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pytorch-image-models
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c4572cc5
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pytorch-image-models
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前往新版Gitcode,体验更适合开发者的 AI 搜索 >>
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c4572cc5
编写于
5月 24, 2021
作者:
R
Ross Wightman
浏览文件
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浏览文件
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电子邮件补丁
差异文件
Add Visformer-small weighs, tweak torchscript jit test img size.
上级
83487e2a
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
14 addition
and
8 deletion
+14
-8
tests/test_models.py
tests/test_models.py
+11
-7
timm/models/visformer.py
timm/models/visformer.py
+3
-1
未找到文件。
tests/test_models.py
浏览文件 @
c4572cc5
...
...
@@ -33,7 +33,11 @@ else:
TARGET_FWD_SIZE
=
MAX_FWD_SIZE
=
384
TARGET_BWD_SIZE
=
128
MAX_BWD_SIZE
=
320
MAX_FWD_FEAT_SIZE
=
448
MAX_FWD_OUT_SIZE
=
448
TARGET_JIT_SIZE
=
128
MAX_JIT_SIZE
=
320
TARGET_FFEAT_SIZE
=
96
MAX_FFEAT_SIZE
=
256
def
_get_input_size
(
model
,
target
=
None
):
...
...
@@ -109,10 +113,10 @@ def test_model_default_cfgs(model_name, batch_size):
pool_size
=
cfg
[
'pool_size'
]
input_size
=
model
.
default_cfg
[
'input_size'
]
if
all
([
x
<=
MAX_FWD_
FEA
T_SIZE
for
x
in
input_size
])
and
\
if
all
([
x
<=
MAX_FWD_
OU
T_SIZE
for
x
in
input_size
])
and
\
not
any
([
fnmatch
.
fnmatch
(
model_name
,
x
)
for
x
in
EXCLUDE_FILTERS
]):
# output sizes only checked if default res <= 448 * 448 to keep resource down
input_size
=
tuple
([
min
(
x
,
MAX_FWD_
FEA
T_SIZE
)
for
x
in
input_size
])
input_size
=
tuple
([
min
(
x
,
MAX_FWD_
OU
T_SIZE
)
for
x
in
input_size
])
input_tensor
=
torch
.
randn
((
batch_size
,
*
input_size
))
# test forward_features (always unpooled)
...
...
@@ -176,8 +180,8 @@ def test_model_forward_torchscript(model_name, batch_size):
model
=
create_model
(
model_name
,
pretrained
=
False
)
model
.
eval
()
input_size
=
_get_input_size
(
model
,
128
)
if
max
(
input_size
)
>
MAX_
FWD
_SIZE
:
# NOTE using MAX_FWD_SIZE as the final limit is intentional
input_size
=
_get_input_size
(
model
,
TARGET_JIT_SIZE
)
if
max
(
input_size
)
>
MAX_
JIT
_SIZE
:
# NOTE using MAX_FWD_SIZE as the final limit is intentional
pytest
.
skip
(
"Fixed input size model > limit."
)
model
=
torch
.
jit
.
script
(
model
)
...
...
@@ -205,8 +209,8 @@ def test_model_forward_features(model_name, batch_size):
expected_channels
=
model
.
feature_info
.
channels
()
assert
len
(
expected_channels
)
>=
4
# all models here should have at least 4 feature levels by default, some 5 or 6
input_size
=
_get_input_size
(
model
,
96
)
# jit compile is already a bit slow and we've tested normal res already...
if
max
(
input_size
)
>
MAX_F
WD_SIZE
:
# NOTE using MAX_FWD_SIZE as the final limit is intentional
input_size
=
_get_input_size
(
model
,
TARGET_FFEAT_SIZE
)
if
max
(
input_size
)
>
MAX_F
FEAT_SIZE
:
pytest
.
skip
(
"Fixed input size model > limit."
)
outputs
=
model
(
torch
.
randn
((
batch_size
,
*
input_size
)))
...
...
timm/models/visformer.py
浏览文件 @
c4572cc5
...
...
@@ -33,7 +33,9 @@ def _cfg(url='', **kwargs):
default_cfgs
=
dict
(
visformer_tiny
=
_cfg
(),
visformer_small
=
_cfg
(),
visformer_small
=
_cfg
(
url
=
'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/visformer_small-839e1f5b.pth'
),
)
...
...
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