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cf872f91
编写于
1月 20, 2020
作者:
Q
qingqing01
提交者:
GitHub
1月 20, 2020
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configs2/faster_rcnn_r50_1x.yml
configs2/faster_rcnn_r50_1x.yml
+0
-96
configs2/faster_reader.yml
configs2/faster_reader.yml
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slim/eval.py
slim/eval.py
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-194
slim/infer.py
slim/infer.py
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configs2/faster_rcnn_r50_1x.yml
已删除
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浏览文件 @
ccf94523
architecture
:
FasterRCNN
use_gpu
:
true
max_iters
:
180000
log_smooth_window
:
20
save_dir
:
output
snapshot_iter
:
10000
pretrain_weights
:
https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
metric
:
COCO
weights
:
output/faster_rcnn_r50_1x/model_final
num_classes
:
81
FasterRCNN
:
backbone
:
ResNet
rpn_head
:
RPNHead
roi_extractor
:
RoIAlign
bbox_head
:
BBoxHead
bbox_assigner
:
BBoxAssigner
ResNet
:
norm_type
:
affine_channel
depth
:
50
feature_maps
:
4
freeze_at
:
2
ResNetC5
:
depth
:
50
norm_type
:
affine_channel
RPNHead
:
anchor_generator
:
anchor_sizes
:
[
32
,
64
,
128
,
256
,
512
]
aspect_ratios
:
[
0.5
,
1.0
,
2.0
]
stride
:
[
16.0
,
16.0
]
variance
:
[
1.0
,
1.0
,
1.0
,
1.0
]
rpn_target_assign
:
rpn_batch_size_per_im
:
256
rpn_fg_fraction
:
0.5
rpn_negative_overlap
:
0.3
rpn_positive_overlap
:
0.7
rpn_straddle_thresh
:
0.0
use_random
:
true
train_proposal
:
min_size
:
0.0
nms_thresh
:
0.7
pre_nms_top_n
:
12000
post_nms_top_n
:
2000
test_proposal
:
min_size
:
0.0
nms_thresh
:
0.7
pre_nms_top_n
:
6000
post_nms_top_n
:
1000
RoIAlign
:
resolution
:
14
sampling_ratio
:
0
spatial_scale
:
0.0625
BBoxAssigner
:
batch_size_per_im
:
512
bbox_reg_weights
:
[
0.1
,
0.1
,
0.2
,
0.2
]
bg_thresh_hi
:
0.5
bg_thresh_lo
:
0.0
fg_fraction
:
0.25
fg_thresh
:
0.5
BBoxHead
:
head
:
ResNetC5
nms
:
keep_top_k
:
100
nms_threshold
:
0.5
score_threshold
:
0.05
LearningRate
:
base_lr
:
0.01
schedulers
:
-
!PiecewiseDecay
gamma
:
0.1
milestones
:
[
120000
,
160000
]
-
!LinearWarmup
start_factor
:
0.3333333333333333
steps
:
500
OptimizerBuilder
:
optimizer
:
momentum
:
0.9
type
:
Momentum
regularizer
:
factor
:
0.0001
type
:
L2
_LOADER_
:
'
faster_reader.yml'
TrainLoader
:
inputs_def
:
image_shape
:
[
3
,
800
,
800
]
fields
:
[
'
image'
,
'
im_info'
,
'
im_id'
,
'
gt_bbox'
,
'
gt_class'
,
'
is_crowd'
]
batch_size
:
3
configs2/faster_reader.yml
已删除
100644 → 0
浏览文件 @
ccf94523
TrainReader
:
inputs_def
:
image_shape
:
[
3
,
NULL
,
NULL
]
fields
:
[
'
image'
,
'
im_info'
,
'
im_id'
,
'
gt_bbox'
,
'
gt_class'
,
'
is_crowd'
]
dataset
:
!COCODataSet
image_dir
:
val2017
anno_path
:
annotations/instances_val2017.json
dataset_dir
:
dataset/coco
sample_transforms
:
-
!DecodeImage
to_rgb
:
true
-
!RandomFlipImage
prob
:
0.5
-
!NormalizeImage
is_channel_first
:
false
is_scale
:
true
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
-
!ResizeImage
target_size
:
800
max_size
:
1333
interp
:
1
use_cv2
:
true
-
!Permute
to_bgr
:
false
channel_first
:
true
batch_transforms
:
-
!PadBatch
pad_to_stride
:
32
use_padded_im_info
:
false
batch_size
:
1
shuffle
:
true
worker_num
:
2
drop_last
:
false
use_multi_process
:
false
EvalReader
:
inputs_def
:
image_shape
:
[
3
,
800
,
1333
]
fields
:
[
'
image'
,
'
im_info'
,
'
im_id'
,
'
im_shape'
]
# for voc
#fields: ['image', 'im_info', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
dataset
:
!COCODataSet
image_dir
:
val2017
anno_path
:
annotations/instances_val2017.json
dataset_dir
:
dataset/coco
#sample_num: 100
sample_transforms
:
-
!DecodeImage
to_rgb
:
true
with_mixup
:
false
-
!NormalizeImage
is_channel_first
:
false
is_scale
:
true
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
-
!ResizeImage
interp
:
1
max_size
:
1333
target_size
:
800
use_cv2
:
true
-
!Permute
channel_first
:
true
to_bgr
:
false
batch_transforms
:
-
!PadBatch
pad_to_stride
:
32
use_padded_im_info
:
true
batch_size
:
1
shuffle
:
false
drop_last
:
false
# worker_num: 2
TestReader
:
inputs_def
:
image_shape
:
[
3
,
800
,
1333
]
fields
:
[
'
image'
,
'
im_info'
,
'
im_id'
,
'
im_shape'
]
dataset
:
!ImageFolder
anno_path
:
annotations/instances_val2017.json
sample_transforms
:
-
!DecodeImage
to_rgb
:
true
with_mixup
:
false
-
!NormalizeImage
is_channel_first
:
false
is_scale
:
true
mean
:
[
0.485
,
0.456
,
0.406
]
std
:
[
0.229
,
0.224
,
0.225
]
-
!ResizeImage
interp
:
1
max_size
:
1333
target_size
:
800
use_cv2
:
true
-
!Permute
channel_first
:
true
to_bgr
:
false
batch_transforms
:
-
!PadBatch
pad_to_stride
:
32
use_padded_im_info
:
true
batch_size
:
1
shuffle
:
false
drop_last
:
false
slim/eval.py
已删除
100644 → 0
浏览文件 @
ccf94523
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
absolute_import
from
__future__
import
division
from
__future__
import
print_function
import
os
import
time
import
multiprocessing
import
numpy
as
np
import
datetime
from
collections
import
deque
import
sys
sys
.
path
.
append
(
"../../"
)
from
paddle.fluid.contrib.slim
import
Compressor
from
paddle.fluid.framework
import
IrGraph
from
paddle.fluid
import
core
from
paddle.fluid.contrib.slim.quantization
import
QuantizationTransformPass
from
paddle.fluid.contrib.slim.quantization
import
QuantizationFreezePass
from
paddle.fluid.contrib.slim.quantization
import
ConvertToInt8Pass
from
paddle.fluid.contrib.slim.quantization
import
TransformForMobilePass
def
set_paddle_flags
(
**
kwargs
):
for
key
,
value
in
kwargs
.
items
():
if
os
.
environ
.
get
(
key
,
None
)
is
None
:
os
.
environ
[
key
]
=
str
(
value
)
# NOTE(paddle-dev): All of these flags should be set before
# `import paddle`. Otherwise, it would not take any effect.
set_paddle_flags
(
FLAGS_eager_delete_tensor_gb
=
0
,
# enable GC to save memory
)
from
paddle
import
fluid
from
ppdet.core.workspace
import
load_config
,
merge_config
,
create
from
ppdet.data.data_feed
import
create_reader
from
ppdet.utils.eval_utils
import
parse_fetches
,
eval_results
from
ppdet.utils.stats
import
TrainingStats
from
ppdet.utils.cli
import
ArgsParser
from
ppdet.utils.check
import
check_gpu
import
ppdet.utils.checkpoint
as
checkpoint
from
ppdet.modeling.model_input
import
create_feed
import
logging
FORMAT
=
'%(asctime)s-%(levelname)s: %(message)s'
logging
.
basicConfig
(
level
=
logging
.
INFO
,
format
=
FORMAT
)
logger
=
logging
.
getLogger
(
__name__
)
def
eval_run
(
exe
,
compile_program
,
reader
,
keys
,
values
,
cls
,
test_feed
):
"""
Run evaluation program, return program outputs.
"""
iter_id
=
0
results
=
[]
images_num
=
0
start_time
=
time
.
time
()
has_bbox
=
'bbox'
in
keys
for
data
in
reader
():
data
=
test_feed
.
feed
(
data
)
feed_data
=
{
'image'
:
data
[
'image'
],
'im_size'
:
data
[
'im_size'
]}
outs
=
exe
.
run
(
compile_program
,
feed
=
feed_data
,
fetch_list
=
values
[
0
],
return_numpy
=
False
)
outs
.
append
(
data
[
'gt_box'
])
outs
.
append
(
data
[
'gt_label'
])
outs
.
append
(
data
[
'is_difficult'
])
res
=
{
k
:
(
np
.
array
(
v
),
v
.
recursive_sequence_lengths
())
for
k
,
v
in
zip
(
keys
,
outs
)
}
results
.
append
(
res
)
if
iter_id
%
100
==
0
:
logger
.
info
(
'Test iter {}'
.
format
(
iter_id
))
iter_id
+=
1
images_num
+=
len
(
res
[
'bbox'
][
1
][
0
])
if
has_bbox
else
1
logger
.
info
(
'Test finish iter {}'
.
format
(
iter_id
))
end_time
=
time
.
time
()
fps
=
images_num
/
(
end_time
-
start_time
)
if
has_bbox
:
logger
.
info
(
'Total number of images: {}, inference time: {} fps.'
.
format
(
images_num
,
fps
))
else
:
logger
.
info
(
'Total iteration: {}, inference time: {} batch/s.'
.
format
(
images_num
,
fps
))
return
results
def
main
():
cfg
=
load_config
(
FLAGS
.
config
)
if
'architecture'
in
cfg
:
main_arch
=
cfg
.
architecture
else
:
raise
ValueError
(
"'architecture' not specified in config file."
)
merge_config
(
FLAGS
.
opt
)
if
'log_iter'
not
in
cfg
:
cfg
.
log_iter
=
20
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu
(
cfg
.
use_gpu
)
if
cfg
.
use_gpu
:
devices_num
=
fluid
.
core
.
get_cuda_device_count
()
else
:
devices_num
=
int
(
os
.
environ
.
get
(
'CPU_NUM'
,
multiprocessing
.
cpu_count
()))
if
'eval_feed'
not
in
cfg
:
eval_feed
=
create
(
main_arch
+
'EvalFeed'
)
else
:
eval_feed
=
create
(
cfg
.
eval_feed
)
place
=
fluid
.
CUDAPlace
(
0
)
if
cfg
.
use_gpu
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
_
,
test_feed_vars
=
create_feed
(
eval_feed
,
False
)
eval_reader
=
create_reader
(
eval_feed
,
args_path
=
FLAGS
.
dataset_dir
)
#eval_pyreader.decorate_sample_list_generator(eval_reader, place)
test_data_feed
=
fluid
.
DataFeeder
(
test_feed_vars
.
values
(),
place
)
assert
os
.
path
.
exists
(
FLAGS
.
model_path
)
infer_prog
,
feed_names
,
fetch_targets
=
fluid
.
io
.
load_inference_model
(
dirname
=
FLAGS
.
model_path
,
executor
=
exe
,
model_filename
=
FLAGS
.
model_name
,
params_filename
=
FLAGS
.
params_name
)
eval_keys
=
[
'bbox'
,
'gt_box'
,
'gt_label'
,
'is_difficult'
]
eval_values
=
[
'multiclass_nms_0.tmp_0'
,
'gt_box'
,
'gt_label'
,
'is_difficult'
]
eval_cls
=
[]
eval_values
[
0
]
=
fetch_targets
[
0
]
results
=
eval_run
(
exe
,
infer_prog
,
eval_reader
,
eval_keys
,
eval_values
,
eval_cls
,
test_data_feed
)
resolution
=
None
if
'mask'
in
results
[
0
]:
resolution
=
model
.
mask_head
.
resolution
eval_results
(
results
,
eval_feed
,
cfg
.
metric
,
cfg
.
num_classes
,
resolution
,
False
,
FLAGS
.
output_eval
)
if
__name__
==
'__main__'
:
parser
=
ArgsParser
()
parser
.
add_argument
(
"-m"
,
"--model_path"
,
default
=
None
,
type
=
str
,
help
=
"path of checkpoint"
)
parser
.
add_argument
(
"--output_eval"
,
default
=
None
,
type
=
str
,
help
=
"Evaluation directory, default is current directory."
)
parser
.
add_argument
(
"-d"
,
"--dataset_dir"
,
default
=
None
,
type
=
str
,
help
=
"Dataset path, same as DataFeed.dataset.dataset_dir"
)
parser
.
add_argument
(
"--model_name"
,
default
=
'model'
,
type
=
str
,
help
=
"model file name to load_inference_model"
)
parser
.
add_argument
(
"--params_name"
,
default
=
'params'
,
type
=
str
,
help
=
"params file name to load_inference_model"
)
FLAGS
=
parser
.
parse_args
()
main
()
slim/infer.py
已删除
100644 → 0
浏览文件 @
ccf94523
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
absolute_import
from
__future__
import
division
from
__future__
import
print_function
import
os
import
sys
import
glob
import
time
import
numpy
as
np
from
PIL
import
Image
sys
.
path
.
append
(
"../../"
)
def
set_paddle_flags
(
**
kwargs
):
for
key
,
value
in
kwargs
.
items
():
if
os
.
environ
.
get
(
key
,
None
)
is
None
:
os
.
environ
[
key
]
=
str
(
value
)
# NOTE(paddle-dev): All of these flags should be set before
# `import paddle`. Otherwise, it would not take any effect.
set_paddle_flags
(
FLAGS_eager_delete_tensor_gb
=
0
,
# enable GC to save memory
)
from
paddle
import
fluid
from
ppdet.utils.cli
import
print_total_cfg
from
ppdet.core.workspace
import
load_config
,
merge_config
,
create
from
ppdet.modeling.model_input
import
create_feed
from
ppdet.data.data_feed
import
create_reader
from
ppdet.utils.eval_utils
import
parse_fetches
from
ppdet.utils.cli
import
ArgsParser
from
ppdet.utils.check
import
check_gpu
from
ppdet.utils.visualizer
import
visualize_results
import
ppdet.utils.checkpoint
as
checkpoint
import
logging
FORMAT
=
'%(asctime)s-%(levelname)s: %(message)s'
logging
.
basicConfig
(
level
=
logging
.
INFO
,
format
=
FORMAT
)
logger
=
logging
.
getLogger
(
__name__
)
def
get_save_image_name
(
output_dir
,
image_path
):
"""
Get save image name from source image path.
"""
if
not
os
.
path
.
exists
(
output_dir
):
os
.
makedirs
(
output_dir
)
image_name
=
os
.
path
.
split
(
image_path
)[
-
1
]
name
,
ext
=
os
.
path
.
splitext
(
image_name
)
return
os
.
path
.
join
(
output_dir
,
"{}"
.
format
(
name
))
+
ext
def
get_test_images
(
infer_dir
,
infer_img
):
"""
Get image path list in TEST mode
"""
assert
infer_img
is
not
None
or
infer_dir
is
not
None
,
\
"--infer_img or --infer_dir should be set"
assert
infer_img
is
None
or
os
.
path
.
isfile
(
infer_img
),
\
"{} is not a file"
.
format
(
infer_img
)
assert
infer_dir
is
None
or
os
.
path
.
isdir
(
infer_dir
),
\
"{} is not a directory"
.
format
(
infer_dir
)
images
=
[]
# infer_img has a higher priority
if
infer_img
and
os
.
path
.
isfile
(
infer_img
):
images
.
append
(
infer_img
)
return
images
infer_dir
=
os
.
path
.
abspath
(
infer_dir
)
assert
os
.
path
.
isdir
(
infer_dir
),
\
"infer_dir {} is not a directory"
.
format
(
infer_dir
)
exts
=
[
'jpg'
,
'jpeg'
,
'png'
,
'bmp'
]
exts
+=
[
ext
.
upper
()
for
ext
in
exts
]
for
ext
in
exts
:
images
.
extend
(
glob
.
glob
(
'{}/*.{}'
.
format
(
infer_dir
,
ext
)))
assert
len
(
images
)
>
0
,
"no image found in {}"
.
format
(
infer_dir
)
logger
.
info
(
"Found {} inference images in total."
.
format
(
len
(
images
)))
return
images
def
main
():
cfg
=
load_config
(
FLAGS
.
config
)
if
'architecture'
in
cfg
:
main_arch
=
cfg
.
architecture
else
:
raise
ValueError
(
"'architecture' not specified in config file."
)
merge_config
(
FLAGS
.
opt
)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu
(
cfg
.
use_gpu
)
# print_total_cfg(cfg)
if
'test_feed'
not
in
cfg
:
test_feed
=
create
(
main_arch
+
'TestFeed'
)
else
:
test_feed
=
create
(
cfg
.
test_feed
)
test_images
=
get_test_images
(
FLAGS
.
infer_dir
,
FLAGS
.
infer_img
)
test_feed
.
dataset
.
add_images
(
test_images
)
place
=
fluid
.
CUDAPlace
(
0
)
if
cfg
.
use_gpu
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
infer_prog
,
feed_var_names
,
fetch_list
=
fluid
.
io
.
load_inference_model
(
dirname
=
FLAGS
.
model_path
,
model_filename
=
FLAGS
.
model_name
,
params_filename
=
FLAGS
.
params_name
,
executor
=
exe
)
reader
=
create_reader
(
test_feed
)
feeder
=
fluid
.
DataFeeder
(
place
=
place
,
feed_list
=
feed_var_names
,
program
=
infer_prog
)
# parse infer fetches
assert
cfg
.
metric
in
[
'COCO'
,
'VOC'
],
\
"unknown metric type {}"
.
format
(
cfg
.
metric
)
extra_keys
=
[]
if
cfg
[
'metric'
]
==
'COCO'
:
extra_keys
=
[
'im_info'
,
'im_id'
,
'im_shape'
]
if
cfg
[
'metric'
]
==
'VOC'
:
extra_keys
=
[
'im_id'
,
'im_shape'
]
keys
,
values
,
_
=
parse_fetches
({
'bbox'
:
fetch_list
},
infer_prog
,
extra_keys
)
# parse dataset category
if
cfg
.
metric
==
'COCO'
:
from
ppdet.utils.coco_eval
import
bbox2out
,
mask2out
,
get_category_info
if
cfg
.
metric
==
"VOC"
:
from
ppdet.utils.voc_eval
import
bbox2out
,
get_category_info
anno_file
=
getattr
(
test_feed
.
dataset
,
'annotation'
,
None
)
with_background
=
getattr
(
test_feed
,
'with_background'
,
True
)
use_default_label
=
getattr
(
test_feed
,
'use_default_label'
,
False
)
clsid2catid
,
catid2name
=
get_category_info
(
anno_file
,
with_background
,
use_default_label
)
# whether output bbox is normalized in model output layer
is_bbox_normalized
=
False
# use tb-paddle to log image
if
FLAGS
.
use_tb
:
from
tb_paddle
import
SummaryWriter
tb_writer
=
SummaryWriter
(
FLAGS
.
tb_log_dir
)
tb_image_step
=
0
tb_image_frame
=
0
# each frame can display ten pictures at most.
imid2path
=
reader
.
imid2path
keys
=
[
'bbox'
]
infer_time
=
True
compile_prog
=
fluid
.
compiler
.
CompiledProgram
(
infer_prog
)
for
iter_id
,
data
in
enumerate
(
reader
()):
feed_data
=
[[
d
[
0
],
d
[
1
]]
for
d
in
data
]
# for infer time
if
infer_time
:
warmup_times
=
10
repeats_time
=
100
feed_data_dict
=
feeder
.
feed
(
feed_data
)
for
i
in
range
(
warmup_times
):
exe
.
run
(
compile_prog
,
feed
=
feed_data_dict
,
fetch_list
=
fetch_list
,
return_numpy
=
False
)
start_time
=
time
.
time
()
for
i
in
range
(
repeats_time
):
exe
.
run
(
compile_prog
,
feed
=
feed_data_dict
,
fetch_list
=
fetch_list
,
return_numpy
=
False
)
print
(
"infer time: {} ms/sample"
.
format
((
time
.
time
()
-
start_time
)
*
1000
/
repeats_time
))
infer_time
=
False
outs
=
exe
.
run
(
compile_prog
,
feed
=
feeder
.
feed
(
feed_data
),
fetch_list
=
fetch_list
,
return_numpy
=
False
)
res
=
{
k
:
(
np
.
array
(
v
),
v
.
recursive_sequence_lengths
())
for
k
,
v
in
zip
(
keys
,
outs
)
}
res
[
'im_id'
]
=
[[
d
[
2
]
for
d
in
data
]]
logger
.
info
(
'Infer iter {}'
.
format
(
iter_id
))
bbox_results
=
None
mask_results
=
None
if
'bbox'
in
res
:
bbox_results
=
bbox2out
([
res
],
clsid2catid
,
is_bbox_normalized
)
if
'mask'
in
res
:
mask_results
=
mask2out
([
res
],
clsid2catid
,
model
.
mask_head
.
resolution
)
# visualize result
im_ids
=
res
[
'im_id'
][
0
]
for
im_id
in
im_ids
:
image_path
=
imid2path
[
int
(
im_id
)]
image
=
Image
.
open
(
image_path
).
convert
(
'RGB'
)
# use tb-paddle to log original image
if
FLAGS
.
use_tb
:
original_image_np
=
np
.
array
(
image
)
tb_writer
.
add_image
(
"original/frame_{}"
.
format
(
tb_image_frame
),
original_image_np
,
tb_image_step
,
dataformats
=
'HWC'
)
image
=
visualize_results
(
image
,
int
(
im_id
),
catid2name
,
FLAGS
.
draw_threshold
,
bbox_results
,
mask_results
)
# use tb-paddle to log image with bbox
if
FLAGS
.
use_tb
:
infer_image_np
=
np
.
array
(
image
)
tb_writer
.
add_image
(
"bbox/frame_{}"
.
format
(
tb_image_frame
),
infer_image_np
,
tb_image_step
,
dataformats
=
'HWC'
)
tb_image_step
+=
1
if
tb_image_step
%
10
==
0
:
tb_image_step
=
0
tb_image_frame
+=
1
save_name
=
get_save_image_name
(
FLAGS
.
output_dir
,
image_path
)
logger
.
info
(
"Detection bbox results save in {}"
.
format
(
save_name
))
image
.
save
(
save_name
,
quality
=
95
)
if
__name__
==
'__main__'
:
parser
=
ArgsParser
()
parser
.
add_argument
(
"--infer_dir"
,
type
=
str
,
default
=
None
,
help
=
"Directory for images to perform inference on."
)
parser
.
add_argument
(
"--infer_img"
,
type
=
str
,
default
=
None
,
help
=
"Image path, has higher priority over --infer_dir"
)
parser
.
add_argument
(
"--output_dir"
,
type
=
str
,
default
=
"output"
,
help
=
"Directory for storing the output visualization files."
)
parser
.
add_argument
(
"--draw_threshold"
,
type
=
float
,
default
=
0.5
,
help
=
"Threshold to reserve the result for visualization."
)
parser
.
add_argument
(
"--use_tb"
,
type
=
bool
,
default
=
False
,
help
=
"whether to record the data to Tensorboard."
)
parser
.
add_argument
(
'--tb_log_dir'
,
type
=
str
,
default
=
"tb_log_dir/image"
,
help
=
'Tensorboard logging directory for image.'
)
parser
.
add_argument
(
'--model_path'
,
type
=
str
,
default
=
None
,
help
=
"inference model path"
)
parser
.
add_argument
(
'--model_name'
,
type
=
str
,
default
=
'__model__.infer'
,
help
=
"model filename for inference model"
)
parser
.
add_argument
(
'--params_name'
,
type
=
str
,
default
=
'__params__'
,
help
=
"params filename for inference model"
)
FLAGS
=
parser
.
parse_args
()
main
()
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