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f92927ef
编写于
2月 13, 2020
作者:
W
whs
提交者:
GitHub
2月 13, 2020
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Remove unused file from pruning demo. (#225)
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slim/prune/compress.py
slim/prune/compress.py
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slim/prune/images/MobileNetV1-YoloV3.pdf
slim/prune/images/MobileNetV1-YoloV3.pdf
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slim/prune/yolov3_mobilenet_v1_slim.yaml
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slim/prune/compress.py
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# 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
sys
sys
.
path
.
append
(
"../../"
)
from
paddle.fluid.contrib.slim
import
Compressor
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.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
,
cfg
):
"""
Run evaluation program, return program outputs.
"""
iter_id
=
0
results
=
[]
if
len
(
cls
)
!=
0
:
values
=
[]
for
i
in
range
(
len
(
cls
)):
_
,
accum_map
=
cls
[
i
].
get_map_var
()
cls
[
i
].
reset
(
exe
)
values
.
append
(
accum_map
)
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
)
if
cfg
.
metric
==
'VOC'
:
outs
.
append
(
data
[
'gt_box'
])
outs
.
append
(
data
[
'gt_label'
])
outs
.
append
(
data
[
'is_difficult'
])
elif
cfg
.
metric
==
'COCO'
:
outs
.
append
(
data
[
'im_info'
])
outs
.
append
(
data
[
'im_id'
])
outs
.
append
(
data
[
'im_shape'
])
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
'train_feed'
not
in
cfg
:
train_feed
=
create
(
main_arch
+
'TrainFeed'
)
else
:
train_feed
=
create
(
cfg
.
train_feed
)
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
)
lr_builder
=
create
(
'LearningRate'
)
optim_builder
=
create
(
'OptimizerBuilder'
)
# build program
startup_prog
=
fluid
.
Program
()
train_prog
=
fluid
.
Program
()
with
fluid
.
program_guard
(
train_prog
,
startup_prog
):
with
fluid
.
unique_name
.
guard
():
model
=
create
(
main_arch
)
_
,
feed_vars
=
create_feed
(
train_feed
,
True
)
train_fetches
=
model
.
train
(
feed_vars
)
loss
=
train_fetches
[
'loss'
]
lr
=
lr_builder
()
optimizer
=
optim_builder
(
lr
)
optimizer
.
minimize
(
loss
)
train_reader
=
create_reader
(
train_feed
,
cfg
.
max_iters
,
FLAGS
.
dataset_dir
)
# parse train fetches
train_keys
,
train_values
,
_
=
parse_fetches
(
train_fetches
)
train_keys
.
append
(
"lr"
)
train_values
.
append
(
lr
.
name
)
train_fetch_list
=
[]
for
k
,
v
in
zip
(
train_keys
,
train_values
):
train_fetch_list
.
append
((
k
,
v
))
eval_prog
=
fluid
.
Program
()
with
fluid
.
program_guard
(
eval_prog
,
startup_prog
):
with
fluid
.
unique_name
.
guard
():
model
=
create
(
main_arch
)
_
,
test_feed_vars
=
create_feed
(
eval_feed
,
True
)
fetches
=
model
.
eval
(
test_feed_vars
)
eval_prog
=
eval_prog
.
clone
(
True
)
eval_reader
=
create_reader
(
eval_feed
,
args_path
=
FLAGS
.
dataset_dir
)
test_data_feed
=
fluid
.
DataFeeder
(
test_feed_vars
.
values
(),
place
)
# parse eval fetches
extra_keys
=
[]
if
cfg
.
metric
==
'COCO'
:
extra_keys
=
[
'im_info'
,
'im_id'
,
'im_shape'
]
if
cfg
.
metric
==
'VOC'
:
extra_keys
=
[
'gt_box'
,
'gt_label'
,
'is_difficult'
]
eval_keys
,
eval_values
,
eval_cls
=
parse_fetches
(
fetches
,
eval_prog
,
extra_keys
)
eval_fetch_list
=
[]
for
k
,
v
in
zip
(
eval_keys
,
eval_values
):
eval_fetch_list
.
append
((
k
,
v
))
exe
.
run
(
startup_prog
)
checkpoint
.
load_params
(
exe
,
train_prog
,
cfg
.
pretrain_weights
)
best_box_ap_list
=
[]
def
eval_func
(
program
,
scope
):
#place = fluid.CPUPlace()
#exe = fluid.Executor(place)
results
=
eval_run
(
exe
,
program
,
eval_reader
,
eval_keys
,
eval_values
,
eval_cls
,
test_data_feed
,
cfg
)
resolution
=
None
if
'mask'
in
results
[
0
]:
resolution
=
model
.
mask_head
.
resolution
box_ap_stats
=
eval_results
(
results
,
eval_feed
,
cfg
.
metric
,
cfg
.
num_classes
,
resolution
,
False
,
FLAGS
.
output_eval
)
if
len
(
best_box_ap_list
)
==
0
:
best_box_ap_list
.
append
(
box_ap_stats
[
0
])
elif
box_ap_stats
[
0
]
>
best_box_ap_list
[
0
]:
best_box_ap_list
[
0
]
=
box_ap_stats
[
0
]
logger
.
info
(
"Best test box ap: {}"
.
format
(
best_box_ap_list
[
0
]))
return
best_box_ap_list
[
0
]
test_feed
=
[(
'image'
,
test_feed_vars
[
'image'
].
name
),
(
'im_size'
,
test_feed_vars
[
'im_size'
].
name
)]
com
=
Compressor
(
place
,
fluid
.
global_scope
(),
train_prog
,
train_reader
=
train_reader
,
train_feed_list
=
[(
key
,
value
.
name
)
for
key
,
value
in
feed_vars
.
items
()],
train_fetch_list
=
train_fetch_list
,
eval_program
=
eval_prog
,
eval_reader
=
eval_reader
,
eval_feed_list
=
test_feed
,
eval_func
=
{
'map'
:
eval_func
},
eval_fetch_list
=
[
eval_fetch_list
[
0
]],
save_eval_model
=
True
,
prune_infer_model
=
[[
"image"
,
"im_size"
],
[
"multiclass_nms_0.tmp_0"
]],
train_optimizer
=
None
)
com
.
config
(
FLAGS
.
slim_file
)
com
.
run
()
if
__name__
==
'__main__'
:
parser
=
ArgsParser
()
parser
.
add_argument
(
"-s"
,
"--slim_file"
,
default
=
None
,
type
=
str
,
help
=
"Config file of PaddleSlim."
)
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"
)
FLAGS
=
parser
.
parse_args
()
main
()
slim/prune/images/MobileNetV1-YoloV3.pdf
已删除
100644 → 0
浏览文件 @
2ae28aaf
文件已删除
slim/prune/yolov3_mobilenet_v1_slim.yaml
已删除
100644 → 0
浏览文件 @
2ae28aaf
version
:
1.0
pruners
:
pruner_1
:
class
:
'
StructurePruner'
pruning_axis
:
'
*'
:
0
criterions
:
'
*'
:
'
l1_norm'
strategies
:
uniform_pruning_strategy
:
class
:
'
UniformPruneStrategy'
pruner
:
'
pruner_1'
start_epoch
:
0
target_ratio
:
0.5
pruned_params
:
'
(conv2_1_sep_weights)|(conv2_2_sep_weights)|(conv3_1_sep_weights)|(conv4_1_sep_weights)|(conv5_1_sep_weights)|(conv5_2_sep_weights)|(conv5_3_sep_weights)|(conv5_4_sep_weights)|(conv5_5_sep_weights)|(conv5_6_sep_weights)|(yolo_block.0.0.0.conv.weights)|(yolo_block.0.0.1.conv.weights)|(yolo_block.0.1.0.conv.weights)|(yolo_block.0.1.1.conv.weights)|(yolo_block.1.0.0.conv.weights)|(yolo_block.1.0.1.conv.weights)|(yolo_block.1.1.0.conv.weights)|(yolo_block.1.1.1.conv.weights)|(yolo_block.1.2.conv.weights)|(yolo_block.2.0.0.conv.weights)|(yolo_block.2.0.1.conv.weights)|(yolo_block.2.1.1.conv.weights)|(yolo_block.2.2.conv.weights)|(yolo_block.2.tip.conv.weights)'
metric_name
:
'
acc_top1'
compressor
:
epoch
:
271
eval_epoch
:
10
#init_model: './checkpoints/0' # Please enable this option for loading checkpoint.
checkpoint_path
:
'
./checkpoints/'
strategies
:
-
uniform_pruning_strategy
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