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1303affa
编写于
5月 13, 2021
作者:
W
Wei Shengyu
提交者:
GitHub
5月 13, 2021
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差异文件
Merge pull request #720 from lyuwenyu/hub_L_b
Release unnecessary dependent pkgs
上级
e8752538
72ab665b
变更
1
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1 changed file
with
808 addition
and
816 deletion
+808
-816
hubconf.py
hubconf.py
+808
-816
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hubconf.py
浏览文件 @
1303affa
...
...
@@ -12,21 +12,48 @@
# See the License for the specific language governing permissions and
# limitations under the License.
dependencies
=
[
'paddle'
,
'numpy'
]
dependencies
=
[
'paddle'
]
import
paddle
from
ppcls.modeling
import
architectures
import
os
import
sys
def
_load_pretrained_parameters
(
model
,
name
):
class
_SysPathG
(
object
):
"""
_SysPathG used to add/clean path for sys.path. Making sure minimal pkgs dependents by skiping parent dirs.
__enter__
add path into sys.path
__exit__
clean user's sys.path to avoid unexpect behaviors
"""
def
__init__
(
self
,
path
):
self
.
path
=
path
def
__enter__
(
self
,
):
sys
.
path
.
insert
(
0
,
self
.
path
)
def
__exit__
(
self
,
type
,
value
,
traceback
):
_p
=
sys
.
path
.
pop
(
0
)
assert
_p
==
self
.
path
,
'Make sure sys.path cleaning {} correctly.'
.
format
(
self
.
path
)
with
_SysPathG
(
os
.
path
.
join
(
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
)),
'ppcls'
,
'modeling'
)):
import
architectures
def
_load_pretrained_parameters
(
model
,
name
):
url
=
'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/{}_pretrained.pdparams'
.
format
(
name
)
path
=
paddle
.
utils
.
download
.
get_weights_path_from_url
(
url
)
model
.
set_state_dict
(
paddle
.
load
(
path
))
return
model
def
alexnet
(
pretrained
=
False
,
**
kwargs
):
def
alexnet
(
pretrained
=
False
,
**
kwargs
):
"""
AlexNet
Args:
...
...
@@ -42,8 +69,7 @@ def alexnet(pretrained=False, **kwargs):
return
model
def
vgg11
(
pretrained
=
False
,
**
kwargs
):
def
vgg11
(
pretrained
=
False
,
**
kwargs
):
"""
VGG11
Args:
...
...
@@ -60,8 +86,7 @@ def vgg11(pretrained=False, **kwargs):
return
model
def
vgg13
(
pretrained
=
False
,
**
kwargs
):
def
vgg13
(
pretrained
=
False
,
**
kwargs
):
"""
VGG13
Args:
...
...
@@ -78,8 +103,7 @@ def vgg13(pretrained=False, **kwargs):
return
model
def
vgg16
(
pretrained
=
False
,
**
kwargs
):
def
vgg16
(
pretrained
=
False
,
**
kwargs
):
"""
VGG16
Args:
...
...
@@ -96,8 +120,7 @@ def vgg16(pretrained=False, **kwargs):
return
model
def
vgg19
(
pretrained
=
False
,
**
kwargs
):
def
vgg19
(
pretrained
=
False
,
**
kwargs
):
"""
VGG19
Args:
...
...
@@ -114,8 +137,7 @@ def vgg19(pretrained=False, **kwargs):
return
model
def
resnet18
(
pretrained
=
False
,
**
kwargs
):
def
resnet18
(
pretrained
=
False
,
**
kwargs
):
"""
ResNet18
Args:
...
...
@@ -133,8 +155,7 @@ def resnet18(pretrained=False, **kwargs):
return
model
def
resnet34
(
pretrained
=
False
,
**
kwargs
):
def
resnet34
(
pretrained
=
False
,
**
kwargs
):
"""
ResNet34
Args:
...
...
@@ -152,8 +173,7 @@ def resnet34(pretrained=False, **kwargs):
return
model
def
resnet50
(
pretrained
=
False
,
**
kwargs
):
def
resnet50
(
pretrained
=
False
,
**
kwargs
):
"""
ResNet50
Args:
...
...
@@ -171,8 +191,7 @@ def resnet50(pretrained=False, **kwargs):
return
model
def
resnet101
(
pretrained
=
False
,
**
kwargs
):
def
resnet101
(
pretrained
=
False
,
**
kwargs
):
"""
ResNet101
Args:
...
...
@@ -190,8 +209,7 @@ def resnet101(pretrained=False, **kwargs):
return
model
def
resnet152
(
pretrained
=
False
,
**
kwargs
):
def
resnet152
(
pretrained
=
False
,
**
kwargs
):
"""
ResNet152
Args:
...
...
@@ -209,8 +227,7 @@ def resnet152(pretrained=False, **kwargs):
return
model
def
squeezenet1_0
(
pretrained
=
False
,
**
kwargs
):
def
squeezenet1_0
(
pretrained
=
False
,
**
kwargs
):
"""
SqueezeNet1_0
Args:
...
...
@@ -226,8 +243,7 @@ def squeezenet1_0(pretrained=False, **kwargs):
return
model
def
squeezenet1_1
(
pretrained
=
False
,
**
kwargs
):
def
squeezenet1_1
(
pretrained
=
False
,
**
kwargs
):
"""
SqueezeNet1_1
Args:
...
...
@@ -243,8 +259,7 @@ def squeezenet1_1(pretrained=False, **kwargs):
return
model
def
densenet121
(
pretrained
=
False
,
**
kwargs
):
def
densenet121
(
pretrained
=
False
,
**
kwargs
):
"""
DenseNet121
Args:
...
...
@@ -262,8 +277,7 @@ def densenet121(pretrained=False, **kwargs):
return
model
def
densenet161
(
pretrained
=
False
,
**
kwargs
):
def
densenet161
(
pretrained
=
False
,
**
kwargs
):
"""
DenseNet161
Args:
...
...
@@ -281,8 +295,7 @@ def densenet161(pretrained=False, **kwargs):
return
model
def
densenet169
(
pretrained
=
False
,
**
kwargs
):
def
densenet169
(
pretrained
=
False
,
**
kwargs
):
"""
DenseNet169
Args:
...
...
@@ -300,8 +313,7 @@ def densenet169(pretrained=False, **kwargs):
return
model
def
densenet201
(
pretrained
=
False
,
**
kwargs
):
def
densenet201
(
pretrained
=
False
,
**
kwargs
):
"""
DenseNet201
Args:
...
...
@@ -319,8 +331,7 @@ def densenet201(pretrained=False, **kwargs):
return
model
def
densenet264
(
pretrained
=
False
,
**
kwargs
):
def
densenet264
(
pretrained
=
False
,
**
kwargs
):
"""
DenseNet264
Args:
...
...
@@ -338,8 +349,7 @@ def densenet264(pretrained=False, **kwargs):
return
model
def
inceptionv3
(
pretrained
=
False
,
**
kwargs
):
def
inceptionv3
(
pretrained
=
False
,
**
kwargs
):
"""
InceptionV3
Args:
...
...
@@ -355,8 +365,7 @@ def inceptionv3(pretrained=False, **kwargs):
return
model
def
inceptionv4
(
pretrained
=
False
,
**
kwargs
):
def
inceptionv4
(
pretrained
=
False
,
**
kwargs
):
"""
InceptionV4
Args:
...
...
@@ -372,8 +381,7 @@ def inceptionv4(pretrained=False, **kwargs):
return
model
def
googlenet
(
pretrained
=
False
,
**
kwargs
):
def
googlenet
(
pretrained
=
False
,
**
kwargs
):
"""
GoogLeNet
Args:
...
...
@@ -389,8 +397,7 @@ def googlenet(pretrained=False, **kwargs):
return
model
def
shufflenetv2_x0_25
(
pretrained
=
False
,
**
kwargs
):
def
shufflenetv2_x0_25
(
pretrained
=
False
,
**
kwargs
):
"""
ShuffleNetV2_x0_25
Args:
...
...
@@ -406,8 +413,7 @@ def shufflenetv2_x0_25(pretrained=False, **kwargs):
return
model
def
mobilenetv1
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv1
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV1
Args:
...
...
@@ -423,8 +429,7 @@ def mobilenetv1(pretrained=False, **kwargs):
return
model
def
mobilenetv1_x0_25
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv1_x0_25
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV1_x0_25
Args:
...
...
@@ -440,8 +445,7 @@ def mobilenetv1_x0_25(pretrained=False, **kwargs):
return
model
def
mobilenetv1_x0_5
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv1_x0_5
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV1_x0_5
Args:
...
...
@@ -457,8 +461,7 @@ def mobilenetv1_x0_5(pretrained=False, **kwargs):
return
model
def
mobilenetv1_x0_75
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv1_x0_75
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV1_x0_75
Args:
...
...
@@ -474,8 +477,7 @@ def mobilenetv1_x0_75(pretrained=False, **kwargs):
return
model
def
mobilenetv2_x0_25
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv2_x0_25
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV2_x0_25
Args:
...
...
@@ -491,8 +493,7 @@ def mobilenetv2_x0_25(pretrained=False, **kwargs):
return
model
def
mobilenetv2_x0_5
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv2_x0_5
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV2_x0_5
Args:
...
...
@@ -508,8 +509,7 @@ def mobilenetv2_x0_5(pretrained=False, **kwargs):
return
model
def
mobilenetv2_x0_75
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv2_x0_75
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV2_x0_75
Args:
...
...
@@ -525,8 +525,7 @@ def mobilenetv2_x0_75(pretrained=False, **kwargs):
return
model
def
mobilenetv2_x1_5
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv2_x1_5
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV2_x1_5
Args:
...
...
@@ -542,8 +541,7 @@ def mobilenetv2_x1_5(pretrained=False, **kwargs):
return
model
def
mobilenetv2_x2_0
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv2_x2_0
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV2_x2_0
Args:
...
...
@@ -559,8 +557,7 @@ def mobilenetv2_x2_0(pretrained=False, **kwargs):
return
model
def
mobilenetv3_large_x0_35
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv3_large_x0_35
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV3_large_x0_35
Args:
...
...
@@ -572,12 +569,12 @@ def mobilenetv3_large_x0_35(pretrained=False, **kwargs):
"""
model
=
architectures
.
MobileNetV3_large_x0_35
(
**
kwargs
)
if
pretrained
:
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_large_x0_35'
)
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_large_x0_35'
)
return
model
def
mobilenetv3_large_x0_5
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv3_large_x0_5
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV3_large_x0_5
Args:
...
...
@@ -589,12 +586,12 @@ def mobilenetv3_large_x0_5(pretrained=False, **kwargs):
"""
model
=
architectures
.
MobileNetV3_large_x0_5
(
**
kwargs
)
if
pretrained
:
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_large_x0_5'
)
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_large_x0_5'
)
return
model
def
mobilenetv3_large_x0_75
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv3_large_x0_75
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV3_large_x0_75
Args:
...
...
@@ -606,12 +603,12 @@ def mobilenetv3_large_x0_75(pretrained=False, **kwargs):
"""
model
=
architectures
.
MobileNetV3_large_x0_75
(
**
kwargs
)
if
pretrained
:
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_large_x0_75'
)
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_large_x0_75'
)
return
model
def
mobilenetv3_large_x1_0
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv3_large_x1_0
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV3_large_x1_0
Args:
...
...
@@ -623,12 +620,12 @@ def mobilenetv3_large_x1_0(pretrained=False, **kwargs):
"""
model
=
architectures
.
MobileNetV3_large_x1_0
(
**
kwargs
)
if
pretrained
:
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_large_x1_0'
)
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_large_x1_0'
)
return
model
def
mobilenetv3_large_x1_25
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv3_large_x1_25
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV3_large_x1_25
Args:
...
...
@@ -640,12 +637,12 @@ def mobilenetv3_large_x1_25(pretrained=False, **kwargs):
"""
model
=
architectures
.
MobileNetV3_large_x1_25
(
**
kwargs
)
if
pretrained
:
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_large_x1_25'
)
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_large_x1_25'
)
return
model
def
mobilenetv3_small_x0_35
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv3_small_x0_35
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV3_small_x0_35
Args:
...
...
@@ -657,12 +654,12 @@ def mobilenetv3_small_x0_35(pretrained=False, **kwargs):
"""
model
=
architectures
.
MobileNetV3_small_x0_35
(
**
kwargs
)
if
pretrained
:
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_small_x0_35'
)
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_small_x0_35'
)
return
model
def
mobilenetv3_small_x0_5
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv3_small_x0_5
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV3_small_x0_5
Args:
...
...
@@ -674,12 +671,12 @@ def mobilenetv3_small_x0_5(pretrained=False, **kwargs):
"""
model
=
architectures
.
MobileNetV3_small_x0_5
(
**
kwargs
)
if
pretrained
:
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_small_x0_5'
)
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_small_x0_5'
)
return
model
def
mobilenetv3_small_x0_75
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv3_small_x0_75
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV3_small_x0_75
Args:
...
...
@@ -691,12 +688,12 @@ def mobilenetv3_small_x0_75(pretrained=False, **kwargs):
"""
model
=
architectures
.
MobileNetV3_small_x0_75
(
**
kwargs
)
if
pretrained
:
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_small_x0_75'
)
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_small_x0_75'
)
return
model
def
mobilenetv3_small_x1_0
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv3_small_x1_0
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV3_small_x1_0
Args:
...
...
@@ -708,12 +705,12 @@ def mobilenetv3_small_x1_0(pretrained=False, **kwargs):
"""
model
=
architectures
.
MobileNetV3_small_x1_0
(
**
kwargs
)
if
pretrained
:
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_small_x1_0'
)
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_small_x1_0'
)
return
model
def
mobilenetv3_small_x1_25
(
pretrained
=
False
,
**
kwargs
):
def
mobilenetv3_small_x1_25
(
pretrained
=
False
,
**
kwargs
):
"""
MobileNetV3_small_x1_25
Args:
...
...
@@ -725,12 +722,12 @@ def mobilenetv3_small_x1_25(pretrained=False, **kwargs):
"""
model
=
architectures
.
MobileNetV3_small_x1_25
(
**
kwargs
)
if
pretrained
:
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_small_x1_25'
)
model
=
_load_pretrained_parameters
(
model
,
'MobileNetV3_small_x1_25'
)
return
model
def
resnext101_32x4d
(
pretrained
=
False
,
**
kwargs
):
def
resnext101_32x4d
(
pretrained
=
False
,
**
kwargs
):
"""
ResNeXt101_32x4d
Args:
...
...
@@ -746,8 +743,7 @@ def resnext101_32x4d(pretrained=False, **kwargs):
return
model
def
resnext101_64x4d
(
pretrained
=
False
,
**
kwargs
):
def
resnext101_64x4d
(
pretrained
=
False
,
**
kwargs
):
"""
ResNeXt101_64x4d
Args:
...
...
@@ -763,8 +759,7 @@ def resnext101_64x4d(pretrained=False, **kwargs):
return
model
def
resnext152_32x4d
(
pretrained
=
False
,
**
kwargs
):
def
resnext152_32x4d
(
pretrained
=
False
,
**
kwargs
):
"""
ResNeXt152_32x4d
Args:
...
...
@@ -780,8 +775,7 @@ def resnext152_32x4d(pretrained=False, **kwargs):
return
model
def
resnext152_64x4d
(
pretrained
=
False
,
**
kwargs
):
def
resnext152_64x4d
(
pretrained
=
False
,
**
kwargs
):
"""
ResNeXt152_64x4d
Args:
...
...
@@ -797,8 +791,7 @@ def resnext152_64x4d(pretrained=False, **kwargs):
return
model
def
resnext50_32x4d
(
pretrained
=
False
,
**
kwargs
):
def
resnext50_32x4d
(
pretrained
=
False
,
**
kwargs
):
"""
ResNeXt50_32x4d
Args:
...
...
@@ -814,8 +807,7 @@ def resnext50_32x4d(pretrained=False, **kwargs):
return
model
def
resnext50_64x4d
(
pretrained
=
False
,
**
kwargs
):
def
resnext50_64x4d
(
pretrained
=
False
,
**
kwargs
):
"""
ResNeXt50_64x4d
Args:
...
...
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