int32'
,
'lod_level'
:
0
},
{
'name'
:
'gt_box'
,
'shape'
:
[
4
],
'dtype'
:
'float32'
,
'lod_level'
:
1
},
{
'name'
:
'gt_label'
,
'shape'
:
[
1
],
'dtype'
:
'int32'
,
'lod_level'
:
1
},
{
'name'
:
'is_crowd'
,
'shape'
:
[
1
],
'dtype'
:
'int32'
,
'lod_level'
:
1
},
{
'name'
:
'gt_mask'
,
'shape'
:
[
2
],
'dtype'
:
'float32'
,
'lod_level'
:
3
},
{
'name'
:
'is_difficult'
,
'shape'
:
[
1
],
'dtype'
:
'int32'
,
'lod_level'
:
1
},
{
'name'
:
'gt_score'
,
'shape'
:
[
1
],
'dtype'
:
'float32'
,
'lod_level'
:
0
},
{
'name'
:
'im_shape'
,
'shape'
:
[
3
],
'dtype'
:
'float32'
,
'lod_level'
:
0
},
{
'name'
:
'im_size'
,
'shape'
:
[
2
],
'dtype'
:
'int32'
,
'lod_level'
:
0
},
{
'name'
:
'im_info'
,
'shape'
:
[
None
,
3
],
'dtype'
:
'float32'
,
'lod_level'
:
0
},
{
'name'
:
'im_id'
,
'shape'
:
[
None
,
1
],
'dtype'
:
'int32'
,
'lod_level'
:
0
},
{
'name'
:
'gt_box'
,
'shape'
:
[
None
,
4
],
'dtype'
:
'float32'
,
'lod_level'
:
1
},
{
'name'
:
'gt_label'
,
'shape'
:
[
None
,
1
],
'dtype'
:
'int32'
,
'lod_level'
:
1
},
{
'name'
:
'is_crowd'
,
'shape'
:
[
None
,
1
],
'dtype'
:
'int32'
,
'lod_level'
:
1
},
{
'name'
:
'gt_mask'
,
'shape'
:
[
None
,
2
],
'dtype'
:
'float32'
,
'lod_level'
:
3
},
{
'name'
:
'is_difficult'
,
'shape'
:
[
None
,
1
],
'dtype'
:
'int32'
,
'lod_level'
:
1
},
{
'name'
:
'gt_score'
,
'shape'
:
[
None
,
1
],
'dtype'
:
'float32'
,
'lod_level'
:
0
},
{
'name'
:
'im_shape'
,
'shape'
:
[
None
,
3
],
'dtype'
:
'float32'
,
'lod_level'
:
0
},
{
'name'
:
'im_size'
,
'shape'
:
[
None
,
2
],
'dtype'
:
'int32'
,
'lod_level'
:
0
},
]
# yapf: enable
...
...
@@ -52,10 +52,10 @@ def create_feed(feed, use_pyreader=True, sub_prog_feed=False):
# tensor padding with 0 is used instead of LoD tensor when
# num_max_boxes is set
if
getattr
(
feed
,
'num_max_boxes'
,
None
)
is
not
None
:
feed_var_map
[
'gt_label'
][
'shape'
]
=
[
feed
.
num_max_boxes
]
feed_var_map
[
'gt_score'
][
'shape'
]
=
[
feed
.
num_max_boxes
]
feed_var_map
[
'gt_box'
][
'shape'
]
=
[
feed
.
num_max_boxes
,
4
]
feed_var_map
[
'is_difficult'
][
'shape'
]
=
[
feed
.
num_max_boxes
]
feed_var_map
[
'gt_label'
][
'shape'
]
=
[
None
,
feed
.
num_max_boxes
]
feed_var_map
[
'gt_score'
][
'shape'
]
=
[
None
,
feed
.
num_max_boxes
]
feed_var_map
[
'gt_box'
][
'shape'
]
=
[
None
,
feed
.
num_max_boxes
,
4
]
feed_var_map
[
'is_difficult'
][
'shape'
]
=
[
None
,
feed
.
num_max_boxes
]
feed_var_map
[
'gt_label'
][
'lod_level'
]
=
0
feed_var_map
[
'gt_score'
][
'lod_level'
]
=
0
feed_var_map
[
'gt_box'
][
'lod_level'
]
=
0
...
...
@@ -113,7 +113,7 @@ def create_feed(feed, use_pyreader=True, sub_prog_feed=False):
feed
.
fields
=
feed
.
fields
+
[
box_name
]
feed_var_map
[
box_name
]
=
sub_prog_feed
feed_vars
=
OrderedDict
([(
key
,
fluid
.
layers
.
data
(
feed_vars
=
OrderedDict
([(
key
,
fluid
.
data
(
name
=
feed_var_map
[
key
][
'name'
],
shape
=
feed_var_map
[
key
][
'shape'
],
dtype
=
feed_var_map
[
key
][
'dtype'
],
...
...
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