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提交 d8def846 编写于 作者: C cuicheng01
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ppcls/arch/backbone/__init__.py
浏览文件 @ d8def846
...@@ -19,11 +19,12 @@ from ppcls.arch.backbone.legendary_models.vgg import VGG11, VGG13, VGG16, VGG19 ...@@ -19,11 +19,12 @@ from ppcls.arch.backbone.legendary_models.vgg import VGG11, VGG13, VGG16, VGG19
from ppcls.arch.backbone.legendary_models.inception_v3 import InceptionV3 from ppcls.arch.backbone.legendary_models.inception_v3 import InceptionV3
from ppcls.arch.backbone.legendary_models.hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W64_C from ppcls.arch.backbone.legendary_models.hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W64_C
from ppcls.arch.backbone.model_zoo.resnet_vc import ResNet18_vc, ResNet34_vc, ResNet50_vc, ResNet101_vc, ResNet152_vc from ppcls.arch.backbone.model_zoo.resnet_vc import ResNet50_vc
from ppcls.arch.backbone.model_zoo.resnext import ResNeXt50_32x4d, ResNeXt50_64x4d, ResNeXt101_32x4d, ResNeXt101_64x4d, ResNeXt152_32x4d, ResNeXt152_64x4d from ppcls.arch.backbone.model_zoo.resnext import ResNeXt50_32x4d, ResNeXt50_64x4d, ResNeXt101_32x4d, ResNeXt101_64x4d, ResNeXt152_32x4d, ResNeXt152_64x4d
from ppcls.arch.backbone.model_zoo.res2net import Res2Net50_48w_2s, Res2Net50_26w_4s, Res2Net50_14w_8s, Res2Net50_48w_2s, Res2Net50_26w_6s, Res2Net50_26w_8s, Res2Net101_26w_4s, Res2Net152_26w_4s, Res2Net200_26w_4s from ppcls.arch.backbone.model_zoo.resnext_vd import ResNeXt50_vd_32x4d, ResNeXt50_vd_64x4d, ResNeXt101_vd_32x4d, ResNeXt101_vd_64x4d, ResNeXt152_vd_32x4d, ResNeXt152_vd_64x4d
from ppcls.arch.backbone.model_zoo.res2net_vd import Res2Net50_vd_48w_2s, Res2Net50_vd_26w_4s, Res2Net50_vd_14w_8s, Res2Net50_vd_48w_2s, Res2Net50_vd_26w_6s, Res2Net50_vd_26w_8s, Res2Net101_vd_26w_4s, Res2Net152_vd_26w_4s, Res2Net200_vd_26w_4s from ppcls.arch.backbone.model_zoo.res2net import Res2Net50_26w_4s, Res2Net50_14w_8s
from ppcls.arch.backbone.model_zoo.se_resnet_vd import SE_ResNet18_vd, SE_ResNet34_vd, SE_ResNet50_vd, SE_ResNet101_vd, SE_ResNet152_vd, SE_ResNet200_vd from ppcls.arch.backbone.model_zoo.res2net_vd import Res2Net50_vd_26w_4s, Res2Net101_vd_26w_4s, Res2Net200_vd_26w_4s
from ppcls.arch.backbone.model_zoo.se_resnet_vd import SE_ResNet18_vd, SE_ResNet34_vd, SE_ResNet50_vd
from ppcls.arch.backbone.model_zoo.se_resnext_vd import SE_ResNeXt50_vd_32x4d, SE_ResNeXt50_vd_32x4d, SENet154_vd from ppcls.arch.backbone.model_zoo.se_resnext_vd import SE_ResNeXt50_vd_32x4d, SE_ResNeXt50_vd_32x4d, SENet154_vd
from ppcls.arch.backbone.model_zoo.se_resnext import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_64x4d from ppcls.arch.backbone.model_zoo.se_resnext import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_64x4d
from ppcls.arch.backbone.model_zoo.dpn import DPN68, DPN92, DPN98, DPN107, DPN131 from ppcls.arch.backbone.model_zoo.dpn import DPN68, DPN92, DPN98, DPN107, DPN131
...@@ -33,10 +34,11 @@ from ppcls.arch.backbone.model_zoo.resnest import ResNeSt50_fast_1s1x64d, ResNeS ...@@ -33,10 +34,11 @@ from ppcls.arch.backbone.model_zoo.resnest import ResNeSt50_fast_1s1x64d, ResNeS
from ppcls.arch.backbone.model_zoo.googlenet import GoogLeNet from ppcls.arch.backbone.model_zoo.googlenet import GoogLeNet
from ppcls.arch.backbone.model_zoo.mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x0_75, MobileNetV2, MobileNetV2_x1_5, MobileNetV2_x2_0 from ppcls.arch.backbone.model_zoo.mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x0_75, MobileNetV2, MobileNetV2_x1_5, MobileNetV2_x2_0
from ppcls.arch.backbone.model_zoo.shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2_swish from ppcls.arch.backbone.model_zoo.shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2_swish
from ppcls.arch.backbone.model_zoo.ghostnet import GhostNet_x0_5, GhostNet_x1_0, GhostNet_x1_3
from ppcls.arch.backbone.model_zoo.alexnet import AlexNet from ppcls.arch.backbone.model_zoo.alexnet import AlexNet
from ppcls.arch.backbone.model_zoo.inception_v4 import InceptionV4 from ppcls.arch.backbone.model_zoo.inception_v4 import InceptionV4
from ppcls.arch.backbone.model_zoo.xception import Xception41, Xception65, Xception71 from ppcls.arch.backbone.model_zoo.xception import Xception41, Xception65, Xception71
from ppcls.arch.backbone.model_zoo.xception_deeplab import Xception41_deeplab, Xception65_deeplab, Xception71_deeplab from ppcls.arch.backbone.model_zoo.xception_deeplab import Xception41_deeplab, Xception65_deeplab
from ppcls.arch.backbone.model_zoo.resnext101_wsl import ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl from ppcls.arch.backbone.model_zoo.resnext101_wsl import ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl
from ppcls.arch.backbone.model_zoo.squeezenet import SqueezeNet1_0, SqueezeNet1_1 from ppcls.arch.backbone.model_zoo.squeezenet import SqueezeNet1_0, SqueezeNet1_1
from ppcls.arch.backbone.model_zoo.darknet import DarkNet53 from ppcls.arch.backbone.model_zoo.darknet import DarkNet53
......
ppcls/arch/backbone/model_zoo/alexnet.py
浏览文件 @ d8def846
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
import paddle import paddle
from paddle import ParamAttr from paddle import ParamAttr
import paddle.nn as nn import paddle.nn as nn
...@@ -7,8 +21,11 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D ...@@ -7,8 +21,11 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform from paddle.nn.initializer import Uniform
import math import math
__all__ = ["AlexNet"] from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"AlexNet": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams"}
__all__ = list(MODEL_URLS.keys())
class ConvPoolLayer(nn.Layer): class ConvPoolLayer(nn.Layer):
def __init__(self, def __init__(self,
...@@ -126,7 +143,19 @@ class AlexNetDY(nn.Layer): ...@@ -126,7 +143,19 @@ class AlexNetDY(nn.Layer):
x = self._fc8(x) x = self._fc8(x)
return x return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def AlexNet(**args): def AlexNet(pretrained=False, use_ssld=False, **kwargs):
model = AlexNetDY(**args) model = AlexNetDY(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["AlexNet"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/darknet.py
浏览文件 @ d8def846
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
import paddle import paddle
from paddle import ParamAttr from paddle import ParamAttr
import paddle.nn as nn import paddle.nn as nn
...@@ -7,8 +21,11 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D ...@@ -7,8 +21,11 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform from paddle.nn.initializer import Uniform
import math import math
__all__ = ["DarkNet53"] from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"DarkNet53": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams"}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
def __init__(self, def __init__(self,
...@@ -155,7 +172,19 @@ class DarkNet(nn.Layer): ...@@ -155,7 +172,19 @@ class DarkNet(nn.Layer):
x = self._out(x) x = self._out(x)
return x return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def DarkNet53(**args): if pretrained is False:
model = DarkNet(**args) pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def DarkNet53(pretrained=False, use_ssld=False, **kwargs):
model = DarkNet(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DarkNet53"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/densenet.py
浏览文件 @ d8def846
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. # you may not use this file except in compliance with the License.
...@@ -26,9 +26,16 @@ from paddle.nn.initializer import Uniform ...@@ -26,9 +26,16 @@ from paddle.nn.initializer import Uniform
import math import math
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"DenseNet121", "DenseNet161", "DenseNet169", "DenseNet201", "DenseNet264"
] MODEL_URLS = {"DenseNet121": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet121_pretrained.pdparams",
"DenseNet161": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet161_pretrained.pdparams",
"DenseNet169": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet169_pretrained.pdparams",
"DenseNet201": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet201_pretrained.pdparams",
"DenseNet264": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet264_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class BNACConvLayer(nn.Layer): class BNACConvLayer(nn.Layer):
...@@ -282,27 +289,43 @@ class DenseNet(nn.Layer): ...@@ -282,27 +289,43 @@ class DenseNet(nn.Layer):
y = self.out(y) y = self.out(y)
return y return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def DenseNet121(**args): if pretrained is False:
model = DenseNet(layers=121, **args) pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def DenseNet121(pretrained=False, use_ssld=False, **kwargs):
model = DenseNet(layers=121, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DenseNet121"], use_ssld=use_ssld)
return model return model
def DenseNet161(**args): def DenseNet161(pretrained=False, use_ssld=False, **kwargs):
model = DenseNet(layers=161, **args) model = DenseNet(layers=161, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DenseNet161"], use_ssld=use_ssld)
return model return model
def DenseNet169(**args): def DenseNet169(pretrained=False, use_ssld=False, **kwargs):
model = DenseNet(layers=169, **args) model = DenseNet(layers=169, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DenseNet169"], use_ssld=use_ssld)
return model return model
def DenseNet201(**args): def DenseNet201(pretrained=False, use_ssld=False, **kwargs):
model = DenseNet(layers=201, **args) model = DenseNet(layers=201, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DenseNet201"], use_ssld=use_ssld)
return model return model
def DenseNet264(**args): def DenseNet264(pretrained=False, use_ssld=False, **kwargs):
model = DenseNet(layers=264, **args) model = DenseNet(layers=264, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DenseNet264"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/distilled_vision_transformer.py
浏览文件 @ d8def846
...@@ -16,12 +16,20 @@ import paddle ...@@ -16,12 +16,20 @@ import paddle
import paddle.nn as nn import paddle.nn as nn
from .vision_transformer import VisionTransformer, Identity, trunc_normal_, zeros_ from .vision_transformer import VisionTransformer, Identity, trunc_normal_, zeros_
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
'DeiT_tiny_patch16_224', 'DeiT_small_patch16_224', 'DeiT_base_patch16_224',
'DeiT_tiny_distilled_patch16_224', 'DeiT_small_distilled_patch16_224', MODEL_URLS = {
'DeiT_base_distilled_patch16_224', 'DeiT_base_patch16_384', "DeiT_tiny_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams",
'DeiT_base_distilled_patch16_384' "DeiT_small_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_patch16_224_pretrained.pdparams",
] "DeiT_base_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_224_pretrained.pdparams",
"DeiT_tiny_distilled_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_distilled_patch16_224_pretrained.pdparams",
"DeiT_small_distilled_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_distilled_patch16_224_pretrained.pdparams",
"DeiT_base_distilled_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_224_pretrained.pdparams",
"DeiT_base_patch16_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_384_pretrained.pdparams",
"DeiT_base_distilled_patch16_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_distilled_patch16_384_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class DistilledVisionTransformer(VisionTransformer): class DistilledVisionTransformer(VisionTransformer):
...@@ -90,7 +98,20 @@ class DistilledVisionTransformer(VisionTransformer): ...@@ -90,7 +98,20 @@ class DistilledVisionTransformer(VisionTransformer):
return (x + x_dist) / 2 return (x + x_dist) / 2
def DeiT_tiny_patch16_224(**kwargs): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def DeiT_tiny_patch16_224(pretrained=False, use_ssld=False, **kwargs):
model = VisionTransformer( model = VisionTransformer(
patch_size=16, patch_size=16,
embed_dim=192, embed_dim=192,
...@@ -100,10 +121,11 @@ def DeiT_tiny_patch16_224(**kwargs): ...@@ -100,10 +121,11 @@ def DeiT_tiny_patch16_224(**kwargs):
qkv_bias=True, qkv_bias=True,
epsilon=1e-6, epsilon=1e-6,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_tiny_patch16_224"], use_ssld=use_ssld)
return model return model
def DeiT_small_patch16_224(**kwargs): def DeiT_small_patch16_224(pretrained=False, use_ssld=False, **kwargs):
model = VisionTransformer( model = VisionTransformer(
patch_size=16, patch_size=16,
embed_dim=384, embed_dim=384,
...@@ -113,10 +135,11 @@ def DeiT_small_patch16_224(**kwargs): ...@@ -113,10 +135,11 @@ def DeiT_small_patch16_224(**kwargs):
qkv_bias=True, qkv_bias=True,
epsilon=1e-6, epsilon=1e-6,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_small_patch16_224"], use_ssld=use_ssld)
return model return model
def DeiT_base_patch16_224(**kwargs): def DeiT_base_patch16_224(pretrained=False, use_ssld=False, **kwargs):
model = VisionTransformer( model = VisionTransformer(
patch_size=16, patch_size=16,
embed_dim=768, embed_dim=768,
...@@ -126,10 +149,11 @@ def DeiT_base_patch16_224(**kwargs): ...@@ -126,10 +149,11 @@ def DeiT_base_patch16_224(**kwargs):
qkv_bias=True, qkv_bias=True,
epsilon=1e-6, epsilon=1e-6,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_base_patch16_224"], use_ssld=use_ssld)
return model return model
def DeiT_tiny_distilled_patch16_224(**kwargs): def DeiT_tiny_distilled_patch16_224(pretrained=False, use_ssld=False, **kwargs):
model = DistilledVisionTransformer( model = DistilledVisionTransformer(
patch_size=16, patch_size=16,
embed_dim=192, embed_dim=192,
...@@ -139,10 +163,11 @@ def DeiT_tiny_distilled_patch16_224(**kwargs): ...@@ -139,10 +163,11 @@ def DeiT_tiny_distilled_patch16_224(**kwargs):
qkv_bias=True, qkv_bias=True,
epsilon=1e-6, epsilon=1e-6,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_tiny_distilled_patch16_224"], use_ssld=use_ssld)
return model return model
def DeiT_small_distilled_patch16_224(**kwargs): def DeiT_small_distilled_patch16_224(pretrained=False, use_ssld=False, **kwargs):
model = DistilledVisionTransformer( model = DistilledVisionTransformer(
patch_size=16, patch_size=16,
embed_dim=384, embed_dim=384,
...@@ -152,10 +177,11 @@ def DeiT_small_distilled_patch16_224(**kwargs): ...@@ -152,10 +177,11 @@ def DeiT_small_distilled_patch16_224(**kwargs):
qkv_bias=True, qkv_bias=True,
epsilon=1e-6, epsilon=1e-6,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_small_distilled_patch16_224"], use_ssld=use_ssld)
return model return model
def DeiT_base_distilled_patch16_224(**kwargs): def DeiT_base_distilled_patch16_224(pretrained=False, use_ssld=False, **kwargs):
model = DistilledVisionTransformer( model = DistilledVisionTransformer(
patch_size=16, patch_size=16,
embed_dim=768, embed_dim=768,
...@@ -165,10 +191,11 @@ def DeiT_base_distilled_patch16_224(**kwargs): ...@@ -165,10 +191,11 @@ def DeiT_base_distilled_patch16_224(**kwargs):
qkv_bias=True, qkv_bias=True,
epsilon=1e-6, epsilon=1e-6,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_base_distilled_patch16_224"], use_ssld=use_ssld)
return model return model
def DeiT_base_patch16_384(**kwargs): def DeiT_base_patch16_384(pretrained=False, use_ssld=False, **kwargs):
model = VisionTransformer( model = VisionTransformer(
img_size=384, img_size=384,
patch_size=16, patch_size=16,
...@@ -179,10 +206,11 @@ def DeiT_base_patch16_384(**kwargs): ...@@ -179,10 +206,11 @@ def DeiT_base_patch16_384(**kwargs):
qkv_bias=True, qkv_bias=True,
epsilon=1e-6, epsilon=1e-6,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_base_patch16_384"], use_ssld=use_ssld)
return model return model
def DeiT_base_distilled_patch16_384(**kwargs): def DeiT_base_distilled_patch16_384(pretrained=False, use_ssld=False, **kwargs):
model = DistilledVisionTransformer( model = DistilledVisionTransformer(
img_size=384, img_size=384,
patch_size=16, patch_size=16,
...@@ -193,4 +221,5 @@ def DeiT_base_distilled_patch16_384(**kwargs): ...@@ -193,4 +221,5 @@ def DeiT_base_distilled_patch16_384(**kwargs):
qkv_bias=True, qkv_bias=True,
epsilon=1e-6, epsilon=1e-6,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_base_distilled_patch16_384"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/dla.py
浏览文件 @ d8def846
...@@ -26,25 +26,25 @@ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_f ...@@ -26,25 +26,25 @@ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_f
MODEL_URLS = { MODEL_URLS = {
"DLA34": "DLA34":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/DLA34_pretrained.pdparams", "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA34_pretrained.pdparams",
"DLA46_c": "DLA46_c":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/DLA46_c_pretrained.pdparams", "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA46_c_pretrained.pdparams",
"DLA46x_c": "DLA46x_c":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/DLA46x_c_pretrained.pdparams", "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA46x_c_pretrained.pdparams",
"DLA60": "DLA60":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/DLA60_pretrained.pdparams", "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60_pretrained.pdparams",
"DLA60x": "DLA60x":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/DLA60x_pretrained.pdparams", "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_pretrained.pdparams",
"DLA60x_c": "DLA60x_c":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/DLA60x_c_pretrained.pdparams", "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_c_pretrained.pdparams",
"DLA102": "DLA102":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/DLA102_pretrained.pdparams", "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102_pretrained.pdparams",
"DLA102x": "DLA102x":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/DLA102x_pretrained.pdparams", "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x_pretrained.pdparams",
"DLA102x2": "DLA102x2":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/DLA102x2_pretrained.pdparams", "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x2_pretrained.pdparams",
"DLA169": "DLA169":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/DLA169_pretrained.pdparams" "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA169_pretrained.pdparams"
} }
......
ppcls/arch/backbone/model_zoo/dpn.py
浏览文件 @ d8def846
...@@ -27,14 +27,16 @@ from paddle.nn.initializer import Uniform ...@@ -27,14 +27,16 @@ from paddle.nn.initializer import Uniform
import math import math
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"DPN",
"DPN68", MODEL_URLS = {"DPN68": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN68_pretrained.pdparams",
"DPN92", "DPN92": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN92_pretrained.pdparams",
"DPN98", "DPN98": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN98_pretrained.pdparams",
"DPN107", "DPN107": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN107_pretrained.pdparams",
"DPN131", "DPN131": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN131_pretrained.pdparams",
] }
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -398,28 +400,45 @@ class DPN(nn.Layer): ...@@ -398,28 +400,45 @@ class DPN(nn.Layer):
net_arg['init_padding'] = init_padding net_arg['init_padding'] = init_padding
return net_arg return net_arg
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def DPN68(**args): if pretrained is False:
model = DPN(layers=68, **args) pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def DPN68(pretrained=False, use_ssld=False, **kwargs):
model = DPN(layers=68, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DPN68"])
return model return model
def DPN92(**args): def DPN92(pretrained=False, use_ssld=False, **kwargs):
model = DPN(layers=92, **args) model = DPN(layers=92, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DPN92"])
return model return model
def DPN98(**args): def DPN98(pretrained=False, use_ssld=False, **kwargs):
model = DPN(layers=98, **args) model = DPN(layers=98, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DPN98"])
return model return model
def DPN107(**args): def DPN107(pretrained=False, use_ssld=False, **kwargs):
model = DPN(layers=107, **args) model = DPN(layers=107, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DPN107"])
return model return model
def DPN131(**args): def DPN131(pretrained=False, use_ssld=False, **kwargs):
model = DPN(layers=131, **args) model = DPN(layers=131, **kwargs)
return model _load_pretrained(pretrained, model, MODEL_URLS["DPN131"])
return model
\ No newline at end of file
ppcls/arch/backbone/model_zoo/efficientnet.py
浏览文件 @ d8def846
...@@ -9,11 +9,20 @@ import collections ...@@ -9,11 +9,20 @@ import collections
import re import re
import copy import copy
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
'EfficientNet', 'EfficientNetB0_small', 'EfficientNetB0', 'EfficientNetB1',
'EfficientNetB2', 'EfficientNetB3', 'EfficientNetB4', 'EfficientNetB5', MODEL_URLS = {"EfficientNetB0_small": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_small_pretrained.pdparams",
'EfficientNetB6', 'EfficientNetB7' "EfficientNetB0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_pretrained.pdparams",
] "EfficientNetB1": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB1_pretrained.pdparams",
"EfficientNetB2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB2_pretrained.pdparams",
"EfficientNetB3": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB3_pretrained.pdparams",
"EfficientNetB4": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB4_pretrained.pdparams",
"EfficientNetB5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB5_pretrained.pdparams",
"EfficientNetB6": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB6_pretrained.pdparams",
"EfficientNetB7": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB7_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
GlobalParams = collections.namedtuple('GlobalParams', [ GlobalParams = collections.namedtuple('GlobalParams', [
'batch_norm_momentum', 'batch_norm_momentum',
...@@ -783,119 +792,159 @@ class EfficientNet(nn.Layer): ...@@ -783,119 +792,159 @@ class EfficientNet(nn.Layer):
x = self._fc(x) x = self._fc(x)
return x return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def EfficientNetB0_small(padding_type='DYNAMIC', def EfficientNetB0_small(padding_type='DYNAMIC',
override_params=None, override_params=None,
use_se=False, use_se=False,
**args): pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet( model = EfficientNet(
name='b0', name='b0',
padding_type=padding_type, padding_type=padding_type,
override_params=override_params, override_params=override_params,
use_se=use_se, use_se=use_se,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB0_small"])
return model return model
def EfficientNetB0(padding_type='SAME', def EfficientNetB0(padding_type='SAME',
override_params=None, override_params=None,
use_se=True, use_se=True,
**args): pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet( model = EfficientNet(
name='b0', name='b0',
padding_type=padding_type, padding_type=padding_type,
override_params=override_params, override_params=override_params,
use_se=use_se, use_se=use_se,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB0"])
return model return model
def EfficientNetB1(padding_type='SAME', def EfficientNetB1(padding_type='SAME',
override_params=None, override_params=None,
use_se=True, use_se=True,
**args): pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet( model = EfficientNet(
name='b1', name='b1',
padding_type=padding_type, padding_type=padding_type,
override_params=override_params, override_params=override_params,
use_se=use_se, use_se=use_se,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB1"])
return model return model
def EfficientNetB2(padding_type='SAME', def EfficientNetB2(padding_type='SAME',
override_params=None, override_params=None,
use_se=True, use_se=True,
**args): pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet( model = EfficientNet(
name='b2', name='b2',
padding_type=padding_type, padding_type=padding_type,
override_params=override_params, override_params=override_params,
use_se=use_se, use_se=use_se,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB2"])
return model return model
def EfficientNetB3(padding_type='SAME', def EfficientNetB3(padding_type='SAME',
override_params=None, override_params=None,
use_se=True, use_se=True,
**args): pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet( model = EfficientNet(
name='b3', name='b3',
padding_type=padding_type, padding_type=padding_type,
override_params=override_params, override_params=override_params,
use_se=use_se, use_se=use_se,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB3"])
return model return model
def EfficientNetB4(padding_type='SAME', def EfficientNetB4(padding_type='SAME',
override_params=None, override_params=None,
use_se=True, use_se=True,
**args): pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet( model = EfficientNet(
name='b4', name='b4',
padding_type=padding_type, padding_type=padding_type,
override_params=override_params, override_params=override_params,
use_se=use_se, use_se=use_se,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB4"])
return model return model
def EfficientNetB5(padding_type='SAME', def EfficientNetB5(padding_type='SAME',
override_params=None, override_params=None,
use_se=True, use_se=True,
**args): pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet( model = EfficientNet(
name='b5', name='b5',
padding_type=padding_type, padding_type=padding_type,
override_params=override_params, override_params=override_params,
use_se=use_se, use_se=use_se,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB5"])
return model return model
def EfficientNetB6(padding_type='SAME', def EfficientNetB6(padding_type='SAME',
override_params=None, override_params=None,
use_se=True, use_se=True,
**args): pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet( model = EfficientNet(
name='b6', name='b6',
padding_type=padding_type, padding_type=padding_type,
override_params=override_params, override_params=override_params,
use_se=use_se, use_se=use_se,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB6"])
return model return model
def EfficientNetB7(padding_type='SAME', def EfficientNetB7(padding_type='SAME',
override_params=None, override_params=None,
use_se=True, use_se=True,
**args): pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet( model = EfficientNet(
name='b7', name='b7',
padding_type=padding_type, padding_type=padding_type,
override_params=override_params, override_params=override_params,
use_se=use_se, use_se=use_se,
**args) **kwargs)
return model _load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB7"])
return model
\ No newline at end of file
ppcls/arch/backbone/model_zoo/ghostnet.py
浏览文件 @ d8def846
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. # you may not use this file except in compliance with the License.
...@@ -21,7 +21,14 @@ from paddle.nn import Conv2D, BatchNorm, AdaptiveAvgPool2D, Linear ...@@ -21,7 +21,14 @@ from paddle.nn import Conv2D, BatchNorm, AdaptiveAvgPool2D, Linear
from paddle.regularizer import L2Decay from paddle.regularizer import L2Decay
from paddle.nn.initializer import Uniform, KaimingNormal from paddle.nn.initializer import Uniform, KaimingNormal
__all__ = ["GhostNet_x0_5", "GhostNet_x1_0", "GhostNet_x1_3"] from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"GhostNet_x0_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x0_5_pretrained.pdparams",
"GhostNet_x1_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_0_pretrained.pdparams",
"GhostNet_x1_3": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -315,17 +322,33 @@ class GhostNet(nn.Layer): ...@@ -315,17 +322,33 @@ class GhostNet(nn.Layer):
new_v += divisor new_v += divisor
return new_v return new_v
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def GhostNet_x0_5(**args): def GhostNet_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = GhostNet(scale=0.5) model = GhostNet(scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["GhostNet_x0_5"], use_ssld=use_ssld)
return model return model
def GhostNet_x1_0(**args): def GhostNet_x1_0(pretrained=False, use_ssld=False, **kwargs):
model = GhostNet(scale=1.0) model = GhostNet(scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["GhostNet_x1_0"], use_ssld=use_ssld)
return model return model
def GhostNet_x1_3(**args): def GhostNet_x1_3(pretrained=False, use_ssld=False, **kwargs):
model = GhostNet(scale=1.3) model = GhostNet(scale=1.3, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["GhostNet_x1_3"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/googlenet.py
浏览文件 @ d8def846
...@@ -8,7 +8,12 @@ from paddle.nn.initializer import Uniform ...@@ -8,7 +8,12 @@ from paddle.nn.initializer import Uniform
import math import math
__all__ = ['GoogLeNet'] from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"GoogLeNet": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
def xavier(channels, filter_size, name): def xavier(channels, filter_size, name):
...@@ -200,8 +205,22 @@ class GoogLeNetDY(nn.Layer): ...@@ -200,8 +205,22 @@ class GoogLeNetDY(nn.Layer):
x = self._drop_o2(x) x = self._drop_o2(x)
out2 = self._out2(x) out2 = self._out2(x)
return [out, out1, out2] return [out, out1, out2]
def GoogLeNet(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = GoogLeNetDY(**args) if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def GoogLeNet(pretrained=False, use_ssld=False, **kwargs):
model = GoogLeNetDY(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["GoogLeNet"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/gvt.py
浏览文件 @ d8def846
...@@ -22,10 +22,19 @@ from paddle.regularizer import L2Decay ...@@ -22,10 +22,19 @@ from paddle.regularizer import L2Decay
from .vision_transformer import trunc_normal_, normal_, zeros_, ones_, to_2tuple, DropPath, Identity, Mlp from .vision_transformer import trunc_normal_, normal_, zeros_, ones_, to_2tuple, DropPath, Identity, Mlp
from .vision_transformer import Block as ViTBlock from .vision_transformer import Block as ViTBlock
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"CPVTV2", "PCPVT", "ALTGVT", "pcpvt_small", "pcpvt_base", "pcpvt_large",
"alt_gvt_small", "alt_gvt_base", "alt_gvt_large" MODEL_URLS = {
] "pcpvt_small": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_small_pretrained.pdparams",
"pcpvt_base": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_base_pretrained.pdparams",
"pcpvt_large": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_large_pretrained.pdparams",
"alt_gvt_small": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_small_pretrained.pdparams",
"alt_gvt_base": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_base_pretrained.pdparams",
"alt_gvt_large": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_large_pretrained.pdparams"
}
__all__ = list(MODEL_URLS.keys())
class GroupAttention(nn.Layer): class GroupAttention(nn.Layer):
...@@ -559,8 +568,20 @@ class ALTGVT(PCPVT): ...@@ -559,8 +568,20 @@ class ALTGVT(PCPVT):
cur += depths[k] cur += depths[k]
self.apply(self._init_weights) self.apply(self._init_weights)
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def pcpvt_small(pretrained=False, **kwargs):
def pcpvt_small(pretrained=False, use_ssld=False, **kwargs):
model = CPVTV2( model = CPVTV2(
patch_size=4, patch_size=4,
embed_dims=[64, 128, 320, 512], embed_dims=[64, 128, 320, 512],
...@@ -572,11 +593,11 @@ def pcpvt_small(pretrained=False, **kwargs): ...@@ -572,11 +593,11 @@ def pcpvt_small(pretrained=False, **kwargs):
depths=[3, 4, 6, 3], depths=[3, 4, 6, 3],
sr_ratios=[8, 4, 2, 1], sr_ratios=[8, 4, 2, 1],
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["pcpvt_small"], use_ssld=use_ssld)
return model return model
def pcpvt_base(pretrained=False, **kwargs): def pcpvt_base(pretrained=False, use_ssld=False, **kwargs):
model = CPVTV2( model = CPVTV2(
patch_size=4, patch_size=4,
embed_dims=[64, 128, 320, 512], embed_dims=[64, 128, 320, 512],
...@@ -588,11 +609,11 @@ def pcpvt_base(pretrained=False, **kwargs): ...@@ -588,11 +609,11 @@ def pcpvt_base(pretrained=False, **kwargs):
depths=[3, 4, 18, 3], depths=[3, 4, 18, 3],
sr_ratios=[8, 4, 2, 1], sr_ratios=[8, 4, 2, 1],
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["pcpvt_base"], use_ssld=use_ssld)
return model return model
def pcpvt_large(pretrained=False, **kwargs): def pcpvt_large(pretrained=False, use_ssld=False, **kwargs):
model = CPVTV2( model = CPVTV2(
patch_size=4, patch_size=4,
embed_dims=[64, 128, 320, 512], embed_dims=[64, 128, 320, 512],
...@@ -604,11 +625,11 @@ def pcpvt_large(pretrained=False, **kwargs): ...@@ -604,11 +625,11 @@ def pcpvt_large(pretrained=False, **kwargs):
depths=[3, 8, 27, 3], depths=[3, 8, 27, 3],
sr_ratios=[8, 4, 2, 1], sr_ratios=[8, 4, 2, 1],
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["pcpvt_large"], use_ssld=use_ssld)
return model return model
def alt_gvt_small(pretrained=False, **kwargs): def alt_gvt_small(pretrained=False, use_ssld=False, **kwargs):
model = ALTGVT( model = ALTGVT(
patch_size=4, patch_size=4,
embed_dims=[64, 128, 256, 512], embed_dims=[64, 128, 256, 512],
...@@ -621,11 +642,11 @@ def alt_gvt_small(pretrained=False, **kwargs): ...@@ -621,11 +642,11 @@ def alt_gvt_small(pretrained=False, **kwargs):
wss=[7, 7, 7, 7], wss=[7, 7, 7, 7],
sr_ratios=[8, 4, 2, 1], sr_ratios=[8, 4, 2, 1],
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["alt_gvt_small"], use_ssld=use_ssld)
return model return model
def alt_gvt_base(pretrained=False, **args): def alt_gvt_base(pretrained=False, use_ssld=False, **kwargs):
model = ALTGVT( model = ALTGVT(
patch_size=4, patch_size=4,
embed_dims=[96, 192, 384, 768], embed_dims=[96, 192, 384, 768],
...@@ -637,12 +658,12 @@ def alt_gvt_base(pretrained=False, **args): ...@@ -637,12 +658,12 @@ def alt_gvt_base(pretrained=False, **args):
depths=[2, 2, 18, 2], depths=[2, 2, 18, 2],
wss=[7, 7, 7, 7], wss=[7, 7, 7, 7],
sr_ratios=[8, 4, 2, 1], sr_ratios=[8, 4, 2, 1],
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["alt_gvt_base"], use_ssld=use_ssld)
return model return model
def alt_gvt_large(pretrained=False, **kwargs): def alt_gvt_large(pretrained=False, use_ssld=False, **kwargs):
model = ALTGVT( model = ALTGVT(
patch_size=4, patch_size=4,
embed_dims=[128, 256, 512, 1024], embed_dims=[128, 256, 512, 1024],
...@@ -655,5 +676,5 @@ def alt_gvt_large(pretrained=False, **kwargs): ...@@ -655,5 +676,5 @@ def alt_gvt_large(pretrained=False, **kwargs):
wss=[7, 7, 7, 7], wss=[7, 7, 7, 7],
sr_ratios=[8, 4, 2, 1], sr_ratios=[8, 4, 2, 1],
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["alt_gvt_large"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/hardnet.py
浏览文件 @ d8def846
...@@ -20,13 +20,13 @@ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_f ...@@ -20,13 +20,13 @@ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_f
MODEL_URLS = { MODEL_URLS = {
'HarDNet39_ds': 'HarDNet39_ds':
'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/HarDNet39_ds_pretrained.pdparams', 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet39_ds_pretrained.pdparams',
'HarDNet68_ds': 'HarDNet68_ds':
'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/HarDNet68_ds_pretrained.pdparams', 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_ds_pretrained.pdparams',
'HarDNet68': 'HarDNet68':
'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/HarDNet68_pretrained.pdparams', 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_pretrained.pdparams',
'HarDNet85': 'HarDNet85':
'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/HarDNet85_pretrained.pdparams' 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet85_pretrained.pdparams'
} }
......
ppcls/arch/backbone/model_zoo/hrnet.py
浏览文件 @ d8def846
...@@ -27,24 +27,18 @@ from paddle.nn.initializer import Uniform ...@@ -27,24 +27,18 @@ from paddle.nn.initializer import Uniform
import math import math
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"HRNet_W18_C",
"HRNet_W30_C", MODEL_URLS = {"HRNet_W18_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W18_C_pretrained.pdparams",
"HRNet_W32_C", "HRNet_W30_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W30_C_pretrained.pdparams",
"HRNet_W40_C", "HRNet_W32_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W32_C_pretrained.pdparams",
"HRNet_W44_C", "HRNet_W40_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W40_C_pretrained.pdparams",
"HRNet_W48_C", "HRNet_W44_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W44_C_pretrained.pdparams",
"HRNet_W60_C", "HRNet_W48_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams",
"HRNet_W64_C", "HRNet_W64_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams",
"SE_HRNet_W18_C", }
"SE_HRNet_W30_C",
"SE_HRNet_W32_C", __all__ = list(MODEL_URLS.keys())
"SE_HRNet_W40_C",
"SE_HRNet_W44_C",
"SE_HRNet_W48_C",
"SE_HRNet_W60_C",
"SE_HRNet_W64_C",
]
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -661,82 +655,62 @@ class HRNet(nn.Layer): ...@@ -661,82 +655,62 @@ class HRNet(nn.Layer):
y = self.out(y) y = self.out(y)
return y return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def HRNet_W18_C(**args): if pretrained is False:
model = HRNet(width=18, **args) pass
return model elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
def HRNet_W30_C(**args): load_dygraph_pretrain(model, pretrained)
model = HRNet(width=30, **args) else:
return model raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def HRNet_W32_C(**args):
model = HRNet(width=32, **args)
return model def HRNet_W18_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=18, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W18_C"], use_ssld=use_ssld)
def HRNet_W40_C(**args):
model = HRNet(width=40, **args)
return model
def HRNet_W44_C(**args):
model = HRNet(width=44, **args)
return model
def HRNet_W48_C(**args):
model = HRNet(width=48, **args)
return model
def HRNet_W60_C(**args):
model = HRNet(width=60, **args)
return model
def HRNet_W64_C(**args):
model = HRNet(width=64, **args)
return model
def SE_HRNet_W18_C(**args):
model = HRNet(width=18, has_se=True, **args)
return model return model
def SE_HRNet_W30_C(**args): def HRNet_W30_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=30, has_se=True, **args) model = HRNet(width=30, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W30_C"], use_ssld=use_ssld)
return model return model
def SE_HRNet_W32_C(**args): def HRNet_W32_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=32, has_se=True, **args) model = HRNet(width=32, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W32_C"], use_ssld=use_ssld)
return model return model
def SE_HRNet_W40_C(**args): def HRNet_W40_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=40, has_se=True, **args) model = HRNet(width=40, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W40_C"], use_ssld=use_ssld)
return model return model
def SE_HRNet_W44_C(**args): def HRNet_W44_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=44, has_se=True, **args) model = HRNet(width=44, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W44_C"], use_ssld=use_ssld)
return model return model
def SE_HRNet_W48_C(**args): def HRNet_W48_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=48, has_se=True, **args) model = HRNet(width=48, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W48_C"], use_ssld=use_ssld)
return model return model
def SE_HRNet_W60_C(**args): def HRNet_W64_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=60, has_se=True, **args) model = HRNet(width=64, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W64_C"], use_ssld=use_ssld)
return model return model
def SE_HRNet_W64_C(**args): def SE_HRNet_W64_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=64, has_se=True, **args) model = HRNet(width=64, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["SE_HRNet_W64_C"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/inception_v3.py
浏览文件 @ d8def846
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. # you may not use this file except in compliance with the License.
...@@ -26,7 +26,11 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D ...@@ -26,7 +26,11 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform from paddle.nn.initializer import Uniform
import math import math
__all__ = ["InceptionV3"] from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"InceptionV3": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV3_pretrained.pdparams"}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -425,9 +429,9 @@ class InceptionE(nn.Layer): ...@@ -425,9 +429,9 @@ class InceptionE(nn.Layer):
return outputs return outputs
class InceptionV3(nn.Layer): class Inception_V3(nn.Layer):
def __init__(self, class_dim=1000): def __init__(self, class_dim=1000):
super(InceptionV3, self).__init__() super(Inception_V3, self).__init__()
self.inception_a_list = [[192, 256, 288], [32, 64, 64]] self.inception_a_list = [[192, 256, 288], [32, 64, 64]]
self.inception_c_list = [[768, 768, 768, 768], [128, 160, 160, 192]] self.inception_c_list = [[768, 768, 768, 768], [128, 160, 160, 192]]
...@@ -472,10 +476,28 @@ class InceptionV3(nn.Layer): ...@@ -472,10 +476,28 @@ class InceptionV3(nn.Layer):
def forward(self, x): def forward(self, x):
y = self.inception_stem(x) y = self.inception_stem(x)
for inception_block in self.inception_block_list: for inception_block in self.inception_block_list:
y = inception_block(y) y = inception_block(y)
y = self.gap(y) y = self.gap(y)
y = paddle.reshape(y, shape=[-1, 2048]) y = paddle.reshape(y, shape=[-1, 2048])
y = self.drop(y) y = self.drop(y)
y = self.out(y) y = self.out(y)
return y return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def InceptionV3(pretrained=False, use_ssld=False, **kwargs):
model = Inception_V3(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["InceptionV3"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/inception_v4.py
浏览文件 @ d8def846
...@@ -21,7 +21,11 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D ...@@ -21,7 +21,11 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform from paddle.nn.initializer import Uniform
import math import math
__all__ = ["InceptionV4"] from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"InceptionV4": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams"}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -450,6 +454,19 @@ class InceptionV4DY(nn.Layer): ...@@ -450,6 +454,19 @@ class InceptionV4DY(nn.Layer):
return x return x
def InceptionV4(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = InceptionV4DY(**args) if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def InceptionV4(pretrained=False, use_ssld=False, **kwargs):
model = InceptionV4DY(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["InceptionV4"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/levit.py
浏览文件 @ d8def846
...@@ -24,7 +24,17 @@ from paddle.regularizer import L2Decay ...@@ -24,7 +24,17 @@ from paddle.regularizer import L2Decay
from .vision_transformer import trunc_normal_, zeros_, ones_, Identity from .vision_transformer import trunc_normal_, zeros_, ones_, Identity
__all__ = ["LeViT_128S", "LeViT_128", "LeViT_192", "LeViT_256", "LeViT_384"] from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"LeViT_128S": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams",
"LeViT_128": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128_pretrained.pdparams",
"LeViT_192": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_192_pretrained.pdparams",
"LeViT_256": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_256_pretrained.pdparams",
"LeViT_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_384_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
def cal_attention_biases(attention_biases, attention_bias_idxs): def cal_attention_biases(attention_biases, attention_bias_idxs):
...@@ -479,37 +489,59 @@ specification = { ...@@ -479,37 +489,59 @@ specification = {
}, },
} }
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def LeViT_128S(class_dim=1000, distillation=True, pretrained=False): if pretrained is False:
return model_factory( pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def LeViT_128S(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
model = model_factory(
**specification['LeViT_128S'], **specification['LeViT_128S'],
class_dim=class_dim, class_dim=class_dim,
distillation=distillation) distillation=distillation)
_load_pretrained(pretrained, model, MODEL_URLS["LeViT_128S"], use_ssld=use_ssld)
return model
def LeViT_128(class_dim=1000, distillation=True): def LeViT_128(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
return model_factory( model = model_factory(
**specification['LeViT_128'], **specification['LeViT_128'],
class_dim=class_dim, class_dim=class_dim,
distillation=distillation) distillation=distillation)
_load_pretrained(pretrained, model, MODEL_URLS["LeViT_128"], use_ssld=use_ssld)
return model
def LeViT_192(class_dim=1000, distillation=True): def LeViT_192(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
return model_factory( model = model_factory(
**specification['LeViT_192'], **specification['LeViT_192'],
class_dim=class_dim, class_dim=class_dim,
distillation=distillation) distillation=distillation)
_load_pretrained(pretrained, model, MODEL_URLS["LeViT_192"], use_ssld=use_ssld)
return model
def LeViT_256(class_dim=1000, distillation=False): def LeViT_256(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
return model_factory( model = model_factory(
**specification['LeViT_256'], **specification['LeViT_256'],
class_dim=class_dim, class_dim=class_dim,
distillation=distillation) distillation=distillation)
_load_pretrained(pretrained, model, MODEL_URLS["LeViT_256"], use_ssld=use_ssld)
return model
def LeViT_384(class_dim=1000, distillation=True): def LeViT_384(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
return model_factory( model = model_factory(
**specification['LeViT_384'], **specification['LeViT_384'],
class_dim=class_dim, class_dim=class_dim,
distillation=distillation) distillation=distillation)
_load_pretrained(pretrained, model, MODEL_URLS["LeViT_384"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/mixnet.py
浏览文件 @ d8def846
...@@ -17,14 +17,20 @@ ...@@ -17,14 +17,20 @@
https://arxiv.org/abs/1907.09595. https://arxiv.org/abs/1907.09595.
""" """
__all__ = ['MixNet_S', 'MixNet_M', 'MixNet_L']
import os import os
from inspect import isfunction from inspect import isfunction
from functools import reduce from functools import reduce
import paddle import paddle
import paddle.nn as nn import paddle.nn as nn
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"MixNet_S": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_S_pretrained.pdparams",
"MixNet_M": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_M_pretrained.pdparams",
"MixNet_L": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_L_pretrained.pdparams"}
__all__ = list(MODEL_URLS.keys())
class Identity(nn.Layer): class Identity(nn.Layer):
""" """
...@@ -755,13 +761,33 @@ def get_mixnet(version, width_scale, model_name=None, **kwargs): ...@@ -755,13 +761,33 @@ def get_mixnet(version, width_scale, model_name=None, **kwargs):
return net return net
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def MixNet_S(pretrained=False, use_ssld=False, **kwargs):
model = InceptionV4DY(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["InceptionV4"], use_ssld=use_ssld)
return model
def MixNet_S(**kwargs): def MixNet_S(**kwargs):
""" """
MixNet-S model from 'MixConv: Mixed Depthwise Convolutional Kernels,' MixNet-S model from 'MixConv: Mixed Depthwise Convolutional Kernels,'
https://arxiv.org/abs/1907.09595. https://arxiv.org/abs/1907.09595.
""" """
return get_mixnet( model = get_mixnet(
version="s", width_scale=1.0, model_name="MixNet_S", **kwargs) version="s", width_scale=1.0, model_name="MixNet_S", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MixNet_S"], use_ssld=use_ssld)
return model
def MixNet_M(**kwargs): def MixNet_M(**kwargs):
...@@ -769,14 +795,19 @@ def MixNet_M(**kwargs): ...@@ -769,14 +795,19 @@ def MixNet_M(**kwargs):
MixNet-M model from 'MixConv: Mixed Depthwise Convolutional Kernels,' MixNet-M model from 'MixConv: Mixed Depthwise Convolutional Kernels,'
https://arxiv.org/abs/1907.09595. https://arxiv.org/abs/1907.09595.
""" """
return get_mixnet( model = get_mixnet(
version="m", width_scale=1.0, model_name="MixNet_M", **kwargs) version="m", width_scale=1.0, model_name="MixNet_M", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MixNet_M"], use_ssld=use_ssld)
return model
def MixNet_L(**kwargs): def MixNet_L(**kwargs):
""" """
MixNet-L model from 'MixConv: Mixed Depthwise Convolutional Kernels,' MixNet-S model from 'MixConv: Mixed Depthwise Convolutional Kernels,'
https://arxiv.org/abs/1907.09595. https://arxiv.org/abs/1907.09595.
""" """
return get_mixnet( model = get_mixnet(
version="m", width_scale=1.3, model_name="MixNet_L", **kwargs) version="m", width_scale=1.3, model_name="MixNet_L", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MixNet_L"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/mobilenet_v1.py
浏览文件 @ d8def846
...@@ -26,9 +26,14 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D ...@@ -26,9 +26,14 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import KaimingNormal from paddle.nn.initializer import KaimingNormal
import math import math
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"MobileNetV1_x0_25", "MobileNetV1_x0_5", "MobileNetV1_x0_75", "MobileNetV1"
] MODEL_URLS = {"MobileNetV1_x0_25": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_25_pretrained.pdparams",
"MobileNetV1_x0_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_5_pretrained.pdparams",
"MobileNetV1_x0_75": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_x0_75_pretrained.pdparams",
"MobileNetV1": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV1_pretrained.pdparams"}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -245,22 +250,39 @@ class MobileNet(nn.Layer): ...@@ -245,22 +250,39 @@ class MobileNet(nn.Layer):
y = self.out(y) y = self.out(y)
return y return y
def MobileNetV1_x0_25(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = MobileNet(scale=0.25, **args) if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def MobileNetV1_x0_25(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.25, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_25"], use_ssld=use_ssld)
return model return model
def MobileNetV1_x0_5(**args): def MobileNetV1_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.5, **args) model = MobileNet(scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_5"], use_ssld=use_ssld)
return model return model
def MobileNetV1_x0_75(**args): def MobileNetV1_x0_75(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.75, **args) model = MobileNet(scale=0.75, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_75"], use_ssld=use_ssld)
return model return model
def MobileNetV1(**args): def MobileNetV1(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=1.0, **args) model = MobileNet(scale=1.0, **kwargs)
return model _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1"], use_ssld=use_ssld)
return model
\ No newline at end of file
ppcls/arch/backbone/model_zoo/mobilenet_v2.py
浏览文件 @ d8def846
...@@ -26,10 +26,16 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D ...@@ -26,10 +26,16 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
import math import math
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"MobileNetV2_x0_25", "MobileNetV2_x0_5", "MobileNetV2_x0_75",
"MobileNetV2", "MobileNetV2_x1_5", "MobileNetV2_x2_0" MODEL_URLS = {"MobileNetV2_x0_25": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_25_pretrained.pdparams",
] "MobileNetV2_x0_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_5_pretrained.pdparams",
"MobileNetV2_x0_75": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_75_pretrained.pdparams",
"MobileNetV2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_pretrained.pdparams",
"MobileNetV2_x1_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x1_5_pretrained.pdparams",
"MobileNetV2_x2_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x2_0_pretrained.pdparams"}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -149,7 +155,7 @@ class InvresiBlocks(nn.Layer): ...@@ -149,7 +155,7 @@ class InvresiBlocks(nn.Layer):
class MobileNet(nn.Layer): class MobileNet(nn.Layer):
def __init__(self, class_dim=1000, scale=1.0, prefix_name="", **args): def __init__(self, class_dim=1000, scale=1.0, prefix_name=""):
super(MobileNet, self).__init__() super(MobileNet, self).__init__()
self.scale = scale self.scale = scale
self.class_dim = class_dim self.class_dim = class_dim
...@@ -216,33 +222,52 @@ class MobileNet(nn.Layer): ...@@ -216,33 +222,52 @@ class MobileNet(nn.Layer):
y = paddle.flatten(y, start_axis=1, stop_axis=-1) y = paddle.flatten(y, start_axis=1, stop_axis=-1)
y = self.out(y) y = self.out(y)
return y return y
def MobileNetV2_x0_25(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = MobileNet(scale=0.25, **args) if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def MobileNetV2_x0_25(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.25, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2_x0_25"], use_ssld=use_ssld)
return model return model
def MobileNetV2_x0_5(**args): def MobileNetV2_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.5, **args) model = MobileNet(scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2_x0_5"], use_ssld=use_ssld)
return model return model
def MobileNetV2_x0_75(**args): def MobileNetV2_x0_75(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.75, **args) model = MobileNet(scale=0.75, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2_x0_75"], use_ssld=use_ssld)
return model return model
def MobileNetV2(**args): def MobileNetV2(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=1.0, **args) model = MobileNet(scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2"], use_ssld=use_ssld)
return model return model
def MobileNetV2_x1_5(**args): def MobileNetV2_x1_5(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=1.5, **args) model = MobileNet(scale=1.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2_x1_5"], use_ssld=use_ssld)
return model return model
def MobileNetV2_x2_0(**args): def MobileNetV2_x2_0(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=2.0, **args) model = MobileNet(scale=2.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2_x2_0"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/mobilenet_v3.py
浏览文件 @ d8def846
...@@ -28,13 +28,20 @@ from paddle.regularizer import L2Decay ...@@ -28,13 +28,20 @@ from paddle.regularizer import L2Decay
import math import math
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"MobileNetV3_small_x0_35", "MobileNetV3_small_x0_5",
"MobileNetV3_small_x0_75", "MobileNetV3_small_x1_0", MODEL_URLS = {"MobileNetV3_small_x0_35": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_35_pretrained.pdparams",
"MobileNetV3_small_x1_25", "MobileNetV3_large_x0_35", "MobileNetV3_small_x0_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_5_pretrained.pdparams",
"MobileNetV3_large_x0_5", "MobileNetV3_large_x0_75", "MobileNetV3_small_x0_75": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_75_pretrained.pdparams",
"MobileNetV3_large_x1_0", "MobileNetV3_large_x1_25" "MobileNetV3_small_x1_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_0_pretrained.pdparams",
] "MobileNetV3_small_x1_25": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x1_25_pretrained.pdparams",
"MobileNetV3_large_x0_35": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_35_pretrained.pdparams",
"MobileNetV3_large_x0_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams",
"MobileNetV3_large_x0_75": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_75_pretrained.pdparams",
"MobileNetV3_large_x1_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_pretrained.pdparams",
"MobileNetV3_large_x1_25": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_25_pretrained.pdparams"}
__all__ = list(MODEL_URLS.keys())
def make_divisible(v, divisor=8, min_value=None): def make_divisible(v, divisor=8, min_value=None):
...@@ -308,52 +315,75 @@ class SEModule(nn.Layer): ...@@ -308,52 +315,75 @@ class SEModule(nn.Layer):
outputs = hardsigmoid(outputs, slope=0.2, offset=0.5) outputs = hardsigmoid(outputs, slope=0.2, offset=0.5)
return paddle.multiply(x=inputs, y=outputs) return paddle.multiply(x=inputs, y=outputs)
def MobileNetV3_small_x0_35(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = MobileNetV3(model_name="small", scale=0.35, **args) if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def MobileNetV3_small_x0_35(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="small", scale=0.35, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x0_35"], use_ssld=use_ssld)
return model return model
def MobileNetV3_small_x0_5(**args): def MobileNetV3_small_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="small", scale=0.5, **args) model = MobileNetV3(model_name="small", scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x0_5"], use_ssld=use_ssld)
return model return model
def MobileNetV3_small_x0_75(**args): def MobileNetV3_small_x0_75(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="small", scale=0.75, **args) model = MobileNetV3(model_name="small", scale=0.75, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x0_75"], use_ssld=use_ssld)
return model return model
def MobileNetV3_small_x1_0(**args): def MobileNetV3_small_x1_0(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="small", scale=1.0, **args) model = MobileNetV3(model_name="small", scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x1_0"], use_ssld=use_ssld)
return model return model
def MobileNetV3_small_x1_25(**args): def MobileNetV3_small_x1_25(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="small", scale=1.25, **args) model = MobileNetV3(model_name="small", scale=1.25, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x1_25"], use_ssld=use_ssld)
return model return model
def MobileNetV3_large_x0_35(**args): def MobileNetV3_large_x0_35(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="large", scale=0.35, **args) model = MobileNetV3(model_name="large", scale=0.35, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x0_35"], use_ssld=use_ssld)
return model return model
def MobileNetV3_large_x0_5(**args): def MobileNetV3_large_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="large", scale=0.5, **args) model = MobileNetV3(model_name="large", scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x0_5"], use_ssld=use_ssld)
return model return model
def MobileNetV3_large_x0_75(**args): def MobileNetV3_large_x0_75(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="large", scale=0.75, **args) model = MobileNetV3(model_name="large", scale=0.75, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x0_75"], use_ssld=use_ssld)
return model return model
def MobileNetV3_large_x1_0(**args): def MobileNetV3_large_x1_0(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="large", scale=1.0, **args) model = MobileNetV3(model_name="large", scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x1_0"], use_ssld=use_ssld)
return model return model
def MobileNetV3_large_x1_25(**args): def MobileNetV3_large_x1_25(pretrained=False, use_ssld=False, **kwargs):
model = MobileNetV3(model_name="large", scale=1.25, **args) model = MobileNetV3(model_name="large", scale=1.25, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x1_25"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/rednet.py
浏览文件 @ d8def846
...@@ -22,15 +22,15 @@ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_f ...@@ -22,15 +22,15 @@ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_f
MODEL_URLS = { MODEL_URLS = {
"RedNet26": "RedNet26":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/RedNet26_pretrained.pdparams", "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet26_pretrained.pdparams",
"RedNet38": "RedNet38":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/RedNet38_pretrained.pdparams", "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet38_pretrained.pdparams",
"RedNet50": "RedNet50":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/RedNet50_pretrained.pdparams", "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet50_pretrained.pdparams",
"RedNet101": "RedNet101":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/RedNet101_pretrained.pdparams", "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet101_pretrained.pdparams",
"RedNet152": "RedNet152":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/RedNet152_pretrained.pdparams" "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet152_pretrained.pdparams"
} }
......
ppcls/arch/backbone/model_zoo/regnet.py
浏览文件 @ d8def846
...@@ -26,10 +26,17 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D ...@@ -26,10 +26,17 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform from paddle.nn.initializer import Uniform
import math import math
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"RegNetX_200MF", "RegNetX_4GF", "RegNetX_32GF", "RegNetY_200MF",
"RegNetY_4GF", "RegNetY_32GF" MODEL_URLS = {"RegNetX_200MF": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_200MF_pretrained.pdparams",
] "RegNetX_4GF": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams",
"RegNetX_32GF": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_32GF_pretrained.pdparams",
"RegNetY_200MF": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_200MF_pretrained.pdparams",
"RegNetY_4GF": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_4GF_pretrained.pdparams",
"RegNetY_32GF": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetY_32GF_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
def quantize_float(f, q): def quantize_float(f, q):
...@@ -308,14 +315,28 @@ class RegNet(nn.Layer): ...@@ -308,14 +315,28 @@ class RegNet(nn.Layer):
y = self.out(y) y = self.out(y)
return y return y
def RegNetX_200MF(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def RegNetX_200MF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet( model = RegNet(
w_a=36.44, w_0=24, w_m=2.49, d=13, group_w=8, bot_mul=1.0, q=8, **args) w_a=36.44, w_0=24, w_m=2.49, d=13, group_w=8, bot_mul=1.0, q=8, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_200MF"], use_ssld=use_ssld)
return model return model
def RegNetX_4GF(**args): def RegNetX_4GF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet( model = RegNet(
w_a=38.65, w_a=38.65,
w_0=96, w_0=96,
...@@ -324,11 +345,12 @@ def RegNetX_4GF(**args): ...@@ -324,11 +345,12 @@ def RegNetX_4GF(**args):
group_w=40, group_w=40,
bot_mul=1.0, bot_mul=1.0,
q=8, q=8,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_4GF"], use_ssld=use_ssld)
return model return model
def RegNetX_32GF(**args): def RegNetX_32GF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet( model = RegNet(
w_a=69.86, w_a=69.86,
w_0=320, w_0=320,
...@@ -337,11 +359,12 @@ def RegNetX_32GF(**args): ...@@ -337,11 +359,12 @@ def RegNetX_32GF(**args):
group_w=168, group_w=168,
bot_mul=1.0, bot_mul=1.0,
q=8, q=8,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model return model
def RegNetY_200MF(**args): def RegNetY_200MF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet( model = RegNet(
w_a=36.44, w_a=36.44,
w_0=24, w_0=24,
...@@ -351,11 +374,12 @@ def RegNetY_200MF(**args): ...@@ -351,11 +374,12 @@ def RegNetY_200MF(**args):
bot_mul=1.0, bot_mul=1.0,
q=8, q=8,
se_on=True, se_on=True,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model return model
def RegNetY_4GF(**args): def RegNetY_4GF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet( model = RegNet(
w_a=31.41, w_a=31.41,
w_0=96, w_0=96,
...@@ -365,11 +389,12 @@ def RegNetY_4GF(**args): ...@@ -365,11 +389,12 @@ def RegNetY_4GF(**args):
bot_mul=1.0, bot_mul=1.0,
q=8, q=8,
se_on=True, se_on=True,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model return model
def RegNetY_32GF(**args): def RegNetY_32GF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet( model = RegNet(
w_a=115.89, w_a=115.89,
w_0=232, w_0=232,
...@@ -379,5 +404,6 @@ def RegNetY_32GF(**args): ...@@ -379,5 +404,6 @@ def RegNetY_32GF(**args):
bot_mul=1.0, bot_mul=1.0,
q=8, q=8,
se_on=True, se_on=True,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/repvgg.py
浏览文件 @ d8def846
...@@ -2,22 +2,29 @@ import paddle.nn as nn ...@@ -2,22 +2,29 @@ import paddle.nn as nn
import paddle import paddle
import numpy as np import numpy as np
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
'RepVGG',
'RepVGG_A0', MODEL_URLS = {"RepVGG_A0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A0_pretrained.pdparams",
'RepVGG_A1', "RepVGG_A1": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A1_pretrained.pdparams",
'RepVGG_A2', "RepVGG_A2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A2_pretrained.pdparams",
'RepVGG_B0', "RepVGG_B0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B0_pretrained.pdparams",
'RepVGG_B1', "RepVGG_B1": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1_pretrained.pdparams",
'RepVGG_B2', "RepVGG_B2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2_pretrained.pdparams",
'RepVGG_B3', "RepVGG_B3": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3_pretrained.pdparams",
'RepVGG_B1g2', "RepVGG_B1g2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g2_pretrained.pdparams",
'RepVGG_B1g4', "RepVGG_B1g4": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g4_pretrained.pdparams",
'RepVGG_B2g2', "RepVGG_B2g2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g2_pretrained.pdparams",
'RepVGG_B2g4', "RepVGG_B2g4": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g4_pretrained.pdparams",
'RepVGG_B3g2', "RepVGG_B3g2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g2_pretrained.pdparams",
'RepVGG_B3g4', "RepVGG_B3g4": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g4_pretrained.pdparams",
] }
__all__ = list(MODEL_URLS.keys())
optional_groupwise_layers = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26]
g2_map = {l: 2 for l in optional_groupwise_layers}
g4_map = {l: 4 for l in optional_groupwise_layers}
class ConvBN(nn.Layer): class ConvBN(nn.Layer):
...@@ -230,110 +237,144 @@ class RepVGG(nn.Layer): ...@@ -230,110 +237,144 @@ class RepVGG(nn.Layer):
return out return out
optional_groupwise_layers = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26] def _load_pretrained(pretrained, model, model_url, use_ssld=False):
g2_map = {l: 2 for l in optional_groupwise_layers} if pretrained is False:
g4_map = {l: 4 for l in optional_groupwise_layers} pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
def RepVGG_A0(**kwargs): elif isinstance(pretrained, str):
return RepVGG( load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def RepVGG_A0(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
num_blocks=[2, 4, 14, 1], num_blocks=[2, 4, 14, 1],
width_multiplier=[0.75, 0.75, 0.75, 2.5], width_multiplier=[0.75, 0.75, 0.75, 2.5],
override_groups_map=None, override_groups_map=None,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_A0"], use_ssld=use_ssld)
return model
def RepVGG_A1(**kwargs): def RepVGG_A1(pretrained=False, use_ssld=False, **kwargs):
return RepVGG( model = RepVGG(
num_blocks=[2, 4, 14, 1], num_blocks=[2, 4, 14, 1],
width_multiplier=[1, 1, 1, 2.5], width_multiplier=[1, 1, 1, 2.5],
override_groups_map=None, override_groups_map=None,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_A1"], use_ssld=use_ssld)
return model
def RepVGG_A2(**kwargs): def RepVGG_A2(pretrained=False, use_ssld=False, **kwargs):
return RepVGG( model = RepVGG(
num_blocks=[2, 4, 14, 1], num_blocks=[2, 4, 14, 1],
width_multiplier=[1.5, 1.5, 1.5, 2.75], width_multiplier=[1.5, 1.5, 1.5, 2.75],
override_groups_map=None, override_groups_map=None,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_A2"], use_ssld=use_ssld)
return model
def RepVGG_B0(**kwargs): def RepVGG_B0(pretrained=False, use_ssld=False, **kwargs):
return RepVGG( model = RepVGG(
num_blocks=[4, 6, 16, 1], num_blocks=[4, 6, 16, 1],
width_multiplier=[1, 1, 1, 2.5], width_multiplier=[1, 1, 1, 2.5],
override_groups_map=None, override_groups_map=None,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B0"], use_ssld=use_ssld)
return model
def RepVGG_B1(**kwargs): def RepVGG_B1(pretrained=False, use_ssld=False, **kwargs):
return RepVGG( model = RepVGG(
num_blocks=[4, 6, 16, 1], num_blocks=[4, 6, 16, 1],
width_multiplier=[2, 2, 2, 4], width_multiplier=[2, 2, 2, 4],
override_groups_map=None, override_groups_map=None,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B1"], use_ssld=use_ssld)
return model
def RepVGG_B1g2(**kwargs): def RepVGG_B1g2(pretrained=False, use_ssld=False, **kwargs):
return RepVGG( model = RepVGG(
num_blocks=[4, 6, 16, 1], num_blocks=[4, 6, 16, 1],
width_multiplier=[2, 2, 2, 4], width_multiplier=[2, 2, 2, 4],
override_groups_map=g2_map, override_groups_map=g2_map,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B1g2"], use_ssld=use_ssld)
return model
def RepVGG_B1g4(**kwargs): def RepVGG_B1g4(pretrained=False, use_ssld=False, **kwargs):
return RepVGG( model = RepVGG(
num_blocks=[4, 6, 16, 1], num_blocks=[4, 6, 16, 1],
width_multiplier=[2, 2, 2, 4], width_multiplier=[2, 2, 2, 4],
override_groups_map=g4_map, override_groups_map=g4_map,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B1g4"], use_ssld=use_ssld)
return model
def RepVGG_B2(**kwargs): def RepVGG_B2(pretrained=False, use_ssld=False, **kwargs):
return RepVGG( model = RepVGG(
num_blocks=[4, 6, 16, 1], num_blocks=[4, 6, 16, 1],
width_multiplier=[2.5, 2.5, 2.5, 5], width_multiplier=[2.5, 2.5, 2.5, 5],
override_groups_map=None, override_groups_map=None,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B2"], use_ssld=use_ssld)
return model
def RepVGG_B2g2(**kwargs): def RepVGG_B2g2(pretrained=False, use_ssld=False, **kwargs):
return RepVGG( model = RepVGG(
num_blocks=[4, 6, 16, 1], num_blocks=[4, 6, 16, 1],
width_multiplier=[2.5, 2.5, 2.5, 5], width_multiplier=[2.5, 2.5, 2.5, 5],
override_groups_map=g2_map, override_groups_map=g2_map,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B2g2"], use_ssld=use_ssld)
return model
def RepVGG_B2g4(**kwargs): def RepVGG_B2g4(pretrained=False, use_ssld=False, **kwargs):
return RepVGG( model = RepVGG(
num_blocks=[4, 6, 16, 1], num_blocks=[4, 6, 16, 1],
width_multiplier=[2.5, 2.5, 2.5, 5], width_multiplier=[2.5, 2.5, 2.5, 5],
override_groups_map=g4_map, override_groups_map=g4_map,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B2g4"], use_ssld=use_ssld)
return model
def RepVGG_B3(**kwargs): def RepVGG_B3(pretrained=False, use_ssld=False, **kwargs):
return RepVGG( model = RepVGG(
num_blocks=[4, 6, 16, 1], num_blocks=[4, 6, 16, 1],
width_multiplier=[3, 3, 3, 5], width_multiplier=[3, 3, 3, 5],
override_groups_map=None, override_groups_map=None,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B3"], use_ssld=use_ssld)
return model
def RepVGG_B3g2(**kwargs): def RepVGG_B3g2(pretrained=False, use_ssld=False, **kwargs):
return RepVGG( model = RepVGG(
num_blocks=[4, 6, 16, 1], num_blocks=[4, 6, 16, 1],
width_multiplier=[3, 3, 3, 5], width_multiplier=[3, 3, 3, 5],
override_groups_map=g2_map, override_groups_map=g2_map,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B3g2"], use_ssld=use_ssld)
return model
def RepVGG_B3g4(**kwargs): def RepVGG_B3g4(pretrained=False, use_ssld=False, **kwargs):
return RepVGG( model = RepVGG(
num_blocks=[4, 6, 16, 1], num_blocks=[4, 6, 16, 1],
width_multiplier=[3, 3, 3, 5], width_multiplier=[3, 3, 3, 5],
override_groups_map=g4_map, override_groups_map=g4_map,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B3g4"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/res2net.py
浏览文件 @ d8def846
...@@ -27,11 +27,13 @@ from paddle.nn.initializer import Uniform ...@@ -27,11 +27,13 @@ from paddle.nn.initializer import Uniform
import math import math
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"Res2Net50_48w_2s", "Res2Net50_26w_4s", "Res2Net50_14w_8s",
"Res2Net50_48w_2s", "Res2Net50_26w_6s", "Res2Net50_26w_8s", MODEL_URLS = {"Res2Net50_26w_4s": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_26w_4s_pretrained.pdparams",
"Res2Net101_26w_4s", "Res2Net152_26w_4s", "Res2Net200_26w_4s" "Res2Net50_14w_8s": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_14w_8s_pretrained.pdparams",
] }
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -232,41 +234,26 @@ class Res2Net(nn.Layer): ...@@ -232,41 +234,26 @@ class Res2Net(nn.Layer):
return y return y
def Res2Net50_48w_2s(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = Res2Net(layers=50, scales=2, width=48, **args) if pretrained is False:
return model pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
def Res2Net50_26w_4s(**args): elif isinstance(pretrained, str):
model = Res2Net(layers=50, scales=4, width=26, **args) load_dygraph_pretrain(model, pretrained)
return model else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
def Res2Net50_14w_8s(**args): )
model = Res2Net(layers=50, scales=8, width=14, **args)
return model
def Res2Net50_26w_4s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net(layers=50, scales=4, width=26, **kwargs)
def Res2Net50_26w_6s(**args): _load_pretrained(pretrained, model, MODEL_URLS["Res2Net50_26w_4s"], use_ssld=use_ssld)
model = Res2Net(layers=50, scales=6, width=26, **args)
return model return model
def Res2Net50_26w_8s(**args): def Res2Net50_14w_8s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net(layers=50, scales=8, width=26, **args) model = Res2Net(layers=50, scales=8, width=14, **kwargs)
return model _load_pretrained(pretrained, model, MODEL_URLS["Res2Net50_14w_8s"], use_ssld=use_ssld)
return model
\ No newline at end of file
def Res2Net101_26w_4s(**args):
model = Res2Net(layers=101, scales=4, width=26, **args)
return model
def Res2Net152_26w_4s(**args):
model = Res2Net(layers=152, scales=4, width=26, **args)
return model
def Res2Net200_26w_4s(**args):
model = Res2Net(layers=200, scales=4, width=26, **args)
return model
ppcls/arch/backbone/model_zoo/res2net_vd.py
浏览文件 @ d8def846
...@@ -27,11 +27,14 @@ from paddle.nn.initializer import Uniform ...@@ -27,11 +27,14 @@ from paddle.nn.initializer import Uniform
import math import math
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"Res2Net50_vd_48w_2s", "Res2Net50_vd_26w_4s", "Res2Net50_vd_14w_8s",
"Res2Net50_vd_48w_2s", "Res2Net50_vd_26w_6s", "Res2Net50_vd_26w_8s", MODEL_URLS = {"Res2Net50_vd_26w_4s": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_pretrained.pdparams",
"Res2Net101_vd_26w_4s", "Res2Net152_vd_26w_4s", "Res2Net200_vd_26w_4s" "Res2Net101_vd_26w_4s": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_pretrained.pdparams",
] "Res2Net200_vd_26w_4s": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -255,41 +258,32 @@ class Res2Net_vd(nn.Layer): ...@@ -255,41 +258,32 @@ class Res2Net_vd(nn.Layer):
return y return y
def Res2Net50_vd_48w_2s(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = Res2Net_vd(layers=50, scales=2, width=48, **args) if pretrained is False:
return model pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
def Res2Net50_vd_26w_4s(**args): elif isinstance(pretrained, str):
model = Res2Net_vd(layers=50, scales=4, width=26, **args) load_dygraph_pretrain(model, pretrained)
return model else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
def Res2Net50_vd_14w_8s(**args): )
model = Res2Net_vd(layers=50, scales=8, width=14, **args)
return model
def Res2Net50_vd_26w_6s(**args):
model = Res2Net_vd(layers=50, scales=6, width=26, **args)
return model
def Res2Net50_vd_26w_8s(**args): def Res2Net50_vd_26w_4s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net_vd(layers=50, scales=8, width=26, **args) model = Res2Net_vd(layers=50, scales=4, width=26, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Res2Net50_vd_26w_4s"], use_ssld=use_ssld)
return model return model
def Res2Net101_vd_26w_4s(**args): def Res2Net101_vd_26w_4s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net_vd(layers=101, scales=4, width=26, **args) model = Res2Net_vd(layers=101, scales=4, width=26, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Res2Net101_vd_26w_4s"], use_ssld=use_ssld)
return model return model
def Res2Net152_vd_26w_4s(**args): def Res2Net200_vd_26w_4s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net_vd(layers=152, scales=4, width=26, **args) model = Res2Net_vd(layers=200, scales=4, width=26, **kwargs)
return model _load_pretrained(pretrained, model, MODEL_URLS["Res2Net200_vd_26w_4s"], use_ssld=use_ssld)
return model
\ No newline at end of file
def Res2Net200_vd_26w_4s(**args):
model = Res2Net_vd(layers=200, scales=4, width=26, **args)
return model
ppcls/arch/backbone/model_zoo/resnest.py
浏览文件 @ d8def846
...@@ -27,7 +27,14 @@ from paddle.nn import Conv2D, BatchNorm, Linear, Dropout ...@@ -27,7 +27,14 @@ from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.regularizer import L2Decay from paddle.regularizer import L2Decay
__all__ = ["ResNeSt50_fast_1s1x64d", "ResNeSt50", "ResNeSt101"] from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"ResNeSt50_fast_1s1x64d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_fast_1s1x64d_pretrained.pdparams",
"ResNeSt50": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_pretrained.pdparams",
"ResNeSt101": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt101_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -656,8 +663,21 @@ class ResNeSt(nn.Layer): ...@@ -656,8 +663,21 @@ class ResNeSt(nn.Layer):
x = self.out(x) x = self.out(x)
return x return x
def ResNeSt50_fast_1s1x64d(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def ResNeSt50_fast_1s1x64d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeSt( model = ResNeSt(
layers=[3, 4, 6, 3], layers=[3, 4, 6, 3],
radix=1, radix=1,
...@@ -669,11 +689,12 @@ def ResNeSt50_fast_1s1x64d(**args): ...@@ -669,11 +689,12 @@ def ResNeSt50_fast_1s1x64d(**args):
avd=True, avd=True,
avd_first=True, avd_first=True,
final_drop=0.0, final_drop=0.0,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeSt50_fast_1s1x64d"], use_ssld=use_ssld)
return model return model
def ResNeSt50(**args): def ResNeSt50(pretrained=False, use_ssld=False, **kwargs):
model = ResNeSt( model = ResNeSt(
layers=[3, 4, 6, 3], layers=[3, 4, 6, 3],
radix=2, radix=2,
...@@ -685,11 +706,12 @@ def ResNeSt50(**args): ...@@ -685,11 +706,12 @@ def ResNeSt50(**args):
avd=True, avd=True,
avd_first=False, avd_first=False,
final_drop=0.0, final_drop=0.0,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeSt50"], use_ssld=use_ssld)
return model return model
def ResNeSt101(**args): def ResNeSt101(pretrained=False, use_ssld=False, **kwargs):
model = ResNeSt( model = ResNeSt(
layers=[3, 4, 23, 3], layers=[3, 4, 23, 3],
radix=2, radix=2,
...@@ -701,5 +723,6 @@ def ResNeSt101(**args): ...@@ -701,5 +723,6 @@ def ResNeSt101(**args):
avd=True, avd=True,
avd_first=False, avd_first=False,
final_drop=0.0, final_drop=0.0,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeSt101"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/resnet.py
浏览文件 @ d8def846
...@@ -27,7 +27,16 @@ from paddle.nn.initializer import Uniform ...@@ -27,7 +27,16 @@ from paddle.nn.initializer import Uniform
import math import math
__all__ = ["ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"] from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"ResNet18": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_pretrained.pdparams",
"ResNet34": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_pretrained.pdparams",
"ResNet50": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_pretrained.pdparams",
"ResNet101": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_pretrained.pdparams",
"ResNet152": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet152_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -290,27 +299,45 @@ class ResNet(nn.Layer): ...@@ -290,27 +299,45 @@ class ResNet(nn.Layer):
y = self.out(y) y = self.out(y)
return y return y
def ResNet18(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = ResNet(layers=18, **args) if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def ResNet18(pretrained=False, use_ssld=False, **kwargs):
model = ResNet(layers=18, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet18"], use_ssld=use_ssld)
return model return model
def ResNet34(**args): def ResNet34(pretrained=False, use_ssld=False, **kwargs):
model = ResNet(layers=34, **args) model = ResNet(layers=34, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet34"], use_ssld=use_ssld)
return model return model
def ResNet50(**args): def ResNet50(pretrained=False, use_ssld=False, **kwargs):
model = ResNet(layers=50, **args) model = ResNet(layers=50, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet50"], use_ssld=use_ssld)
return model return model
def ResNet101(**args): def ResNet101(pretrained=False, use_ssld=False, **kwargs):
model = ResNet(layers=101, **args) model = ResNet(layers=101, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet101"], use_ssld=use_ssld)
return model return model
def ResNet152(**args): def ResNet152(pretrained=False, use_ssld=False, **kwargs):
model = ResNet(layers=152, **args) model = ResNet(layers=152, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet152"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/resnet_vc.py
浏览文件 @ d8def846
...@@ -27,9 +27,13 @@ from paddle.nn.initializer import Uniform ...@@ -27,9 +27,13 @@ from paddle.nn.initializer import Uniform
import math import math
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"ResNet18_vc", "ResNet34_vc", "ResNet50_vc", "ResNet101_vc", "ResNet152_vc"
] MODEL_URLS = {
"ResNet50_vc": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vc_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -283,27 +287,22 @@ class ResNet_vc(nn.Layer): ...@@ -283,27 +287,22 @@ class ResNet_vc(nn.Layer):
y = self.out(y) y = self.out(y)
return y return y
def ResNet18_vc(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = ResNet_vc(layers=18, **args) if pretrained is False:
return model pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
def ResNet34_vc(**args): elif isinstance(pretrained, str):
model = ResNet_vc(layers=34, **args) load_dygraph_pretrain(model, pretrained)
return model else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
def ResNet50_vc(**args): )
model = ResNet_vc(layers=50, **args)
def ResNet50_vc(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vc(layers=50, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet50_vc"], use_ssld=use_ssld)
return model return model
def ResNet101_vc(**args):
model = ResNet_vc(layers=101, **args)
return model
def ResNet152_vc(**args):
model = ResNet_vc(layers=152, **args)
return model
ppcls/arch/backbone/model_zoo/resnet_vd.py
浏览文件 @ d8def846
...@@ -27,9 +27,18 @@ from paddle.nn.initializer import Uniform ...@@ -27,9 +27,18 @@ from paddle.nn.initializer import Uniform
import math import math
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"ResNet18_vd", "ResNet34_vd", "ResNet50_vd", "ResNet101_vd", "ResNet152_vd"
] MODEL_URLS = {
"ResNet18_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams",
"ResNet34_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet34_vd_pretrained.pdparams",
"ResNet50_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams",
"ResNet101_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet101_vd_pretrained.pdparams",
"ResNet152_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet152_vd_pretrained.pdparams",
"ResNet200_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet200_vd_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -324,31 +333,50 @@ class ResNet_vd(nn.Layer): ...@@ -324,31 +333,50 @@ class ResNet_vd(nn.Layer):
return y return y
def ResNet18_vd(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = ResNet_vd(layers=18, **args) if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def ResNet18_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=18, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet18_vd"], use_ssld=use_ssld)
return model return model
def ResNet34_vd(**args): def ResNet34_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=34, **args) model = ResNet_vd(layers=34, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet34_vd"], use_ssld=use_ssld)
return model return model
def ResNet50_vd(**args): def ResNet50_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=50, **args) model = ResNet_vd(layers=50, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet50_vd"], use_ssld=use_ssld)
return model return model
def ResNet101_vd(**args): def ResNet101_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=101, **args) model = ResNet_vd(layers=101, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet101_vd"], use_ssld=use_ssld)
return model return model
def ResNet152_vd(**args): def ResNet152_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=152, **args) model = ResNet_vd(layers=152, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet152_vd"], use_ssld=use_ssld)
return model return model
def ResNet200_vd(**args): def ResNet200_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=200, **args) model = ResNet_vd(layers=200, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet200_vd"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/resnext.py
浏览文件 @ d8def846
...@@ -27,10 +27,18 @@ from paddle.nn.initializer import Uniform ...@@ -27,10 +27,18 @@ from paddle.nn.initializer import Uniform
import math import math
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"ResNeXt50_32x4d", "ResNeXt50_64x4d", "ResNeXt101_32x4d",
"ResNeXt101_64x4d", "ResNeXt152_32x4d", "ResNeXt152_64x4d" MODEL_URLS = {
] "ResNeXt50_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_32x4d_pretrained.pdparams",
"ResNeXt50_64x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_64x4d_pretrained.pdparams",
"ResNeXt101_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x4d_pretrained.pdparams",
"ResNeXt101_64x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_64x4d_pretrained.pdparams",
"ResNeXt152_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_32x4d_pretrained.pdparams",
"ResNeXt152_64x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_64x4d_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -222,32 +230,51 @@ class ResNeXt(nn.Layer): ...@@ -222,32 +230,51 @@ class ResNeXt(nn.Layer):
y = self.out(y) y = self.out(y)
return y return y
def ResNeXt50_32x4d(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = ResNeXt(layers=50, cardinality=32, **args) if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def ResNeXt50_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=50, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt50_32x4d"], use_ssld=use_ssld)
return model return model
def ResNeXt50_64x4d(**args): def ResNeXt50_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=50, cardinality=64, **args) model = ResNeXt(layers=50, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt50_64x4d"], use_ssld=use_ssld)
return model return model
def ResNeXt101_32x4d(**args): def ResNeXt101_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=32, **args) model = ResNeXt(layers=101, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_32x4d"], use_ssld=use_ssld)
return model return model
def ResNeXt101_64x4d(**args): def ResNeXt101_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=64, **args) model = ResNeXt(layers=101, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_64x4d"], use_ssld=use_ssld)
return model return model
def ResNeXt152_32x4d(**args): def ResNeXt152_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=32, **args) model = ResNeXt(layers=152, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt152_32x4d"], use_ssld=use_ssld)
return model return model
def ResNeXt152_64x4d(**args): def ResNeXt152_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=64, **args) model = ResNeXt(layers=152, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt152_64x4d"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/resnext101_wsl.py
浏览文件 @ d8def846
...@@ -6,10 +6,18 @@ from paddle.nn import Conv2D, BatchNorm, Linear, Dropout ...@@ -6,10 +6,18 @@ from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform from paddle.nn.initializer import Uniform
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"ResNeXt101_32x8d_wsl", "ResNeXt101_32x16d_wsl", "ResNeXt101_32x32d_wsl",
"ResNeXt101_32x48d_wsl" MODEL_URLS = {
] "ResNeXt101_32x8d_wsl": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x8d_wsl_pretrained.pdparams",
"ResNeXt101_32x16d_wsl": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x816_wsl_pretrained.pdparams",
"ResNeXt101_32x32d_wsl": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x32d_wsl_pretrained.pdparams",
"ResNeXt101_32x48d_wsl": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x48d_wsl_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -426,22 +434,39 @@ class ResNeXt101WSL(nn.Layer): ...@@ -426,22 +434,39 @@ class ResNeXt101WSL(nn.Layer):
x = self._out(x) x = self._out(x)
return x return x
def ResNeXt101_32x8d_wsl(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = ResNeXt101WSL(cardinality=32, width=8, **args) if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def ResNeXt101_32x8d_wsl(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt101WSL(cardinality=32, width=8, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_32x8d_wsl"], use_ssld=use_ssld)
return model return model
def ResNeXt101_32x16d_wsl(**args): def ResNeXt101_32x16d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=16, **args) model = ResNeXt101WSL(cardinality=32, width=16, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_32x16d_ws"], use_ssld=use_ssld)
return model return model
def ResNeXt101_32x32d_wsl(**args): def ResNeXt101_32x32d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=32, **args) model = ResNeXt101WSL(cardinality=32, width=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_32x32d_wsl"], use_ssld=use_ssld)
return model return model
def ResNeXt101_32x48d_wsl(**args): def ResNeXt101_32x48d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=48, **args) model = ResNeXt101WSL(cardinality=32, width=48, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_32x48d_wsl"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/resnext_vd.py
浏览文件 @ d8def846
...@@ -27,11 +27,18 @@ from paddle.nn.initializer import Uniform ...@@ -27,11 +27,18 @@ from paddle.nn.initializer import Uniform
import math import math
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"ResNeXt50_vd_32x4d", "ResNeXt50_vd_64x4d", "ResNeXt101_vd_32x4d",
"ResNeXt101_vd_64x4d", "ResNeXt152_vd_32x4d", "ResNeXt152_vd_64x4d"
]
MODEL_URLS = {
"ResNeXt50_vd_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_32x4d_pretrained.pdparams",
"ResNeXt50_vd_64x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_64x4d_pretrained.pdparams",
"ResNeXt101_vd_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_32x4d_pretrained.pdparams",
"ResNeXt101_vd_64x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_64x4d_pretrained.pdparams",
"ResNeXt152_vd_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_32x4d_pretrained.pdparams",
"ResNeXt152_vd_64x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_64x4d_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
def __init__( def __init__(
...@@ -235,32 +242,50 @@ class ResNeXt(nn.Layer): ...@@ -235,32 +242,50 @@ class ResNeXt(nn.Layer):
y = self.out(y) y = self.out(y)
return y return y
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def ResNeXt50_vd_32x4d(**args): if pretrained is False:
model = ResNeXt(layers=50, cardinality=32, **args) pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def ResNeXt50_vd_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=50, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt50_vd_32x4d"], use_ssld=use_ssld)
return model return model
def ResNeXt50_vd_64x4d(**args): def ResNeXt50_vd_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=50, cardinality=64, **args) model = ResNeXt(layers=50, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt50_vd_64x4d"], use_ssld=use_ssld)
return model return model
def ResNeXt101_vd_32x4d(**args): def ResNeXt101_vd_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=32, **args) model = ResNeXt(layers=101, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_vd_32x4d"], use_ssld=use_ssld)
return model return model
def ResNeXt101_vd_64x4d(**args): def ResNeXt101_vd_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=64, **args) model = ResNeXt(layers=101, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_vd_64x4d"], use_ssld=use_ssld)
return model return model
def ResNeXt152_vd_32x4d(**args): def ResNeXt152_vd_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=32, **args) model = ResNeXt(layers=152, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt152_vd_32x4d"], use_ssld=use_ssld)
return model return model
def ResNeXt152_vd_64x4d(**args): def ResNeXt152_vd_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=64, **args) model = ResNeXt(layers=152, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt152_vd_64x4d"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/rexnet.py
浏览文件 @ d8def846
...@@ -22,9 +22,17 @@ from paddle import ParamAttr ...@@ -22,9 +22,17 @@ from paddle import ParamAttr
import paddle.nn as nn import paddle.nn as nn
from math import ceil from math import ceil
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"ReXNet_1_0", "ReXNet_1_3", "ReXNet_1_5", "ReXNet_2_0", "ReXNet_3_0"
] MODEL_URLS = {
"ReXNet_1_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_0_pretrained.pdparams",
"ReXNet_1_3": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_3_pretrained.pdparams",
"ReXNet_1_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_5_32x4d_pretrained.pdparams",
"ReXNet_2_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_2_0_pretrained.pdparams",
"ReXNet_3_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_3_0_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
def conv_bn_act(out, def conv_bn_act(out,
...@@ -220,21 +228,44 @@ class ReXNetV1(nn.Layer): ...@@ -220,21 +228,44 @@ class ReXNetV1(nn.Layer):
return x return x
def ReXNet_1_0(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
return ReXNetV1(width_mult=1.0, **args) if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def ReXNet_1_0(pretrained=False, use_ssld=False, **kwargs):
model = ReXNetV1(width_mult=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ReXNet_1_0"], use_ssld=use_ssld)
return model
def ReXNet_1_3(**args): def ReXNet_1_3(pretrained=False, use_ssld=False, **kwargs):
return ReXNetV1(width_mult=1.3, **args) model = ReXNetV1(width_mult=1.3, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ReXNet_1_3"], use_ssld=use_ssld)
return model
def ReXNet_1_5(**args): def ReXNet_1_5(pretrained=False, use_ssld=False, **kwargs):
return ReXNetV1(width_mult=1.5, **args) model = ReXNetV1(width_mult=1.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ReXNet_1_5"], use_ssld=use_ssld)
return model
def ReXNet_2_0(**args): def ReXNet_2_0(pretrained=False, use_ssld=False, **kwargs):
return ReXNetV1(width_mult=2.0, **args) model = ReXNetV1(width_mult=2.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ReXNet_2_0"], use_ssld=use_ssld)
return model
def ReXNet_3_0(**args): def ReXNet_3_0(pretrained=False, use_ssld=False, **kwargs):
return ReXNetV1(width_mult=3.0, **args) model = ReXNetV1(width_mult=3.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ReXNet_3_0"], use_ssld=use_ssld)
return model
\ No newline at end of file
ppcls/arch/backbone/model_zoo/se_resnet_vd.py
浏览文件 @ d8def846
...@@ -26,10 +26,16 @@ from paddle.nn.initializer import Uniform ...@@ -26,10 +26,16 @@ from paddle.nn.initializer import Uniform
import math import math
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"SE_ResNet18_vd", "SE_ResNet34_vd", "SE_ResNet50_vd", "SE_ResNet101_vd",
"SE_ResNet152_vd", "SE_ResNet200_vd" MODEL_URLS = {
] "SE_ResNet18_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet18_vd_pretrained.pdparams",
"SE_ResNet34_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet34_vd_pretrained.pdparams",
"SE_ResNet50_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet50_vd_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -347,32 +353,33 @@ class SE_ResNet_vd(nn.Layer): ...@@ -347,32 +353,33 @@ class SE_ResNet_vd(nn.Layer):
y = self.out(y) y = self.out(y)
return y return y
def SE_ResNet18_vd(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = SE_ResNet_vd(layers=18, **args) if pretrained is False:
return model pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
def SE_ResNet34_vd(**args): elif isinstance(pretrained, str):
model = SE_ResNet_vd(layers=34, **args) load_dygraph_pretrain(model, pretrained)
return model else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
def SE_ResNet50_vd(**args): )
model = SE_ResNet_vd(layers=50, **args)
return model
def SE_ResNet18_vd(pretrained=False, use_ssld=False, **kwargs):
model = SE_ResNet_vd(layers=18, **kwargs)
def SE_ResNet101_vd(**args): _load_pretrained(pretrained, model, MODEL_URLS["SE_ResNet18_vd"], use_ssld=use_ssld)
model = SE_ResNet_vd(layers=101, **args)
return model return model
def SE_ResNet152_vd(**args): def SE_ResNet34_vd(pretrained=False, use_ssld=False, **kwargs):
model = SE_ResNet_vd(layers=152, **args) model = SE_ResNet_vd(layers=34, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNet34_vd"], use_ssld=use_ssld)
return model return model
def SE_ResNet200_vd(**args): def SE_ResNet50_vd(pretrained=False, use_ssld=False, **kwargs):
model = SE_ResNet_vd(layers=200, **args) model = SE_ResNet_vd(layers=50, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNet50_vd"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/se_resnext.py
浏览文件 @ d8def846
...@@ -27,7 +27,16 @@ from paddle.nn.initializer import Uniform ...@@ -27,7 +27,16 @@ from paddle.nn.initializer import Uniform
import math import math
__all__ = ["SE_ResNeXt50_32x4d", "SE_ResNeXt101_32x4d", "SE_ResNeXt152_64x4d"] from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SE_ResNeXt50_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_32x4d_pretrained.pdparams",
"SE_ResNeXt101_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt101_32x4d_pretrained.pdparams",
"SE_ResNeXt152_64x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt152_64x4d_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -301,17 +310,33 @@ class ResNeXt(nn.Layer): ...@@ -301,17 +310,33 @@ class ResNeXt(nn.Layer):
y = self.out(y) y = self.out(y)
return y return y
def SE_ResNeXt50_32x4d(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = ResNeXt(layers=50, cardinality=32, **args) if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def SE_ResNeXt50_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=50, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNeXt50_32x4d"], use_ssld=use_ssld)
return model return model
def SE_ResNeXt101_32x4d(**args): def SE_ResNeXt101_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=32, **args) model = ResNeXt(layers=101, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNeXt101_32x4d"], use_ssld=use_ssld)
return model return model
def SE_ResNeXt152_64x4d(**args): def SE_ResNeXt152_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=64, **args) model = ResNeXt(layers=152, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNeXt152_64x4d"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/se_resnext_vd.py
浏览文件 @ d8def846
...@@ -27,7 +27,16 @@ from paddle.nn.initializer import Uniform ...@@ -27,7 +27,16 @@ from paddle.nn.initializer import Uniform
import math import math
__all__ = ["SE_ResNeXt50_vd_32x4d", "SE_ResNeXt50_vd_32x4d", "SENet154_vd"] from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SE_ResNeXt50_vd_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams",
"SE_ResNeXt50_vd_32x4d": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams",
"SENet154_vd": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SENet154_vd_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -269,17 +278,33 @@ class ResNeXt(nn.Layer): ...@@ -269,17 +278,33 @@ class ResNeXt(nn.Layer):
y = self.out(y) y = self.out(y)
return y return y
def SE_ResNeXt50_vd_32x4d(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = ResNeXt(layers=50, cardinality=32, **args) if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def SE_ResNeXt50_vd_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=50, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNeXt50_vd_32x4d"], use_ssld=use_ssld)
return model return model
def SE_ResNeXt101_vd_32x4d(**args): def SE_ResNeXt101_vd_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=32, **args) model = ResNeXt(layers=101, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNeXt101_vd_32x4d"], use_ssld=use_ssld)
return model return model
def SENet154_vd(**args): def SENet154_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=64, **args) model = ResNeXt(layers=152, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SENet154_vd"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/shufflenet_v2.py
浏览文件 @ d8def846
...@@ -22,11 +22,19 @@ from paddle.nn import Layer, Conv2D, MaxPool2D, AdaptiveAvgPool2D, BatchNorm, Li ...@@ -22,11 +22,19 @@ from paddle.nn import Layer, Conv2D, MaxPool2D, AdaptiveAvgPool2D, BatchNorm, Li
from paddle.nn.initializer import KaimingNormal from paddle.nn.initializer import KaimingNormal
from paddle.nn.functional import swish from paddle.nn.functional import swish
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"ShuffleNetV2_x0_25", "ShuffleNetV2_x0_33", "ShuffleNetV2_x0_5",
"ShuffleNetV2_x1_0", "ShuffleNetV2_x1_5", "ShuffleNetV2_x2_0", MODEL_URLS = {
"ShuffleNetV2_swish" "ShuffleNetV2_x0_25": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams",
] "ShuffleNetV2_x0_33": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_33_pretrained.pdparams",
"ShuffleNetV2_x0_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_5_pretrained.pdparams",
"ShuffleNetV2_x1_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_0_pretrained.pdparams",
"ShuffleNetV2_x1_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_5_pretrained.pdparams",
"ShuffleNetV2_x2_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x2_0_pretrained.pdparams",
"ShuffleNetV2_swish": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_swish_pretrained.pdparams"
}
__all__ = list(MODEL_URLS.keys())
def channel_shuffle(x, groups): def channel_shuffle(x, groups):
...@@ -285,36 +293,56 @@ class ShuffleNet(Layer): ...@@ -285,36 +293,56 @@ class ShuffleNet(Layer):
return y return y
def ShuffleNetV2_x0_25(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
model = ShuffleNet(scale=0.25, **args) if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def ShuffleNetV2_x0_25(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=0.25, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x0_25"], use_ssld=use_ssld)
return model return model
def ShuffleNetV2_x0_33(**args): def ShuffleNetV2_x0_33(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=0.33, **args) model = ShuffleNet(scale=0.33, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x0_33"], use_ssld=use_ssld)
return model return model
def ShuffleNetV2_x0_5(**args): def ShuffleNetV2_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=0.5, **args) model = ShuffleNet(scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x0_5"], use_ssld=use_ssld)
return model return model
def ShuffleNetV2_x1_0(**args): def ShuffleNetV2_x1_0(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=1.0, **args) model = ShuffleNet(scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x1_0"], use_ssld=use_ssld)
return model return model
def ShuffleNetV2_x1_5(**args): def ShuffleNetV2_x1_5(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=1.5, **args) model = ShuffleNet(scale=1.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x1_5"], use_ssld=use_ssld)
return model return model
def ShuffleNetV2_x2_0(**args): def ShuffleNetV2_x2_0(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=2.0, **args) model = ShuffleNet(scale=2.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x2_0"], use_ssld=use_ssld)
return model return model
def ShuffleNetV2_swish(**args): def ShuffleNetV2_swish(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=1.0, act="swish", **args) model = ShuffleNet(scale=1.0, act="swish", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_swish"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/squeezenet.py
浏览文件 @ d8def846
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
import paddle import paddle
from paddle import ParamAttr from paddle import ParamAttr
import paddle.nn as nn import paddle.nn as nn
...@@ -5,7 +19,14 @@ import paddle.nn.functional as F ...@@ -5,7 +19,14 @@ import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
__all__ = ["SqueezeNet1_0", "SqueezeNet1_1"] from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SqueezeNet1_0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams",
"SqueezeNet1_1": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class MakeFireConv(nn.Layer): class MakeFireConv(nn.Layer):
...@@ -143,12 +164,26 @@ class SqueezeNet(nn.Layer): ...@@ -143,12 +164,26 @@ class SqueezeNet(nn.Layer):
x = paddle.squeeze(x, axis=[2, 3]) x = paddle.squeeze(x, axis=[2, 3])
return x return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def SqueezeNet1_0(**args): if pretrained is False:
model = SqueezeNet(version="1.0", **args) pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def SqueezeNet1_0(pretrained=False, use_ssld=False, **kwargs):
model = SqueezeNet(version="1.0", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SqueezeNet1_0"], use_ssld=use_ssld)
return model return model
def SqueezeNet1_1(**args): def SqueezeNet1_1(pretrained=False, use_ssld=False, **kwargs):
model = SqueezeNet(version="1.1", **args) model = SqueezeNet(version="1.1", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SqueezeNet1_1"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/swin_transformer.py
浏览文件 @ d8def846
...@@ -21,6 +21,19 @@ from paddle.nn.initializer import TruncatedNormal, Constant ...@@ -21,6 +21,19 @@ from paddle.nn.initializer import TruncatedNormal, Constant
from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SwinTransformer_tiny_patch4_window7_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_small_patch4_window7_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_base_patch4_window7_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_base_patch4_window12_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams",
"SwinTransformer_large_patch4_window7_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_large_patch4_window12_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class Mlp(nn.Layer): class Mlp(nn.Layer):
def __init__(self, def __init__(self,
...@@ -716,40 +729,56 @@ class SwinTransformer(nn.Layer): ...@@ -716,40 +729,56 @@ class SwinTransformer(nn.Layer):
flops += self.num_features * self.num_classes flops += self.num_features * self.num_classes
return flops return flops
def SwinTransformer_tiny_patch4_window7_224(**args): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def SwinTransformer_tiny_patch4_window7_224(pretrained=False, use_ssld=False, **kwargs):
model = SwinTransformer( model = SwinTransformer(
embed_dim=96, embed_dim=96,
depths=[2, 2, 6, 2], depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24], num_heads=[3, 6, 12, 24],
window_size=7, window_size=7,
drop_path_rate=0.2, drop_path_rate=0.2,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_tiny_patch4_window7_224"], use_ssld=use_ssld)
return model return model
def SwinTransformer_small_patch4_window7_224(**args): def SwinTransformer_small_patch4_window7_224(pretrained=False, use_ssld=False, **kwargs):
model = SwinTransformer( model = SwinTransformer(
embed_dim=96, embed_dim=96,
depths=[2, 2, 18, 2], depths=[2, 2, 18, 2],
num_heads=[3, 6, 12, 24], num_heads=[3, 6, 12, 24],
window_size=7, window_size=7,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_small_patch4_window7_224"], use_ssld=use_ssld)
return model return model
def SwinTransformer_base_patch4_window7_224(**args): def SwinTransformer_base_patch4_window7_224(pretrained=False, use_ssld=False, **kwargs):
model = SwinTransformer( model = SwinTransformer(
embed_dim=128, embed_dim=128,
depths=[2, 2, 18, 2], depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32], num_heads=[4, 8, 16, 32],
window_size=7, window_size=7,
drop_path_rate=0.5, drop_path_rate=0.5,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_base_patch4_window7_224"], use_ssld=use_ssld)
return model return model
def SwinTransformer_base_patch4_window12_384(**args): def SwinTransformer_base_patch4_window12_384(pretrained=False, use_ssld=False, **kwargs):
model = SwinTransformer( model = SwinTransformer(
img_size=384, img_size=384,
embed_dim=128, embed_dim=128,
...@@ -757,26 +786,29 @@ def SwinTransformer_base_patch4_window12_384(**args): ...@@ -757,26 +786,29 @@ def SwinTransformer_base_patch4_window12_384(**args):
num_heads=[4, 8, 16, 32], num_heads=[4, 8, 16, 32],
window_size=12, window_size=12,
drop_path_rate=0.5, # NOTE: do not appear in offical code drop_path_rate=0.5, # NOTE: do not appear in offical code
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_base_patch4_window12_384"], use_ssld=use_ssld)
return model return model
def SwinTransformer_large_patch4_window7_224(**args): def SwinTransformer_large_patch4_window7_224(pretrained=False, use_ssld=False, **kwargs):
model = SwinTransformer( model = SwinTransformer(
embed_dim=192, embed_dim=192,
depths=[2, 2, 18, 2], depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48], num_heads=[6, 12, 24, 48],
window_size=7, window_size=7,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_large_patch4_window7_224"], use_ssld=use_ssld)
return model return model
def SwinTransformer_large_patch4_window12_384(**args): def SwinTransformer_large_patch4_window12_384(pretrained=False, use_ssld=False, **kwargs):
model = SwinTransformer( model = SwinTransformer(
img_size=384, img_size=384,
embed_dim=192, embed_dim=192,
depths=[2, 2, 18, 2], depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48], num_heads=[6, 12, 24, 48],
window_size=12, window_size=12,
**args) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_large_patch4_window12_384"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/tnt.py
浏览文件 @ d8def846
...@@ -26,7 +26,7 @@ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_f ...@@ -26,7 +26,7 @@ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_f
MODEL_URLS = { MODEL_URLS = {
"TNT_small": "TNT_small":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/model_zoo/TNT_small_pretrained.pdparams" "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/TNT_small_pretrained.pdparams"
} }
......
ppcls/arch/backbone/model_zoo/vgg.py
浏览文件 @ d8def846
...@@ -5,7 +5,16 @@ import paddle.nn.functional as F ...@@ -5,7 +5,16 @@ import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
__all__ = ["VGG11", "VGG13", "VGG16", "VGG19"] from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"VGG11": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG11_pretrained.pdparams",
"VGG13": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG13_pretrained.pdparams",
"VGG16": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG16_pretrained.pdparams",
"VGG19": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG19_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBlock(nn.Layer): class ConvBlock(nn.Layer):
...@@ -131,22 +140,40 @@ class VGGNet(nn.Layer): ...@@ -131,22 +140,40 @@ class VGGNet(nn.Layer):
x = self._out(x) x = self._out(x)
return x return x
def VGG11(**args):
model = VGGNet(layers=11, **args) def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def VGG11(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VGGNet(layers=11, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG11"], use_ssld=use_ssld)
return model return model
def VGG13(**args): def VGG13(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VGGNet(layers=13, **args) model = VGGNet(layers=13, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG13"], use_ssld=use_ssld)
return model return model
def VGG16(**args): def VGG16(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VGGNet(layers=16, **args) model = VGGNet(layers=16, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG16"], use_ssld=use_ssld)
return model return model
def VGG19(**args): def VGG19(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VGGNet(layers=19, **args) model = VGGNet(layers=19, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG19"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/vision_transformer.py
浏览文件 @ d8def846
...@@ -19,12 +19,22 @@ import paddle ...@@ -19,12 +19,22 @@ import paddle
import paddle.nn as nn import paddle.nn as nn
from paddle.nn.initializer import TruncatedNormal, Constant, Normal from paddle.nn.initializer import TruncatedNormal, Constant, Normal
__all__ = [ from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
"VisionTransformer", "ViT_small_patch16_224", "ViT_base_patch16_224",
"ViT_base_patch16_384", "ViT_base_patch32_384", "ViT_large_patch16_224", MODEL_URLS = {
"ViT_large_patch16_384", "ViT_large_patch32_384", "ViT_huge_patch16_224", "ViT_small_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams",
"ViT_huge_patch32_384" "ViT_base_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_224_pretrained.pdparams",
] "ViT_base_patch16_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_384_pretrained.pdparams",
"ViT_base_patch32_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch32_384_pretrained.pdparams",
"ViT_large_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_224_pretrained.pdparams",
"ViT_large_patch16_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams",
"ViT_large_patch32_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams",
"ViT_huge_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_huge_patch16_224_pretrained.pdparams",
"ViT_huge_patch32_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_huge_patch32_384_pretrained.pdparams"
}
__all__ = list(MODEL_URLS.keys())
trunc_normal_ = TruncatedNormal(std=.02) trunc_normal_ = TruncatedNormal(std=.02)
normal_ = Normal normal_ = Normal
...@@ -300,7 +310,21 @@ class VisionTransformer(nn.Layer): ...@@ -300,7 +310,21 @@ class VisionTransformer(nn.Layer):
return x return x
def ViT_small_patch16_224(**kwargs): def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def ViT_small_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer( model = VisionTransformer(
patch_size=16, patch_size=16,
embed_dim=768, embed_dim=768,
...@@ -309,10 +333,12 @@ def ViT_small_patch16_224(**kwargs): ...@@ -309,10 +333,12 @@ def ViT_small_patch16_224(**kwargs):
mlp_ratio=3, mlp_ratio=3,
qk_scale=768**-0.5, qk_scale=768**-0.5,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_small_patch16_224"], use_ssld=use_ssld)
return model return model
def ViT_base_patch16_224(**kwargs):
def ViT_base_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer( model = VisionTransformer(
patch_size=16, patch_size=16,
embed_dim=768, embed_dim=768,
...@@ -322,10 +348,11 @@ def ViT_base_patch16_224(**kwargs): ...@@ -322,10 +348,11 @@ def ViT_base_patch16_224(**kwargs):
qkv_bias=True, qkv_bias=True,
epsilon=1e-6, epsilon=1e-6,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_base_patch16_224"], use_ssld=use_ssld)
return model return model
def ViT_base_patch16_384(**kwargs): def ViT_base_patch16_384(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer( model = VisionTransformer(
img_size=384, img_size=384,
patch_size=16, patch_size=16,
...@@ -336,10 +363,11 @@ def ViT_base_patch16_384(**kwargs): ...@@ -336,10 +363,11 @@ def ViT_base_patch16_384(**kwargs):
qkv_bias=True, qkv_bias=True,
epsilon=1e-6, epsilon=1e-6,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_base_patch16_384"], use_ssld=use_ssld)
return model return model
def ViT_base_patch32_384(**kwargs): def ViT_base_patch32_384(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer( model = VisionTransformer(
img_size=384, img_size=384,
patch_size=32, patch_size=32,
...@@ -350,10 +378,11 @@ def ViT_base_patch32_384(**kwargs): ...@@ -350,10 +378,11 @@ def ViT_base_patch32_384(**kwargs):
qkv_bias=True, qkv_bias=True,
epsilon=1e-6, epsilon=1e-6,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_base_patch32_384"], use_ssld=use_ssld)
return model return model
def ViT_large_patch16_224(**kwargs): def ViT_large_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer( model = VisionTransformer(
patch_size=16, patch_size=16,
embed_dim=1024, embed_dim=1024,
...@@ -363,10 +392,11 @@ def ViT_large_patch16_224(**kwargs): ...@@ -363,10 +392,11 @@ def ViT_large_patch16_224(**kwargs):
qkv_bias=True, qkv_bias=True,
epsilon=1e-6, epsilon=1e-6,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_large_patch16_224"], use_ssld=use_ssld)
return model return model
def ViT_large_patch16_384(**kwargs): def ViT_large_patch16_384(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer( model = VisionTransformer(
img_size=384, img_size=384,
patch_size=16, patch_size=16,
...@@ -377,10 +407,11 @@ def ViT_large_patch16_384(**kwargs): ...@@ -377,10 +407,11 @@ def ViT_large_patch16_384(**kwargs):
qkv_bias=True, qkv_bias=True,
epsilon=1e-6, epsilon=1e-6,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_large_patch16_384"], use_ssld=use_ssld)
return model return model
def ViT_large_patch32_384(**kwargs): def ViT_large_patch32_384(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer( model = VisionTransformer(
img_size=384, img_size=384,
patch_size=32, patch_size=32,
...@@ -391,10 +422,11 @@ def ViT_large_patch32_384(**kwargs): ...@@ -391,10 +422,11 @@ def ViT_large_patch32_384(**kwargs):
qkv_bias=True, qkv_bias=True,
epsilon=1e-6, epsilon=1e-6,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_large_patch32_384"], use_ssld=use_ssld)
return model return model
def ViT_huge_patch16_224(**kwargs): def ViT_huge_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer( model = VisionTransformer(
patch_size=16, patch_size=16,
embed_dim=1280, embed_dim=1280,
...@@ -402,10 +434,11 @@ def ViT_huge_patch16_224(**kwargs): ...@@ -402,10 +434,11 @@ def ViT_huge_patch16_224(**kwargs):
num_heads=16, num_heads=16,
mlp_ratio=4, mlp_ratio=4,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_huge_patch16_224"], use_ssld=use_ssld)
return model return model
def ViT_huge_patch32_384(**kwargs): def ViT_huge_patch32_384(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer( model = VisionTransformer(
img_size=384, img_size=384,
patch_size=32, patch_size=32,
...@@ -414,4 +447,5 @@ def ViT_huge_patch32_384(**kwargs): ...@@ -414,4 +447,5 @@ def ViT_huge_patch32_384(**kwargs):
num_heads=16, num_heads=16,
mlp_ratio=4, mlp_ratio=4,
**kwargs) **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_huge_patch32_384"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/xception.py
浏览文件 @ d8def846
...@@ -8,7 +8,16 @@ from paddle.nn.initializer import Uniform ...@@ -8,7 +8,16 @@ from paddle.nn.initializer import Uniform
import math import math
import sys import sys
__all__ = ['Xception41', 'Xception65', 'Xception71']
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"Xception41": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_pretrained.pdparams",
"Xception65": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_pretrained.pdparams",
"Xception71": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception71_pretrained.pdparams"
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer): class ConvBNLayer(nn.Layer):
...@@ -329,17 +338,32 @@ class Xception(nn.Layer): ...@@ -329,17 +338,32 @@ class Xception(nn.Layer):
x = self._exit_flow(x) x = self._exit_flow(x)
return x return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
def Xception41(**args): if pretrained is False:
model = Xception(entry_flow_block_num=3, middle_flow_block_num=8, **args) pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def Xception41(pretrained=False, use_ssld=False, **kwargs):
model = Xception(entry_flow_block_num=3, middle_flow_block_num=8, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Xception41"], use_ssld=use_ssld)
return model return model
def Xception65(**args): def Xception65(pretrained=False, use_ssld=False, **kwargs):
model = Xception(entry_flow_block_num=3, middle_flow_block_num=16, **args) model = Xception(entry_flow_block_num=3, middle_flow_block_num=16, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Xception65"], use_ssld=use_ssld)
return model return model
def Xception71(**args): def Xception71(pretrained=False, use_ssld=False, **kwargs):
model = Xception(entry_flow_block_num=5, middle_flow_block_num=16, **args) model = Xception(entry_flow_block_num=5, middle_flow_block_num=16, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Xception71"], use_ssld=use_ssld)
return model return model
ppcls/arch/backbone/model_zoo/xception_deeplab.py
浏览文件 @ d8def846
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
import paddle import paddle
from paddle import ParamAttr from paddle import ParamAttr
import paddle.nn as nn import paddle.nn as nn
...@@ -5,7 +19,12 @@ import paddle.nn.functional as F ...@@ -5,7 +19,12 @@ import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
__all__ = ["Xception41_deeplab", "Xception65_deeplab", "Xception71_deeplab"] from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"Xception41_deeplab": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_deeplab_pretrained.pdparams",
"Xception65_deeplab": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_deeplab_pretrained.pdparams"}
__all__ = list(MODEL_URLS.keys())
def check_data(data, number): def check_data(data, number):
...@@ -369,18 +388,28 @@ class XceptionDeeplab(nn.Layer): ...@@ -369,18 +388,28 @@ class XceptionDeeplab(nn.Layer):
x = paddle.squeeze(x, axis=[2, 3]) x = paddle.squeeze(x, axis=[2, 3])
x = self._fc(x) x = self._fc(x)
return x return x
def _load_pretrained(pretrained, model, model_url, use_ssld=False):
if pretrained is False:
pass
elif pretrained is True:
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
elif isinstance(pretrained, str):
load_dygraph_pretrain(model, pretrained)
else:
raise RuntimeError(
"pretrained type is not available. Please use `string` or `boolean` type."
)
def Xception41_deeplab(**args): def Xception41_deeplab(pretrained=False, use_ssld=False, **kwargs):
model = XceptionDeeplab('xception_41', **args) model = XceptionDeeplab('xception_41', **kwargs)
return model _load_pretrained(pretrained, model, MODEL_URLS["Xception41_deeplab"], use_ssld=use_ssld)
def Xception65_deeplab(**args):
model = XceptionDeeplab("xception_65", **args)
return model return model
def Xception71_deeplab(**args): def Xception65_deeplab(pretrained=False, use_ssld=False, **kwargs):
model = XceptionDeeplab("xception_71", **args) model = XceptionDeeplab("xception_65", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Xception65_deeplab"], use_ssld=use_ssld)
return model return model
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