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PaddleClas
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
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.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.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.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.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.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 import Res2Net50_26w_4s, Res2Net50_14w_8s
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 import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_64x4d
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
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.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.inception_v4 import InceptionV4
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.squeezenet import SqueezeNet1_0, SqueezeNet1_1
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
from paddle import ParamAttr
import paddle.nn as nn
......@@ -7,8 +21,11 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
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):
def __init__(self,
......@@ -126,7 +143,19 @@ class AlexNetDY(nn.Layer):
x = self._fc8(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):
model = AlexNetDY(**args)
def AlexNet(pretrained=False, use_ssld=False, **kwargs):
model = AlexNetDY(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["AlexNet"], use_ssld=use_ssld)
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
from paddle import ParamAttr
import paddle.nn as nn
......@@ -7,8 +21,11 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
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):
def __init__(self,
......@@ -155,7 +172,19 @@ class DarkNet(nn.Layer):
x = self._out(x)
return x
def DarkNet53(**args):
model = DarkNet(**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 DarkNet53(pretrained=False, use_ssld=False, **kwargs):
model = DarkNet(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DarkNet53"], use_ssld=use_ssld)
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");
# you may not use this file except in compliance with the License.
......@@ -26,9 +26,16 @@ from paddle.nn.initializer import Uniform
import math
__all__ = [
"DenseNet121", "DenseNet161", "DenseNet169", "DenseNet201", "DenseNet264"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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):
......@@ -282,27 +289,43 @@ class DenseNet(nn.Layer):
y = self.out(y)
return y
def DenseNet121(**args):
model = DenseNet(layers=121, **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 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
def DenseNet161(**args):
model = DenseNet(layers=161, **args)
def DenseNet161(pretrained=False, use_ssld=False, **kwargs):
model = DenseNet(layers=161, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DenseNet161"], use_ssld=use_ssld)
return model
def DenseNet169(**args):
model = DenseNet(layers=169, **args)
def DenseNet169(pretrained=False, use_ssld=False, **kwargs):
model = DenseNet(layers=169, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DenseNet169"], use_ssld=use_ssld)
return model
def DenseNet201(**args):
model = DenseNet(layers=201, **args)
def DenseNet201(pretrained=False, use_ssld=False, **kwargs):
model = DenseNet(layers=201, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DenseNet201"], use_ssld=use_ssld)
return model
def DenseNet264(**args):
model = DenseNet(layers=264, **args)
def DenseNet264(pretrained=False, use_ssld=False, **kwargs):
model = DenseNet(layers=264, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DenseNet264"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/distilled_vision_transformer.py
浏览文件 @ d8def846
......@@ -16,12 +16,20 @@ import paddle
import paddle.nn as nn
from .vision_transformer import VisionTransformer, Identity, trunc_normal_, zeros_
__all__ = [
'DeiT_tiny_patch16_224', 'DeiT_small_patch16_224', 'DeiT_base_patch16_224',
'DeiT_tiny_distilled_patch16_224', 'DeiT_small_distilled_patch16_224',
'DeiT_base_distilled_patch16_224', 'DeiT_base_patch16_384',
'DeiT_base_distilled_patch16_384'
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"DeiT_tiny_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams",
"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):
......@@ -90,7 +98,20 @@ class DistilledVisionTransformer(VisionTransformer):
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(
patch_size=16,
embed_dim=192,
......@@ -100,10 +121,11 @@ def DeiT_tiny_patch16_224(**kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_tiny_patch16_224"], use_ssld=use_ssld)
return model
def DeiT_small_patch16_224(**kwargs):
def DeiT_small_patch16_224(pretrained=False, use_ssld=False, **kwargs):
model = VisionTransformer(
patch_size=16,
embed_dim=384,
......@@ -113,10 +135,11 @@ def DeiT_small_patch16_224(**kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_small_patch16_224"], use_ssld=use_ssld)
return model
def DeiT_base_patch16_224(**kwargs):
def DeiT_base_patch16_224(pretrained=False, use_ssld=False, **kwargs):
model = VisionTransformer(
patch_size=16,
embed_dim=768,
......@@ -126,10 +149,11 @@ def DeiT_base_patch16_224(**kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_base_patch16_224"], use_ssld=use_ssld)
return model
def DeiT_tiny_distilled_patch16_224(**kwargs):
def DeiT_tiny_distilled_patch16_224(pretrained=False, use_ssld=False, **kwargs):
model = DistilledVisionTransformer(
patch_size=16,
embed_dim=192,
......@@ -139,10 +163,11 @@ def DeiT_tiny_distilled_patch16_224(**kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_tiny_distilled_patch16_224"], use_ssld=use_ssld)
return model
def DeiT_small_distilled_patch16_224(**kwargs):
def DeiT_small_distilled_patch16_224(pretrained=False, use_ssld=False, **kwargs):
model = DistilledVisionTransformer(
patch_size=16,
embed_dim=384,
......@@ -152,10 +177,11 @@ def DeiT_small_distilled_patch16_224(**kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_small_distilled_patch16_224"], use_ssld=use_ssld)
return model
def DeiT_base_distilled_patch16_224(**kwargs):
def DeiT_base_distilled_patch16_224(pretrained=False, use_ssld=False, **kwargs):
model = DistilledVisionTransformer(
patch_size=16,
embed_dim=768,
......@@ -165,10 +191,11 @@ def DeiT_base_distilled_patch16_224(**kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_base_distilled_patch16_224"], use_ssld=use_ssld)
return model
def DeiT_base_patch16_384(**kwargs):
def DeiT_base_patch16_384(pretrained=False, use_ssld=False, **kwargs):
model = VisionTransformer(
img_size=384,
patch_size=16,
......@@ -179,10 +206,11 @@ def DeiT_base_patch16_384(**kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_base_patch16_384"], use_ssld=use_ssld)
return model
def DeiT_base_distilled_patch16_384(**kwargs):
def DeiT_base_distilled_patch16_384(pretrained=False, use_ssld=False, **kwargs):
model = DistilledVisionTransformer(
img_size=384,
patch_size=16,
......@@ -193,4 +221,5 @@ def DeiT_base_distilled_patch16_384(**kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DeiT_base_distilled_patch16_384"], use_ssld=use_ssld)
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
MODEL_URLS = {
"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":
"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":
"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":
"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":
"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":
"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":
"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":
"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":
"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":
"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
import math
__all__ = [
"DPN",
"DPN68",
"DPN92",
"DPN98",
"DPN107",
"DPN131",
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"DPN68": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN68_pretrained.pdparams",
"DPN92": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN92_pretrained.pdparams",
"DPN98": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN98_pretrained.pdparams",
"DPN107": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN107_pretrained.pdparams",
"DPN131": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN131_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer):
......@@ -398,28 +400,45 @@ class DPN(nn.Layer):
net_arg['init_padding'] = init_padding
return net_arg
def DPN68(**args):
model = DPN(layers=68, **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 DPN68(pretrained=False, use_ssld=False, **kwargs):
model = DPN(layers=68, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DPN68"])
return model
def DPN92(**args):
model = DPN(layers=92, **args)
def DPN92(pretrained=False, use_ssld=False, **kwargs):
model = DPN(layers=92, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DPN92"])
return model
def DPN98(**args):
model = DPN(layers=98, **args)
def DPN98(pretrained=False, use_ssld=False, **kwargs):
model = DPN(layers=98, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DPN98"])
return model
def DPN107(**args):
model = DPN(layers=107, **args)
def DPN107(pretrained=False, use_ssld=False, **kwargs):
model = DPN(layers=107, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["DPN107"])
return model
def DPN131(**args):
model = DPN(layers=131, **args)
return model
def DPN131(pretrained=False, use_ssld=False, **kwargs):
model = DPN(layers=131, **kwargs)
_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
import re
import copy
__all__ = [
'EfficientNet', 'EfficientNetB0_small', 'EfficientNetB0', 'EfficientNetB1',
'EfficientNetB2', 'EfficientNetB3', 'EfficientNetB4', 'EfficientNetB5',
'EfficientNetB6', 'EfficientNetB7'
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"EfficientNetB0_small": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_small_pretrained.pdparams",
"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', [
'batch_norm_momentum',
......@@ -783,119 +792,159 @@ class EfficientNet(nn.Layer):
x = self._fc(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',
override_params=None,
use_se=False,
**args):
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b0',
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB0_small"])
return model
def EfficientNetB0(padding_type='SAME',
override_params=None,
use_se=True,
**args):
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b0',
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB0"])
return model
def EfficientNetB1(padding_type='SAME',
override_params=None,
use_se=True,
**args):
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b1',
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB1"])
return model
def EfficientNetB2(padding_type='SAME',
override_params=None,
use_se=True,
**args):
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b2',
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB2"])
return model
def EfficientNetB3(padding_type='SAME',
override_params=None,
use_se=True,
**args):
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b3',
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB3"])
return model
def EfficientNetB4(padding_type='SAME',
override_params=None,
use_se=True,
**args):
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b4',
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB4"])
return model
def EfficientNetB5(padding_type='SAME',
override_params=None,
use_se=True,
**args):
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b5',
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB5"])
return model
def EfficientNetB6(padding_type='SAME',
override_params=None,
use_se=True,
**args):
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b6',
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["EfficientNetB6"])
return model
def EfficientNetB7(padding_type='SAME',
override_params=None,
use_se=True,
**args):
pretrained=False,
use_ssld=False,
**kwargs):
model = EfficientNet(
name='b7',
padding_type=padding_type,
override_params=override_params,
use_se=use_se,
**args)
return model
**kwargs)
_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");
# you may not use this file except in compliance with the License.
......@@ -21,7 +21,14 @@ from paddle.nn import Conv2D, BatchNorm, AdaptiveAvgPool2D, Linear
from paddle.regularizer import L2Decay
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):
......@@ -315,17 +322,33 @@ class GhostNet(nn.Layer):
new_v += divisor
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):
model = GhostNet(scale=0.5)
def GhostNet_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = GhostNet(scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["GhostNet_x0_5"], use_ssld=use_ssld)
return model
def GhostNet_x1_0(**args):
model = GhostNet(scale=1.0)
def GhostNet_x1_0(pretrained=False, use_ssld=False, **kwargs):
model = GhostNet(scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["GhostNet_x1_0"], use_ssld=use_ssld)
return model
def GhostNet_x1_3(**args):
model = GhostNet(scale=1.3)
def GhostNet_x1_3(pretrained=False, use_ssld=False, **kwargs):
model = GhostNet(scale=1.3, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["GhostNet_x1_3"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/googlenet.py
浏览文件 @ d8def846
......@@ -8,7 +8,12 @@ from paddle.nn.initializer import Uniform
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):
......@@ -200,8 +205,22 @@ class GoogLeNetDY(nn.Layer):
x = self._drop_o2(x)
out2 = self._out2(x)
return [out, out1, out2]
def GoogLeNet(**args):
model = GoogLeNetDY(**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 GoogLeNet(pretrained=False, use_ssld=False, **kwargs):
model = GoogLeNetDY(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["GoogLeNet"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/gvt.py
浏览文件 @ d8def846
......@@ -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 Block as ViTBlock
__all__ = [
"CPVTV2", "PCPVT", "ALTGVT", "pcpvt_small", "pcpvt_base", "pcpvt_large",
"alt_gvt_small", "alt_gvt_base", "alt_gvt_large"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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):
......@@ -559,8 +568,20 @@ class ALTGVT(PCPVT):
cur += depths[k]
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(
patch_size=4,
embed_dims=[64, 128, 320, 512],
......@@ -572,11 +593,11 @@ def pcpvt_small(pretrained=False, **kwargs):
depths=[3, 4, 6, 3],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["pcpvt_small"], use_ssld=use_ssld)
return model
def pcpvt_base(pretrained=False, **kwargs):
def pcpvt_base(pretrained=False, use_ssld=False, **kwargs):
model = CPVTV2(
patch_size=4,
embed_dims=[64, 128, 320, 512],
......@@ -588,11 +609,11 @@ def pcpvt_base(pretrained=False, **kwargs):
depths=[3, 4, 18, 3],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["pcpvt_base"], use_ssld=use_ssld)
return model
def pcpvt_large(pretrained=False, **kwargs):
def pcpvt_large(pretrained=False, use_ssld=False, **kwargs):
model = CPVTV2(
patch_size=4,
embed_dims=[64, 128, 320, 512],
......@@ -604,11 +625,11 @@ def pcpvt_large(pretrained=False, **kwargs):
depths=[3, 8, 27, 3],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["pcpvt_large"], use_ssld=use_ssld)
return model
def alt_gvt_small(pretrained=False, **kwargs):
def alt_gvt_small(pretrained=False, use_ssld=False, **kwargs):
model = ALTGVT(
patch_size=4,
embed_dims=[64, 128, 256, 512],
......@@ -621,11 +642,11 @@ def alt_gvt_small(pretrained=False, **kwargs):
wss=[7, 7, 7, 7],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["alt_gvt_small"], use_ssld=use_ssld)
return model
def alt_gvt_base(pretrained=False, **args):
def alt_gvt_base(pretrained=False, use_ssld=False, **kwargs):
model = ALTGVT(
patch_size=4,
embed_dims=[96, 192, 384, 768],
......@@ -637,12 +658,12 @@ def alt_gvt_base(pretrained=False, **args):
depths=[2, 2, 18, 2],
wss=[7, 7, 7, 7],
sr_ratios=[8, 4, 2, 1],
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["alt_gvt_base"], use_ssld=use_ssld)
return model
def alt_gvt_large(pretrained=False, **kwargs):
def alt_gvt_large(pretrained=False, use_ssld=False, **kwargs):
model = ALTGVT(
patch_size=4,
embed_dims=[128, 256, 512, 1024],
......@@ -655,5 +676,5 @@ def alt_gvt_large(pretrained=False, **kwargs):
wss=[7, 7, 7, 7],
sr_ratios=[8, 4, 2, 1],
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["alt_gvt_large"], use_ssld=use_ssld)
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
MODEL_URLS = {
'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':
'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':
'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':
'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
import math
__all__ = [
"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_W18_C",
"SE_HRNet_W30_C",
"SE_HRNet_W32_C",
"SE_HRNet_W40_C",
"SE_HRNet_W44_C",
"SE_HRNet_W48_C",
"SE_HRNet_W60_C",
"SE_HRNet_W64_C",
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"HRNet_W18_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W18_C_pretrained.pdparams",
"HRNet_W30_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W30_C_pretrained.pdparams",
"HRNet_W32_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W32_C_pretrained.pdparams",
"HRNet_W40_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W40_C_pretrained.pdparams",
"HRNet_W44_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W44_C_pretrained.pdparams",
"HRNet_W48_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams",
"HRNet_W64_C": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
class ConvBNLayer(nn.Layer):
......@@ -661,82 +655,62 @@ class HRNet(nn.Layer):
y = self.out(y)
return y
def HRNet_W18_C(**args):
model = HRNet(width=18, **args)
return model
def HRNet_W30_C(**args):
model = HRNet(width=30, **args)
return model
def HRNet_W32_C(**args):
model = HRNet(width=32, **args)
return model
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)
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 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)
return model
def SE_HRNet_W30_C(**args):
model = HRNet(width=30, has_se=True, **args)
def HRNet_W30_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=30, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W30_C"], use_ssld=use_ssld)
return model
def SE_HRNet_W32_C(**args):
model = HRNet(width=32, has_se=True, **args)
def HRNet_W32_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=32, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W32_C"], use_ssld=use_ssld)
return model
def SE_HRNet_W40_C(**args):
model = HRNet(width=40, has_se=True, **args)
def HRNet_W40_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=40, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W40_C"], use_ssld=use_ssld)
return model
def SE_HRNet_W44_C(**args):
model = HRNet(width=44, has_se=True, **args)
def HRNet_W44_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=44, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W44_C"], use_ssld=use_ssld)
return model
def SE_HRNet_W48_C(**args):
model = HRNet(width=48, has_se=True, **args)
def HRNet_W48_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=48, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W48_C"], use_ssld=use_ssld)
return model
def SE_HRNet_W60_C(**args):
model = HRNet(width=60, has_se=True, **args)
def HRNet_W64_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=64, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["HRNet_W64_C"], use_ssld=use_ssld)
return model
def SE_HRNet_W64_C(**args):
model = HRNet(width=64, has_se=True, **args)
def SE_HRNet_W64_C(pretrained=False, use_ssld=False, **kwarg):
model = HRNet(width=64, **kwarg)
_load_pretrained(pretrained, model, MODEL_URLS["SE_HRNet_W64_C"], use_ssld=use_ssld)
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");
# you may not use this file except in compliance with the License.
......@@ -26,7 +26,11 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
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):
......@@ -425,9 +429,9 @@ class InceptionE(nn.Layer):
return outputs
class InceptionV3(nn.Layer):
class Inception_V3(nn.Layer):
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_c_list = [[768, 768, 768, 768], [128, 160, 160, 192]]
......@@ -472,10 +476,28 @@ class InceptionV3(nn.Layer):
def forward(self, x):
y = self.inception_stem(x)
for inception_block in self.inception_block_list:
y = inception_block(y)
y = inception_block(y)
y = self.gap(y)
y = paddle.reshape(y, shape=[-1, 2048])
y = self.drop(y)
y = self.out(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
from paddle.nn.initializer import Uniform
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):
......@@ -450,6 +454,19 @@ class InceptionV4DY(nn.Layer):
return x
def InceptionV4(**args):
model = InceptionV4DY(**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 InceptionV4(pretrained=False, use_ssld=False, **kwargs):
model = InceptionV4DY(**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["InceptionV4"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/levit.py
浏览文件 @ d8def846
......@@ -24,7 +24,17 @@ from paddle.regularizer import L2Decay
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):
......@@ -479,37 +489,59 @@ specification = {
},
}
def LeViT_128S(class_dim=1000, distillation=True, pretrained=False):
return model_factory(
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 LeViT_128S(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
model = model_factory(
**specification['LeViT_128S'],
class_dim=class_dim,
distillation=distillation)
_load_pretrained(pretrained, model, MODEL_URLS["LeViT_128S"], use_ssld=use_ssld)
return model
def LeViT_128(class_dim=1000, distillation=True):
return model_factory(
def LeViT_128(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
model = model_factory(
**specification['LeViT_128'],
class_dim=class_dim,
distillation=distillation)
_load_pretrained(pretrained, model, MODEL_URLS["LeViT_128"], use_ssld=use_ssld)
return model
def LeViT_192(class_dim=1000, distillation=True):
return model_factory(
def LeViT_192(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
model = model_factory(
**specification['LeViT_192'],
class_dim=class_dim,
distillation=distillation)
_load_pretrained(pretrained, model, MODEL_URLS["LeViT_192"], use_ssld=use_ssld)
return model
def LeViT_256(class_dim=1000, distillation=False):
return model_factory(
def LeViT_256(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
model = model_factory(
**specification['LeViT_256'],
class_dim=class_dim,
distillation=distillation)
_load_pretrained(pretrained, model, MODEL_URLS["LeViT_256"], use_ssld=use_ssld)
return model
def LeViT_384(class_dim=1000, distillation=True):
return model_factory(
def LeViT_384(pretrained=False, use_ssld=False, class_dim=1000, distillation=False, **kwargs):
model = model_factory(
**specification['LeViT_384'],
class_dim=class_dim,
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 @@
https://arxiv.org/abs/1907.09595.
"""
__all__ = ['MixNet_S', 'MixNet_M', 'MixNet_L']
import os
from inspect import isfunction
from functools import reduce
import paddle
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):
"""
......@@ -755,13 +761,33 @@ def get_mixnet(version, width_scale, model_name=None, **kwargs):
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):
"""
MixNet-S model from 'MixConv: Mixed Depthwise Convolutional Kernels,'
https://arxiv.org/abs/1907.09595.
"""
return get_mixnet(
model = get_mixnet(
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):
......@@ -769,14 +795,19 @@ def MixNet_M(**kwargs):
MixNet-M model from 'MixConv: Mixed Depthwise Convolutional Kernels,'
https://arxiv.org/abs/1907.09595.
"""
return get_mixnet(
model = get_mixnet(
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):
"""
MixNet-L model from 'MixConv: Mixed Depthwise Convolutional Kernels,'
MixNet-S model from 'MixConv: Mixed Depthwise Convolutional Kernels,'
https://arxiv.org/abs/1907.09595.
"""
return get_mixnet(
model = get_mixnet(
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
from paddle.nn.initializer import KaimingNormal
import math
__all__ = [
"MobileNetV1_x0_25", "MobileNetV1_x0_5", "MobileNetV1_x0_75", "MobileNetV1"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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):
......@@ -245,22 +250,39 @@ class MobileNet(nn.Layer):
y = self.out(y)
return y
def MobileNetV1_x0_25(**args):
model = MobileNet(scale=0.25, **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 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
def MobileNetV1_x0_5(**args):
model = MobileNet(scale=0.5, **args)
def MobileNetV1_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_5"], use_ssld=use_ssld)
return model
def MobileNetV1_x0_75(**args):
model = MobileNet(scale=0.75, **args)
def MobileNetV1_x0_75(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.75, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_75"], use_ssld=use_ssld)
return model
def MobileNetV1(**args):
model = MobileNet(scale=1.0, **args)
return model
def MobileNetV1(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=1.0, **kwargs)
_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
import math
__all__ = [
"MobileNetV2_x0_25", "MobileNetV2_x0_5", "MobileNetV2_x0_75",
"MobileNetV2", "MobileNetV2_x1_5", "MobileNetV2_x2_0"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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):
......@@ -149,7 +155,7 @@ class InvresiBlocks(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__()
self.scale = scale
self.class_dim = class_dim
......@@ -216,33 +222,52 @@ class MobileNet(nn.Layer):
y = paddle.flatten(y, start_axis=1, stop_axis=-1)
y = self.out(y)
return y
def MobileNetV2_x0_25(**args):
model = MobileNet(scale=0.25, **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 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
def MobileNetV2_x0_5(**args):
model = MobileNet(scale=0.5, **args)
def MobileNetV2_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2_x0_5"], use_ssld=use_ssld)
return model
def MobileNetV2_x0_75(**args):
model = MobileNet(scale=0.75, **args)
def MobileNetV2_x0_75(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=0.75, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2_x0_75"], use_ssld=use_ssld)
return model
def MobileNetV2(**args):
model = MobileNet(scale=1.0, **args)
def MobileNetV2(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2"], use_ssld=use_ssld)
return model
def MobileNetV2_x1_5(**args):
model = MobileNet(scale=1.5, **args)
def MobileNetV2_x1_5(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=1.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2_x1_5"], use_ssld=use_ssld)
return model
def MobileNetV2_x2_0(**args):
model = MobileNet(scale=2.0, **args)
def MobileNetV2_x2_0(pretrained=False, use_ssld=False, **kwargs):
model = MobileNet(scale=2.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV2_x2_0"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/mobilenet_v3.py
浏览文件 @ d8def846
......@@ -28,13 +28,20 @@ from paddle.regularizer import L2Decay
import math
__all__ = [
"MobileNetV3_small_x0_35", "MobileNetV3_small_x0_5",
"MobileNetV3_small_x0_75", "MobileNetV3_small_x1_0",
"MobileNetV3_small_x1_25", "MobileNetV3_large_x0_35",
"MobileNetV3_large_x0_5", "MobileNetV3_large_x0_75",
"MobileNetV3_large_x1_0", "MobileNetV3_large_x1_25"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"MobileNetV3_small_x0_35": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_35_pretrained.pdparams",
"MobileNetV3_small_x0_5": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_5_pretrained.pdparams",
"MobileNetV3_small_x0_75": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_small_x0_75_pretrained.pdparams",
"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):
......@@ -308,52 +315,75 @@ class SEModule(nn.Layer):
outputs = hardsigmoid(outputs, slope=0.2, offset=0.5)
return paddle.multiply(x=inputs, y=outputs)
def MobileNetV3_small_x0_35(**args):
model = MobileNetV3(model_name="small", scale=0.35, **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 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
def MobileNetV3_small_x0_5(**args):
model = MobileNetV3(model_name="small", scale=0.5, **args)
def MobileNetV3_small_x0_5(pretrained=False, use_ssld=False, **kwargs):
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
def MobileNetV3_small_x0_75(**args):
model = MobileNetV3(model_name="small", scale=0.75, **args)
def MobileNetV3_small_x0_75(pretrained=False, use_ssld=False, **kwargs):
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
def MobileNetV3_small_x1_0(**args):
model = MobileNetV3(model_name="small", scale=1.0, **args)
def MobileNetV3_small_x1_0(pretrained=False, use_ssld=False, **kwargs):
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
def MobileNetV3_small_x1_25(**args):
model = MobileNetV3(model_name="small", scale=1.25, **args)
def MobileNetV3_small_x1_25(pretrained=False, use_ssld=False, **kwargs):
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
def MobileNetV3_large_x0_35(**args):
model = MobileNetV3(model_name="large", scale=0.35, **args)
def MobileNetV3_large_x0_35(pretrained=False, use_ssld=False, **kwargs):
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
def MobileNetV3_large_x0_5(**args):
model = MobileNetV3(model_name="large", scale=0.5, **args)
def MobileNetV3_large_x0_5(pretrained=False, use_ssld=False, **kwargs):
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
def MobileNetV3_large_x0_75(**args):
model = MobileNetV3(model_name="large", scale=0.75, **args)
def MobileNetV3_large_x0_75(pretrained=False, use_ssld=False, **kwargs):
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
def MobileNetV3_large_x1_0(**args):
model = MobileNetV3(model_name="large", scale=1.0, **args)
def MobileNetV3_large_x1_0(pretrained=False, use_ssld=False, **kwargs):
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
def MobileNetV3_large_x1_25(**args):
model = MobileNetV3(model_name="large", scale=1.25, **args)
def MobileNetV3_large_x1_25(pretrained=False, use_ssld=False, **kwargs):
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
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
MODEL_URLS = {
"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":
"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":
"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":
"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":
"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
from paddle.nn.initializer import Uniform
import math
__all__ = [
"RegNetX_200MF", "RegNetX_4GF", "RegNetX_32GF", "RegNetY_200MF",
"RegNetY_4GF", "RegNetY_32GF"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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):
......@@ -308,14 +315,28 @@ class RegNet(nn.Layer):
y = self.out(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(
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
def RegNetX_4GF(**args):
def RegNetX_4GF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet(
w_a=38.65,
w_0=96,
......@@ -324,11 +345,12 @@ def RegNetX_4GF(**args):
group_w=40,
bot_mul=1.0,
q=8,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_4GF"], use_ssld=use_ssld)
return model
def RegNetX_32GF(**args):
def RegNetX_32GF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet(
w_a=69.86,
w_0=320,
......@@ -337,11 +359,12 @@ def RegNetX_32GF(**args):
group_w=168,
bot_mul=1.0,
q=8,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model
def RegNetY_200MF(**args):
def RegNetY_200MF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet(
w_a=36.44,
w_0=24,
......@@ -351,11 +374,12 @@ def RegNetY_200MF(**args):
bot_mul=1.0,
q=8,
se_on=True,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model
def RegNetY_4GF(**args):
def RegNetY_4GF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet(
w_a=31.41,
w_0=96,
......@@ -365,11 +389,12 @@ def RegNetY_4GF(**args):
bot_mul=1.0,
q=8,
se_on=True,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model
def RegNetY_32GF(**args):
def RegNetY_32GF(pretrained=False, use_ssld=False, **kwargs):
model = RegNet(
w_a=115.89,
w_0=232,
......@@ -379,5 +404,6 @@ def RegNetY_32GF(**args):
bot_mul=1.0,
q=8,
se_on=True,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RegNetX_32GF"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/repvgg.py
浏览文件 @ d8def846
......@@ -2,22 +2,29 @@ import paddle.nn as nn
import paddle
import numpy as np
__all__ = [
'RepVGG',
'RepVGG_A0',
'RepVGG_A1',
'RepVGG_A2',
'RepVGG_B0',
'RepVGG_B1',
'RepVGG_B2',
'RepVGG_B3',
'RepVGG_B1g2',
'RepVGG_B1g4',
'RepVGG_B2g2',
'RepVGG_B2g4',
'RepVGG_B3g2',
'RepVGG_B3g4',
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"RepVGG_A0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A0_pretrained.pdparams",
"RepVGG_A1": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A1_pretrained.pdparams",
"RepVGG_A2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_A2_pretrained.pdparams",
"RepVGG_B0": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B0_pretrained.pdparams",
"RepVGG_B1": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1_pretrained.pdparams",
"RepVGG_B2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2_pretrained.pdparams",
"RepVGG_B3": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3_pretrained.pdparams",
"RepVGG_B1g2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g2_pretrained.pdparams",
"RepVGG_B1g4": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B1g4_pretrained.pdparams",
"RepVGG_B2g2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g2_pretrained.pdparams",
"RepVGG_B2g4": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B2g4_pretrained.pdparams",
"RepVGG_B3g2": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RepVGG_B3g2_pretrained.pdparams",
"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):
......@@ -230,110 +237,144 @@ class RepVGG(nn.Layer):
return out
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}
def RepVGG_A0(**kwargs):
return RepVGG(
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 RepVGG_A0(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
num_blocks=[2, 4, 14, 1],
width_multiplier=[0.75, 0.75, 0.75, 2.5],
override_groups_map=None,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_A0"], use_ssld=use_ssld)
return model
def RepVGG_A1(**kwargs):
return RepVGG(
def RepVGG_A1(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
num_blocks=[2, 4, 14, 1],
width_multiplier=[1, 1, 1, 2.5],
override_groups_map=None,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_A1"], use_ssld=use_ssld)
return model
def RepVGG_A2(**kwargs):
return RepVGG(
def RepVGG_A2(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
num_blocks=[2, 4, 14, 1],
width_multiplier=[1.5, 1.5, 1.5, 2.75],
override_groups_map=None,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_A2"], use_ssld=use_ssld)
return model
def RepVGG_B0(**kwargs):
return RepVGG(
def RepVGG_B0(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
num_blocks=[4, 6, 16, 1],
width_multiplier=[1, 1, 1, 2.5],
override_groups_map=None,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B0"], use_ssld=use_ssld)
return model
def RepVGG_B1(**kwargs):
return RepVGG(
def RepVGG_B1(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
num_blocks=[4, 6, 16, 1],
width_multiplier=[2, 2, 2, 4],
override_groups_map=None,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B1"], use_ssld=use_ssld)
return model
def RepVGG_B1g2(**kwargs):
return RepVGG(
def RepVGG_B1g2(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
num_blocks=[4, 6, 16, 1],
width_multiplier=[2, 2, 2, 4],
override_groups_map=g2_map,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B1g2"], use_ssld=use_ssld)
return model
def RepVGG_B1g4(**kwargs):
return RepVGG(
def RepVGG_B1g4(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
num_blocks=[4, 6, 16, 1],
width_multiplier=[2, 2, 2, 4],
override_groups_map=g4_map,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B1g4"], use_ssld=use_ssld)
return model
def RepVGG_B2(**kwargs):
return RepVGG(
def RepVGG_B2(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
num_blocks=[4, 6, 16, 1],
width_multiplier=[2.5, 2.5, 2.5, 5],
override_groups_map=None,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B2"], use_ssld=use_ssld)
return model
def RepVGG_B2g2(**kwargs):
return RepVGG(
def RepVGG_B2g2(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
num_blocks=[4, 6, 16, 1],
width_multiplier=[2.5, 2.5, 2.5, 5],
override_groups_map=g2_map,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B2g2"], use_ssld=use_ssld)
return model
def RepVGG_B2g4(**kwargs):
return RepVGG(
def RepVGG_B2g4(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
num_blocks=[4, 6, 16, 1],
width_multiplier=[2.5, 2.5, 2.5, 5],
override_groups_map=g4_map,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B2g4"], use_ssld=use_ssld)
return model
def RepVGG_B3(**kwargs):
return RepVGG(
def RepVGG_B3(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
num_blocks=[4, 6, 16, 1],
width_multiplier=[3, 3, 3, 5],
override_groups_map=None,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B3"], use_ssld=use_ssld)
return model
def RepVGG_B3g2(**kwargs):
return RepVGG(
def RepVGG_B3g2(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
num_blocks=[4, 6, 16, 1],
width_multiplier=[3, 3, 3, 5],
override_groups_map=g2_map,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["RepVGG_B3g2"], use_ssld=use_ssld)
return model
def RepVGG_B3g4(**kwargs):
return RepVGG(
def RepVGG_B3g4(pretrained=False, use_ssld=False, **kwargs):
model = RepVGG(
num_blocks=[4, 6, 16, 1],
width_multiplier=[3, 3, 3, 5],
override_groups_map=g4_map,
**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
import math
__all__ = [
"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.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {"Res2Net50_26w_4s": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_26w_4s_pretrained.pdparams",
"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):
......@@ -232,41 +234,26 @@ class Res2Net(nn.Layer):
return y
def Res2Net50_48w_2s(**args):
model = Res2Net(layers=50, scales=2, width=48, **args)
return model
def Res2Net50_26w_4s(**args):
model = Res2Net(layers=50, scales=4, width=26, **args)
return model
def Res2Net50_14w_8s(**args):
model = Res2Net(layers=50, scales=8, width=14, **args)
return model
def Res2Net50_26w_6s(**args):
model = Res2Net(layers=50, scales=6, width=26, **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 Res2Net50_26w_4s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net(layers=50, scales=4, width=26, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Res2Net50_26w_4s"], use_ssld=use_ssld)
return model
def Res2Net50_26w_8s(**args):
model = Res2Net(layers=50, scales=8, width=26, **args)
return model
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
def Res2Net50_14w_8s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net(layers=50, scales=8, width=14, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Res2Net50_14w_8s"], use_ssld=use_ssld)
return model
\ No newline at end of file
ppcls/arch/backbone/model_zoo/res2net_vd.py
浏览文件 @ d8def846
......@@ -27,11 +27,14 @@ from paddle.nn.initializer import Uniform
import math
__all__ = [
"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.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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": "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):
......@@ -255,41 +258,32 @@ class Res2Net_vd(nn.Layer):
return y
def Res2Net50_vd_48w_2s(**args):
model = Res2Net_vd(layers=50, scales=2, width=48, **args)
return model
def Res2Net50_vd_26w_4s(**args):
model = Res2Net_vd(layers=50, scales=4, width=26, **args)
return model
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 _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 Res2Net50_vd_26w_8s(**args):
model = Res2Net_vd(layers=50, scales=8, width=26, **args)
def Res2Net50_vd_26w_4s(pretrained=False, use_ssld=False, **kwargs):
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
def Res2Net101_vd_26w_4s(**args):
model = Res2Net_vd(layers=101, scales=4, width=26, **args)
def Res2Net101_vd_26w_4s(pretrained=False, use_ssld=False, **kwargs):
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
def Res2Net152_vd_26w_4s(**args):
model = Res2Net_vd(layers=152, scales=4, width=26, **args)
return model
def Res2Net200_vd_26w_4s(**args):
model = Res2Net_vd(layers=200, scales=4, width=26, **args)
return model
def Res2Net200_vd_26w_4s(pretrained=False, use_ssld=False, **kwargs):
model = Res2Net_vd(layers=200, scales=4, width=26, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Res2Net200_vd_26w_4s"], use_ssld=use_ssld)
return model
\ No newline at end of file
ppcls/arch/backbone/model_zoo/resnest.py
浏览文件 @ d8def846
......@@ -27,7 +27,14 @@ from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
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):
......@@ -656,8 +663,21 @@ class ResNeSt(nn.Layer):
x = self.out(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(
layers=[3, 4, 6, 3],
radix=1,
......@@ -669,11 +689,12 @@ def ResNeSt50_fast_1s1x64d(**args):
avd=True,
avd_first=True,
final_drop=0.0,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeSt50_fast_1s1x64d"], use_ssld=use_ssld)
return model
def ResNeSt50(**args):
def ResNeSt50(pretrained=False, use_ssld=False, **kwargs):
model = ResNeSt(
layers=[3, 4, 6, 3],
radix=2,
......@@ -685,11 +706,12 @@ def ResNeSt50(**args):
avd=True,
avd_first=False,
final_drop=0.0,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeSt50"], use_ssld=use_ssld)
return model
def ResNeSt101(**args):
def ResNeSt101(pretrained=False, use_ssld=False, **kwargs):
model = ResNeSt(
layers=[3, 4, 23, 3],
radix=2,
......@@ -701,5 +723,6 @@ def ResNeSt101(**args):
avd=True,
avd_first=False,
final_drop=0.0,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeSt101"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/resnet.py
浏览文件 @ d8def846
......@@ -27,7 +27,16 @@ from paddle.nn.initializer import Uniform
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):
......@@ -290,27 +299,45 @@ class ResNet(nn.Layer):
y = self.out(y)
return y
def ResNet18(**args):
model = ResNet(layers=18, **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 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
def ResNet34(**args):
model = ResNet(layers=34, **args)
def ResNet34(pretrained=False, use_ssld=False, **kwargs):
model = ResNet(layers=34, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet34"], use_ssld=use_ssld)
return model
def ResNet50(**args):
model = ResNet(layers=50, **args)
def ResNet50(pretrained=False, use_ssld=False, **kwargs):
model = ResNet(layers=50, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet50"], use_ssld=use_ssld)
return model
def ResNet101(**args):
model = ResNet(layers=101, **args)
def ResNet101(pretrained=False, use_ssld=False, **kwargs):
model = ResNet(layers=101, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet101"], use_ssld=use_ssld)
return model
def ResNet152(**args):
model = ResNet(layers=152, **args)
def ResNet152(pretrained=False, use_ssld=False, **kwargs):
model = ResNet(layers=152, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet152"], use_ssld=use_ssld)
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
import math
__all__ = [
"ResNet18_vc", "ResNet34_vc", "ResNet50_vc", "ResNet101_vc", "ResNet152_vc"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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):
......@@ -283,27 +287,22 @@ class ResNet_vc(nn.Layer):
y = self.out(y)
return y
def ResNet18_vc(**args):
model = ResNet_vc(layers=18, **args)
return model
def ResNet34_vc(**args):
model = ResNet_vc(layers=34, **args)
return model
def ResNet50_vc(**args):
model = ResNet_vc(layers=50, **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 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
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
import math
__all__ = [
"ResNet18_vd", "ResNet34_vd", "ResNet50_vd", "ResNet101_vd", "ResNet152_vd"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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):
......@@ -324,31 +333,50 @@ class ResNet_vd(nn.Layer):
return y
def ResNet18_vd(**args):
model = ResNet_vd(layers=18, **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 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
def ResNet34_vd(**args):
model = ResNet_vd(layers=34, **args)
def ResNet34_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=34, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet34_vd"], use_ssld=use_ssld)
return model
def ResNet50_vd(**args):
model = ResNet_vd(layers=50, **args)
def ResNet50_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=50, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet50_vd"], use_ssld=use_ssld)
return model
def ResNet101_vd(**args):
model = ResNet_vd(layers=101, **args)
def ResNet101_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=101, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet101_vd"], use_ssld=use_ssld)
return model
def ResNet152_vd(**args):
model = ResNet_vd(layers=152, **args)
def ResNet152_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=152, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet152_vd"], use_ssld=use_ssld)
return model
def ResNet200_vd(**args):
model = ResNet_vd(layers=200, **args)
def ResNet200_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNet_vd(layers=200, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNet200_vd"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/resnext.py
浏览文件 @ d8def846
......@@ -27,10 +27,18 @@ from paddle.nn.initializer import Uniform
import math
__all__ = [
"ResNeXt50_32x4d", "ResNeXt50_64x4d", "ResNeXt101_32x4d",
"ResNeXt101_64x4d", "ResNeXt152_32x4d", "ResNeXt152_64x4d"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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):
......@@ -222,32 +230,51 @@ class ResNeXt(nn.Layer):
y = self.out(y)
return y
def ResNeXt50_32x4d(**args):
model = ResNeXt(layers=50, cardinality=32, **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 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
def ResNeXt50_64x4d(**args):
model = ResNeXt(layers=50, cardinality=64, **args)
def ResNeXt50_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=50, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt50_64x4d"], use_ssld=use_ssld)
return model
def ResNeXt101_32x4d(**args):
model = ResNeXt(layers=101, cardinality=32, **args)
def ResNeXt101_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_32x4d"], use_ssld=use_ssld)
return model
def ResNeXt101_64x4d(**args):
model = ResNeXt(layers=101, cardinality=64, **args)
def ResNeXt101_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_64x4d"], use_ssld=use_ssld)
return model
def ResNeXt152_32x4d(**args):
model = ResNeXt(layers=152, cardinality=32, **args)
def ResNeXt152_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt152_32x4d"], use_ssld=use_ssld)
return model
def ResNeXt152_64x4d(**args):
model = ResNeXt(layers=152, cardinality=64, **args)
def ResNeXt152_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt152_64x4d"], use_ssld=use_ssld)
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
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
__all__ = [
"ResNeXt101_32x8d_wsl", "ResNeXt101_32x16d_wsl", "ResNeXt101_32x32d_wsl",
"ResNeXt101_32x48d_wsl"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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):
......@@ -426,22 +434,39 @@ class ResNeXt101WSL(nn.Layer):
x = self._out(x)
return x
def ResNeXt101_32x8d_wsl(**args):
model = ResNeXt101WSL(cardinality=32, width=8, **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 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
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
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
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
ppcls/arch/backbone/model_zoo/resnext_vd.py
浏览文件 @ d8def846
......@@ -27,11 +27,18 @@ from paddle.nn.initializer import Uniform
import math
__all__ = [
"ResNeXt50_vd_32x4d", "ResNeXt50_vd_64x4d", "ResNeXt101_vd_32x4d",
"ResNeXt101_vd_64x4d", "ResNeXt152_vd_32x4d", "ResNeXt152_vd_64x4d"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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):
def __init__(
......@@ -235,32 +242,50 @@ class ResNeXt(nn.Layer):
y = self.out(y)
return y
def ResNeXt50_vd_32x4d(**args):
model = ResNeXt(layers=50, cardinality=32, **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 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
def ResNeXt50_vd_64x4d(**args):
model = ResNeXt(layers=50, cardinality=64, **args)
def ResNeXt50_vd_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=50, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt50_vd_64x4d"], use_ssld=use_ssld)
return model
def ResNeXt101_vd_32x4d(**args):
model = ResNeXt(layers=101, cardinality=32, **args)
def ResNeXt101_vd_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_vd_32x4d"], use_ssld=use_ssld)
return model
def ResNeXt101_vd_64x4d(**args):
model = ResNeXt(layers=101, cardinality=64, **args)
def ResNeXt101_vd_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt101_vd_64x4d"], use_ssld=use_ssld)
return model
def ResNeXt152_vd_32x4d(**args):
model = ResNeXt(layers=152, cardinality=32, **args)
def ResNeXt152_vd_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt152_vd_32x4d"], use_ssld=use_ssld)
return model
def ResNeXt152_vd_64x4d(**args):
model = ResNeXt(layers=152, cardinality=64, **args)
def ResNeXt152_vd_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ResNeXt152_vd_64x4d"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/rexnet.py
浏览文件 @ d8def846
......@@ -22,9 +22,17 @@ from paddle import ParamAttr
import paddle.nn as nn
from math import ceil
__all__ = [
"ReXNet_1_0", "ReXNet_1_3", "ReXNet_1_5", "ReXNet_2_0", "ReXNet_3_0"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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,
......@@ -220,21 +228,44 @@ class ReXNetV1(nn.Layer):
return x
def ReXNet_1_0(**args):
return ReXNetV1(width_mult=1.0, **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 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):
return ReXNetV1(width_mult=1.3, **args)
def ReXNet_1_3(pretrained=False, use_ssld=False, **kwargs):
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):
return ReXNetV1(width_mult=1.5, **args)
def ReXNet_1_5(pretrained=False, use_ssld=False, **kwargs):
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):
return ReXNetV1(width_mult=2.0, **args)
def ReXNet_2_0(pretrained=False, use_ssld=False, **kwargs):
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):
return ReXNetV1(width_mult=3.0, **args)
def ReXNet_3_0(pretrained=False, use_ssld=False, **kwargs):
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
import math
__all__ = [
"SE_ResNet18_vd", "SE_ResNet34_vd", "SE_ResNet50_vd", "SE_ResNet101_vd",
"SE_ResNet152_vd", "SE_ResNet200_vd"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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):
......@@ -347,32 +353,33 @@ class SE_ResNet_vd(nn.Layer):
y = self.out(y)
return y
def SE_ResNet18_vd(**args):
model = SE_ResNet_vd(layers=18, **args)
return model
def SE_ResNet34_vd(**args):
model = SE_ResNet_vd(layers=34, **args)
return model
def SE_ResNet50_vd(**args):
model = SE_ResNet_vd(layers=50, **args)
return model
def SE_ResNet101_vd(**args):
model = SE_ResNet_vd(layers=101, **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 SE_ResNet18_vd(pretrained=False, use_ssld=False, **kwargs):
model = SE_ResNet_vd(layers=18, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNet18_vd"], use_ssld=use_ssld)
return model
def SE_ResNet152_vd(**args):
model = SE_ResNet_vd(layers=152, **args)
def SE_ResNet34_vd(pretrained=False, use_ssld=False, **kwargs):
model = SE_ResNet_vd(layers=34, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNet34_vd"], use_ssld=use_ssld)
return model
def SE_ResNet200_vd(**args):
model = SE_ResNet_vd(layers=200, **args)
def SE_ResNet50_vd(pretrained=False, use_ssld=False, **kwargs):
model = SE_ResNet_vd(layers=50, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNet50_vd"], use_ssld=use_ssld)
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
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):
......@@ -301,17 +310,33 @@ class ResNeXt(nn.Layer):
y = self.out(y)
return y
def SE_ResNeXt50_32x4d(**args):
model = ResNeXt(layers=50, cardinality=32, **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 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
def SE_ResNeXt101_32x4d(**args):
model = ResNeXt(layers=101, cardinality=32, **args)
def SE_ResNeXt101_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNeXt101_32x4d"], use_ssld=use_ssld)
return model
def SE_ResNeXt152_64x4d(**args):
model = ResNeXt(layers=152, cardinality=64, **args)
def SE_ResNeXt152_64x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNeXt152_64x4d"], use_ssld=use_ssld)
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
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):
......@@ -269,17 +278,33 @@ class ResNeXt(nn.Layer):
y = self.out(y)
return y
def SE_ResNeXt50_vd_32x4d(**args):
model = ResNeXt(layers=50, cardinality=32, **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 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
def SE_ResNeXt101_vd_32x4d(**args):
model = ResNeXt(layers=101, cardinality=32, **args)
def SE_ResNeXt101_vd_32x4d(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=101, cardinality=32, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SE_ResNeXt101_vd_32x4d"], use_ssld=use_ssld)
return model
def SENet154_vd(**args):
model = ResNeXt(layers=152, cardinality=64, **args)
def SENet154_vd(pretrained=False, use_ssld=False, **kwargs):
model = ResNeXt(layers=152, cardinality=64, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SENet154_vd"], use_ssld=use_ssld)
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
from paddle.nn.initializer import KaimingNormal
from paddle.nn.functional import swish
__all__ = [
"ShuffleNetV2_x0_25", "ShuffleNetV2_x0_33", "ShuffleNetV2_x0_5",
"ShuffleNetV2_x1_0", "ShuffleNetV2_x1_5", "ShuffleNetV2_x2_0",
"ShuffleNetV2_swish"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"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):
......@@ -285,36 +293,56 @@ class ShuffleNet(Layer):
return y
def ShuffleNetV2_x0_25(**args):
model = ShuffleNet(scale=0.25, **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 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
def ShuffleNetV2_x0_33(**args):
model = ShuffleNet(scale=0.33, **args)
def ShuffleNetV2_x0_33(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=0.33, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x0_33"], use_ssld=use_ssld)
return model
def ShuffleNetV2_x0_5(**args):
model = ShuffleNet(scale=0.5, **args)
def ShuffleNetV2_x0_5(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=0.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x0_5"], use_ssld=use_ssld)
return model
def ShuffleNetV2_x1_0(**args):
model = ShuffleNet(scale=1.0, **args)
def ShuffleNetV2_x1_0(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=1.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x1_0"], use_ssld=use_ssld)
return model
def ShuffleNetV2_x1_5(**args):
model = ShuffleNet(scale=1.5, **args)
def ShuffleNetV2_x1_5(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=1.5, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x1_5"], use_ssld=use_ssld)
return model
def ShuffleNetV2_x2_0(**args):
model = ShuffleNet(scale=2.0, **args)
def ShuffleNetV2_x2_0(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=2.0, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_x2_0"], use_ssld=use_ssld)
return model
def ShuffleNetV2_swish(**args):
model = ShuffleNet(scale=1.0, act="swish", **args)
def ShuffleNetV2_swish(pretrained=False, use_ssld=False, **kwargs):
model = ShuffleNet(scale=1.0, act="swish", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ShuffleNetV2_swish"], use_ssld=use_ssld)
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
from paddle import ParamAttr
import paddle.nn as nn
......@@ -5,7 +19,14 @@ import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
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):
......@@ -143,12 +164,26 @@ class SqueezeNet(nn.Layer):
x = paddle.squeeze(x, axis=[2, 3])
return x
def SqueezeNet1_0(**args):
model = SqueezeNet(version="1.0", **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 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
def SqueezeNet1_1(**args):
model = SqueezeNet(version="1.1", **args)
def SqueezeNet1_1(pretrained=False, use_ssld=False, **kwargs):
model = SqueezeNet(version="1.1", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SqueezeNet1_1"], use_ssld=use_ssld)
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
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):
def __init__(self,
......@@ -716,40 +729,56 @@ class SwinTransformer(nn.Layer):
flops += self.num_features * self.num_classes
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(
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
drop_path_rate=0.2,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_tiny_patch4_window7_224"], use_ssld=use_ssld)
return model
def SwinTransformer_small_patch4_window7_224(**args):
def SwinTransformer_small_patch4_window7_224(pretrained=False, use_ssld=False, **kwargs):
model = SwinTransformer(
embed_dim=96,
depths=[2, 2, 18, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_small_patch4_window7_224"], use_ssld=use_ssld)
return model
def SwinTransformer_base_patch4_window7_224(**args):
def SwinTransformer_base_patch4_window7_224(pretrained=False, use_ssld=False, **kwargs):
model = SwinTransformer(
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=7,
drop_path_rate=0.5,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_base_patch4_window7_224"], use_ssld=use_ssld)
return model
def SwinTransformer_base_patch4_window12_384(**args):
def SwinTransformer_base_patch4_window12_384(pretrained=False, use_ssld=False, **kwargs):
model = SwinTransformer(
img_size=384,
embed_dim=128,
......@@ -757,26 +786,29 @@ def SwinTransformer_base_patch4_window12_384(**args):
num_heads=[4, 8, 16, 32],
window_size=12,
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
def SwinTransformer_large_patch4_window7_224(**args):
def SwinTransformer_large_patch4_window7_224(pretrained=False, use_ssld=False, **kwargs):
model = SwinTransformer(
embed_dim=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=7,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_large_patch4_window7_224"], use_ssld=use_ssld)
return model
def SwinTransformer_large_patch4_window12_384(**args):
def SwinTransformer_large_patch4_window12_384(pretrained=False, use_ssld=False, **kwargs):
model = SwinTransformer(
img_size=384,
embed_dim=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=12,
**args)
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["SwinTransformer_large_patch4_window12_384"], use_ssld=use_ssld)
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
MODEL_URLS = {
"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
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
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):
......@@ -131,22 +140,40 @@ class VGGNet(nn.Layer):
x = self._out(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
def VGG13(**args):
model = VGGNet(layers=13, **args)
def VGG13(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VGGNet(layers=13, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG13"], use_ssld=use_ssld)
return model
def VGG16(**args):
model = VGGNet(layers=16, **args)
def VGG16(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VGGNet(layers=16, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG16"], use_ssld=use_ssld)
return model
def VGG19(**args):
model = VGGNet(layers=19, **args)
def VGG19(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VGGNet(layers=19, **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["VGG19"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/vision_transformer.py
浏览文件 @ d8def846
......@@ -19,12 +19,22 @@ import paddle
import paddle.nn as nn
from paddle.nn.initializer import TruncatedNormal, Constant, Normal
__all__ = [
"VisionTransformer", "ViT_small_patch16_224", "ViT_base_patch16_224",
"ViT_base_patch16_384", "ViT_base_patch32_384", "ViT_large_patch16_224",
"ViT_large_patch16_384", "ViT_large_patch32_384", "ViT_huge_patch16_224",
"ViT_huge_patch32_384"
]
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ViT_small_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams",
"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)
normal_ = Normal
......@@ -300,7 +310,21 @@ class VisionTransformer(nn.Layer):
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(
patch_size=16,
embed_dim=768,
......@@ -309,10 +333,12 @@ def ViT_small_patch16_224(**kwargs):
mlp_ratio=3,
qk_scale=768**-0.5,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_small_patch16_224"], use_ssld=use_ssld)
return model
def ViT_base_patch16_224(**kwargs):
def ViT_base_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer(
patch_size=16,
embed_dim=768,
......@@ -322,10 +348,11 @@ def ViT_base_patch16_224(**kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_base_patch16_224"], use_ssld=use_ssld)
return model
def ViT_base_patch16_384(**kwargs):
def ViT_base_patch16_384(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer(
img_size=384,
patch_size=16,
......@@ -336,10 +363,11 @@ def ViT_base_patch16_384(**kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_base_patch16_384"], use_ssld=use_ssld)
return model
def ViT_base_patch32_384(**kwargs):
def ViT_base_patch32_384(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer(
img_size=384,
patch_size=32,
......@@ -350,10 +378,11 @@ def ViT_base_patch32_384(**kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_base_patch32_384"], use_ssld=use_ssld)
return model
def ViT_large_patch16_224(**kwargs):
def ViT_large_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer(
patch_size=16,
embed_dim=1024,
......@@ -363,10 +392,11 @@ def ViT_large_patch16_224(**kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_large_patch16_224"], use_ssld=use_ssld)
return model
def ViT_large_patch16_384(**kwargs):
def ViT_large_patch16_384(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer(
img_size=384,
patch_size=16,
......@@ -377,10 +407,11 @@ def ViT_large_patch16_384(**kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_large_patch16_384"], use_ssld=use_ssld)
return model
def ViT_large_patch32_384(**kwargs):
def ViT_large_patch32_384(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer(
img_size=384,
patch_size=32,
......@@ -391,10 +422,11 @@ def ViT_large_patch32_384(**kwargs):
qkv_bias=True,
epsilon=1e-6,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_large_patch32_384"], use_ssld=use_ssld)
return model
def ViT_huge_patch16_224(**kwargs):
def ViT_huge_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer(
patch_size=16,
embed_dim=1280,
......@@ -402,10 +434,11 @@ def ViT_huge_patch16_224(**kwargs):
num_heads=16,
mlp_ratio=4,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_huge_patch16_224"], use_ssld=use_ssld)
return model
def ViT_huge_patch32_384(**kwargs):
def ViT_huge_patch32_384(pretrained, model, model_url, use_ssld=False, **kwargs):
model = VisionTransformer(
img_size=384,
patch_size=32,
......@@ -414,4 +447,5 @@ def ViT_huge_patch32_384(**kwargs):
num_heads=16,
mlp_ratio=4,
**kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["ViT_huge_patch32_384"], use_ssld=use_ssld)
return model
ppcls/arch/backbone/model_zoo/xception.py
浏览文件 @ d8def846
......@@ -8,7 +8,16 @@ from paddle.nn.initializer import Uniform
import math
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):
......@@ -329,17 +338,32 @@ class Xception(nn.Layer):
x = self._exit_flow(x)
return x
def Xception41(**args):
model = Xception(entry_flow_block_num=3, middle_flow_block_num=8, **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 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
def Xception65(**args):
model = Xception(entry_flow_block_num=3, middle_flow_block_num=16, **args)
def Xception65(pretrained=False, use_ssld=False, **kwargs):
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
def Xception71(**args):
model = Xception(entry_flow_block_num=5, middle_flow_block_num=16, **args)
def Xception71(pretrained=False, use_ssld=False, **kwargs):
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
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
from paddle import ParamAttr
import paddle.nn as nn
......@@ -5,7 +19,12 @@ import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
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):
......@@ -369,18 +388,28 @@ class XceptionDeeplab(nn.Layer):
x = paddle.squeeze(x, axis=[2, 3])
x = self._fc(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):
model = XceptionDeeplab('xception_41', **args)
return model
def Xception65_deeplab(**args):
model = XceptionDeeplab("xception_65", **args)
def Xception41_deeplab(pretrained=False, use_ssld=False, **kwargs):
model = XceptionDeeplab('xception_41', **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Xception41_deeplab"], use_ssld=use_ssld)
return model
def Xception71_deeplab(**args):
model = XceptionDeeplab("xception_71", **args)
def Xception65_deeplab(pretrained=False, use_ssld=False, **kwargs):
model = XceptionDeeplab("xception_65", **kwargs)
_load_pretrained(pretrained, model, MODEL_URLS["Xception65_deeplab"], use_ssld=use_ssld)
return model
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