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yolov5
Commits
442a7abd
Unverified
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442a7abd
authored
7月 20, 2021
作者:
Glenn Jocher
提交者:
GitHub
7月 20, 2021
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电子邮件补丁
差异文件
Refactor `export.py` (#4080)
* Refactor `export.py` * cleanup * Update check_requirements() * Update export.py
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+71
-59
export.py
export.py
+71
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export.py
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442a7abd
...
@@ -24,74 +24,29 @@ from utils.general import colorstr, check_img_size, check_requirements, file_siz
...
@@ -24,74 +24,29 @@ from utils.general import colorstr, check_img_size, check_requirements, file_siz
from
utils.torch_utils
import
select_device
from
utils.torch_utils
import
select_device
def
run
(
weights
=
'./yolov5s.pt'
,
# weights path
def
export_torchscript
(
model
,
img
,
file
,
optimize
):
img_size
=
(
640
,
640
),
# image (height, width)
# TorchScript model export
batch_size
=
1
,
# batch size
device
=
'cpu'
,
# cuda device, i.e. 0 or 0,1,2,3 or cpu
include
=
(
'torchscript'
,
'onnx'
,
'coreml'
),
# include formats
half
=
False
,
# FP16 half-precision export
inplace
=
False
,
# set YOLOv5 Detect() inplace=True
train
=
False
,
# model.train() mode
optimize
=
False
,
# TorchScript: optimize for mobile
dynamic
=
False
,
# ONNX: dynamic axes
simplify
=
False
,
# ONNX: simplify model
opset_version
=
12
,
# ONNX: opset version
):
t
=
time
.
time
()
include
=
[
x
.
lower
()
for
x
in
include
]
img_size
*=
2
if
len
(
img_size
)
==
1
else
1
# expand
# Load PyTorch model
device
=
select_device
(
device
)
assert
not
(
device
.
type
==
'cpu'
and
half
),
'--half only compatible with GPU export, i.e. use --device 0'
model
=
attempt_load
(
weights
,
map_location
=
device
)
# load FP32 model
labels
=
model
.
names
# Input
gs
=
int
(
max
(
model
.
stride
))
# grid size (max stride)
img_size
=
[
check_img_size
(
x
,
gs
)
for
x
in
img_size
]
# verify img_size are gs-multiples
img
=
torch
.
zeros
(
batch_size
,
3
,
*
img_size
)
.
to
(
device
)
# image size(1,3,320,192) iDetection
# Update model
if
half
:
img
,
model
=
img
.
half
(),
model
.
half
()
# to FP16
model
.
train
()
if
train
else
model
.
eval
()
# training mode = no Detect() layer grid construction
for
k
,
m
in
model
.
named_modules
():
m
.
_non_persistent_buffers_set
=
set
()
# pytorch 1.6.0 compatibility
if
isinstance
(
m
,
Conv
):
# assign export-friendly activations
if
isinstance
(
m
.
act
,
nn
.
Hardswish
):
m
.
act
=
Hardswish
()
elif
isinstance
(
m
.
act
,
nn
.
SiLU
):
m
.
act
=
SiLU
()
elif
isinstance
(
m
,
Detect
):
m
.
inplace
=
inplace
m
.
onnx_dynamic
=
dynamic
# m.forward = m.forward_export # assign forward (optional)
for
_
in
range
(
2
):
y
=
model
(
img
)
# dry runs
print
(
f
"
\n
{colorstr('PyTorch:')} starting from {weights} ({file_size(weights):.1f} MB)"
)
# TorchScript export -----------------------------------------------------------------------------------------------
if
'torchscript'
in
include
or
'coreml'
in
include
:
prefix
=
colorstr
(
'TorchScript:'
)
prefix
=
colorstr
(
'TorchScript:'
)
try
:
try
:
print
(
f
'
\n
{prefix} starting export with torch {torch.__version__}...'
)
print
(
f
'
\n
{prefix} starting export with torch {torch.__version__}...'
)
f
=
weights
.
replace
(
'.pt'
,
'.torchscript.pt'
)
# filename
f
=
file
.
with_suffix
(
'.torchscript.pt'
)
ts
=
torch
.
jit
.
trace
(
model
,
img
,
strict
=
False
)
ts
=
torch
.
jit
.
trace
(
model
,
img
,
strict
=
False
)
(
optimize_for_mobile
(
ts
)
if
optimize
else
ts
)
.
save
(
f
)
(
optimize_for_mobile
(
ts
)
if
optimize
else
ts
)
.
save
(
f
)
print
(
f
'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)'
)
print
(
f
'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)'
)
return
ts
except
Exception
as
e
:
except
Exception
as
e
:
print
(
f
'{prefix} export failure: {e}'
)
print
(
f
'{prefix} export failure: {e}'
)
# ONNX export ------------------------------------------------------------------------------------------------------
if
'onnx'
in
include
:
def
export_onnx
(
model
,
img
,
file
,
opset_version
,
train
,
dynamic
,
simplify
):
# ONNX model export
prefix
=
colorstr
(
'ONNX:'
)
prefix
=
colorstr
(
'ONNX:'
)
try
:
try
:
check_requirements
((
'onnx'
,
'onnx-simplifier'
))
import
onnx
import
onnx
print
(
f
'{prefix} starting export with onnx {onnx.__version__}...'
)
print
(
f
'{prefix} starting export with onnx {onnx.__version__}...'
)
f
=
weights
.
replace
(
'.pt'
,
'.onnx'
)
# filename
f
=
file
.
with_suffix
(
'.onnx'
)
torch
.
onnx
.
export
(
model
,
img
,
f
,
verbose
=
False
,
opset_version
=
opset_version
,
torch
.
onnx
.
export
(
model
,
img
,
f
,
verbose
=
False
,
opset_version
=
opset_version
,
training
=
torch
.
onnx
.
TrainingMode
.
TRAINING
if
train
else
torch
.
onnx
.
TrainingMode
.
EVAL
,
training
=
torch
.
onnx
.
TrainingMode
.
TRAINING
if
train
else
torch
.
onnx
.
TrainingMode
.
EVAL
,
do_constant_folding
=
not
train
,
do_constant_folding
=
not
train
,
...
@@ -109,7 +64,6 @@ def run(weights='./yolov5s.pt', # weights path
...
@@ -109,7 +64,6 @@ def run(weights='./yolov5s.pt', # weights path
# Simplify
# Simplify
if
simplify
:
if
simplify
:
try
:
try
:
check_requirements
([
'onnx-simplifier'
])
import
onnxsim
import
onnxsim
print
(
f
'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...'
)
print
(
f
'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...'
)
...
@@ -125,21 +79,79 @@ def run(weights='./yolov5s.pt', # weights path
...
@@ -125,21 +79,79 @@ def run(weights='./yolov5s.pt', # weights path
except
Exception
as
e
:
except
Exception
as
e
:
print
(
f
'{prefix} export failure: {e}'
)
print
(
f
'{prefix} export failure: {e}'
)
# CoreML export ----------------------------------------------------------------------------------------------------
if
'coreml'
in
include
:
def
export_coreml
(
ts_model
,
img
,
file
,
train
):
# CoreML model export
prefix
=
colorstr
(
'CoreML:'
)
prefix
=
colorstr
(
'CoreML:'
)
try
:
try
:
import
coremltools
as
ct
import
coremltools
as
ct
print
(
f
'{prefix} starting export with coremltools {ct.__version__}...'
)
print
(
f
'{prefix} starting export with coremltools {ct.__version__}...'
)
f
=
file
.
with_suffix
(
'.mlmodel'
)
assert
train
,
'CoreML exports should be placed in model.train() mode with `python export.py --train`'
assert
train
,
'CoreML exports should be placed in model.train() mode with `python export.py --train`'
model
=
ct
.
convert
(
ts
,
inputs
=
[
ct
.
ImageType
(
'image'
,
shape
=
img
.
shape
,
scale
=
1
/
255.0
,
bias
=
[
0
,
0
,
0
])])
model
=
ct
.
convert
(
ts_model
,
inputs
=
[
ct
.
ImageType
(
'image'
,
shape
=
img
.
shape
,
scale
=
1
/
255.0
,
bias
=
[
0
,
0
,
0
])])
f
=
weights
.
replace
(
'.pt'
,
'.mlmodel'
)
# filename
model
.
save
(
f
)
model
.
save
(
f
)
print
(
f
'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)'
)
print
(
f
'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)'
)
except
Exception
as
e
:
except
Exception
as
e
:
print
(
f
'{prefix} export failure: {e}'
)
print
(
f
'{prefix} export failure: {e}'
)
def
run
(
weights
=
'./yolov5s.pt'
,
# weights path
img_size
=
(
640
,
640
),
# image (height, width)
batch_size
=
1
,
# batch size
device
=
'cpu'
,
# cuda device, i.e. 0 or 0,1,2,3 or cpu
include
=
(
'torchscript'
,
'onnx'
,
'coreml'
),
# include formats
half
=
False
,
# FP16 half-precision export
inplace
=
False
,
# set YOLOv5 Detect() inplace=True
train
=
False
,
# model.train() mode
optimize
=
False
,
# TorchScript: optimize for mobile
dynamic
=
False
,
# ONNX: dynamic axes
simplify
=
False
,
# ONNX: simplify model
opset_version
=
12
,
# ONNX: opset version
):
t
=
time
.
time
()
include
=
[
x
.
lower
()
for
x
in
include
]
img_size
*=
2
if
len
(
img_size
)
==
1
else
1
# expand
file
=
Path
(
weights
)
# Load PyTorch model
device
=
select_device
(
device
)
assert
not
(
device
.
type
==
'cpu'
and
half
),
'--half only compatible with GPU export, i.e. use --device 0'
model
=
attempt_load
(
weights
,
map_location
=
device
)
# load FP32 model
names
=
model
.
names
# Input
gs
=
int
(
max
(
model
.
stride
))
# grid size (max stride)
img_size
=
[
check_img_size
(
x
,
gs
)
for
x
in
img_size
]
# verify img_size are gs-multiples
img
=
torch
.
zeros
(
batch_size
,
3
,
*
img_size
)
.
to
(
device
)
# image size(1,3,320,192) iDetection
# Update model
if
half
:
img
,
model
=
img
.
half
(),
model
.
half
()
# to FP16
model
.
train
()
if
train
else
model
.
eval
()
# training mode = no Detect() layer grid construction
for
k
,
m
in
model
.
named_modules
():
if
isinstance
(
m
,
Conv
):
# assign export-friendly activations
if
isinstance
(
m
.
act
,
nn
.
Hardswish
):
m
.
act
=
Hardswish
()
elif
isinstance
(
m
.
act
,
nn
.
SiLU
):
m
.
act
=
SiLU
()
elif
isinstance
(
m
,
Detect
):
m
.
inplace
=
inplace
m
.
onnx_dynamic
=
dynamic
# m.forward = m.forward_export # assign forward (optional)
for
_
in
range
(
2
):
y
=
model
(
img
)
# dry runs
print
(
f
"
\n
{colorstr('PyTorch:')} starting from {weights} ({file_size(weights):.1f} MB)"
)
# Exports
if
'onnx'
in
include
:
export_onnx
(
model
,
img
,
file
,
opset_version
,
train
,
dynamic
,
simplify
)
if
'torchscript'
in
include
or
'coreml'
in
include
:
ts
=
export_torchscript
(
model
,
img
,
file
,
optimize
)
if
'coreml'
in
include
:
export_coreml
(
ts
,
img
,
file
,
train
)
# Finish
# Finish
print
(
f
'
\n
Export complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.'
)
print
(
f
'
\n
Export complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.'
)
...
...
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