fix: change to relative import

__init__.py

@@ -14,4 +14,4 @@
 
 __all__ = ['PaddleClas']
 from .paddleclas import PaddleClas
-from ppcls.arch.backbone import *
+from .ppcls.arch.backbone import *

ppcls/arch/__init__.py

@@ -23,11 +23,11 @@ from . import backbone, gears
 from .backbone import *
 from .gears import build_gear
 from .utils import *
-from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
-from ppcls.utils import logger
-from ppcls.utils.save_load import load_dygraph_pretrain
-from ppcls.arch.slim import prune_model, quantize_model
-from ppcls.arch.distill.afd_attention import LinearTransformStudent, LinearTransformTeacher
+from .backbone.base.theseus_layer import TheseusLayer
+from ..utils import logger
+from ..utils.save_load import load_dygraph_pretrain
+from .slim import prune_model, quantize_model
+from .distill.afd_attention import LinearTransformStudent, LinearTransformTeacher
 
 __all__ = ["build_model", "RecModel", "DistillationModel", "AttentionModel"]
 

ppcls/arch/backbone/__init__.py

@@ -15,62 +15,62 @@
 import sys
 import inspect
 
-from ppcls.arch.backbone.legendary_models.mobilenet_v1 import MobileNetV1_x0_25, MobileNetV1_x0_5, MobileNetV1_x0_75, MobileNetV1
-from ppcls.arch.backbone.legendary_models.mobilenet_v3 import 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.arch.backbone.legendary_models.resnet import ResNet18, ResNet18_vd, ResNet34, ResNet34_vd, ResNet50, ResNet50_vd, ResNet101, ResNet101_vd, ResNet152, ResNet152_vd, ResNet200_vd
-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.legendary_models.pp_lcnet import PPLCNet_x0_25, PPLCNet_x0_35, PPLCNet_x0_5, PPLCNet_x0_75, PPLCNet_x1_0, PPLCNet_x1_5, PPLCNet_x2_0, PPLCNet_x2_5
-from ppcls.arch.backbone.legendary_models.pp_lcnet_v2 import PPLCNetV2_base
-from ppcls.arch.backbone.legendary_models.esnet import ESNet_x0_25, ESNet_x0_5, ESNet_x0_75, ESNet_x1_0
-from ppcls.arch.backbone.legendary_models.pp_hgnet import PPHGNet_tiny, PPHGNet_small, PPHGNet_base
+from .legendary_models.mobilenet_v1 import MobileNetV1_x0_25, MobileNetV1_x0_5, MobileNetV1_x0_75, MobileNetV1
+from .legendary_models.mobilenet_v3 import 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 .legendary_models.resnet import ResNet18, ResNet18_vd, ResNet34, ResNet34_vd, ResNet50, ResNet50_vd, ResNet101, ResNet101_vd, ResNet152, ResNet152_vd, ResNet200_vd
+from .legendary_models.vgg import VGG11, VGG13, VGG16, VGG19
+from .legendary_models.inception_v3 import InceptionV3
+from .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 .legendary_models.pp_lcnet import PPLCNet_x0_25, PPLCNet_x0_35, PPLCNet_x0_5, PPLCNet_x0_75, PPLCNet_x1_0, PPLCNet_x1_5, PPLCNet_x2_0, PPLCNet_x2_5
+from .legendary_models.pp_lcnet_v2 import PPLCNetV2_base
+from .legendary_models.esnet import ESNet_x0_25, ESNet_x0_5, ESNet_x0_75, ESNet_x1_0
+from .legendary_models.pp_hgnet import PPHGNet_tiny, PPHGNet_small, PPHGNet_base
 
-from ppcls.arch.backbone.model_zoo.resnet_vc import ResNet50_vc
-from ppcls.arch.backbone.model_zoo.resnext import ResNeXt50_32x4d, ResNeXt50_64x4d, ResNeXt101_32x4d, ResNeXt101_64x4d, ResNeXt152_32x4d, ResNeXt152_64x4d
-from ppcls.arch.backbone.model_zoo.resnext_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
-from ppcls.arch.backbone.model_zoo.densenet import DenseNet121, DenseNet161, DenseNet169, DenseNet201, DenseNet264
-from ppcls.arch.backbone.model_zoo.efficientnet import EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7, EfficientNetB0_small
-from ppcls.arch.backbone.model_zoo.resnest import ResNeSt50_fast_1s1x64d, ResNeSt50, ResNeSt101
-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
-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
-from ppcls.arch.backbone.model_zoo.regnet import RegNetX_200MF, RegNetX_4GF, RegNetX_32GF, RegNetY_200MF, RegNetY_4GF, RegNetY_32GF
-from ppcls.arch.backbone.model_zoo.vision_transformer import 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
-from ppcls.arch.backbone.model_zoo.distilled_vision_transformer import 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.arch.backbone.legendary_models.swin_transformer import SwinTransformer_tiny_patch4_window7_224, SwinTransformer_small_patch4_window7_224, SwinTransformer_base_patch4_window7_224, SwinTransformer_base_patch4_window12_384, SwinTransformer_large_patch4_window7_224, SwinTransformer_large_patch4_window12_384
-from ppcls.arch.backbone.model_zoo.cswin_transformer import CSWinTransformer_tiny_224, CSWinTransformer_small_224, CSWinTransformer_base_224, CSWinTransformer_large_224, CSWinTransformer_base_384, CSWinTransformer_large_384
-from ppcls.arch.backbone.model_zoo.mixnet import MixNet_S, MixNet_M, MixNet_L
-from ppcls.arch.backbone.model_zoo.rexnet import ReXNet_1_0, ReXNet_1_3, ReXNet_1_5, ReXNet_2_0, ReXNet_3_0
-from ppcls.arch.backbone.model_zoo.gvt import pcpvt_small, pcpvt_base, pcpvt_large, alt_gvt_small, alt_gvt_base, alt_gvt_large
-from ppcls.arch.backbone.model_zoo.levit import LeViT_128S, LeViT_128, LeViT_192, LeViT_256, LeViT_384
-from ppcls.arch.backbone.model_zoo.dla import DLA34, DLA46_c, DLA46x_c, DLA60, DLA60x, DLA60x_c, DLA102, DLA102x, DLA102x2, DLA169
-from ppcls.arch.backbone.model_zoo.rednet import RedNet26, RedNet38, RedNet50, RedNet101, RedNet152
-from ppcls.arch.backbone.model_zoo.tnt import TNT_small
-from ppcls.arch.backbone.model_zoo.hardnet import HarDNet68, HarDNet85, HarDNet39_ds, HarDNet68_ds
-from ppcls.arch.backbone.model_zoo.cspnet import CSPDarkNet53
-from ppcls.arch.backbone.model_zoo.pvt_v2 import PVT_V2_B0, PVT_V2_B1, PVT_V2_B2_Linear, PVT_V2_B2, PVT_V2_B3, PVT_V2_B4, PVT_V2_B5
-from ppcls.arch.backbone.model_zoo.mobilevit import MobileViT_XXS, MobileViT_XS, MobileViT_S
-from ppcls.arch.backbone.model_zoo.repvgg import RepVGG_A0, RepVGG_A1, RepVGG_A2, RepVGG_B0, RepVGG_B1, RepVGG_B2, RepVGG_B1g2, RepVGG_B1g4, RepVGG_B2g4, RepVGG_B3g4
-from ppcls.arch.backbone.model_zoo.van import VAN_tiny
-from ppcls.arch.backbone.variant_models.resnet_variant import ResNet50_last_stage_stride1
-from ppcls.arch.backbone.variant_models.vgg_variant import VGG19Sigmoid
-from ppcls.arch.backbone.variant_models.pp_lcnet_variant import PPLCNet_x2_5_Tanh
-from ppcls.arch.backbone.model_zoo.adaface_ir_net import AdaFace_IR_18, AdaFace_IR_34, AdaFace_IR_50, AdaFace_IR_101, AdaFace_IR_152, AdaFace_IR_SE_50, AdaFace_IR_SE_101, AdaFace_IR_SE_152, AdaFace_IR_SE_200
+from .model_zoo.resnet_vc import ResNet50_vc
+from .model_zoo.resnext import ResNeXt50_32x4d, ResNeXt50_64x4d, ResNeXt101_32x4d, ResNeXt101_64x4d, ResNeXt152_32x4d, ResNeXt152_64x4d
+from .model_zoo.resnext_vd import ResNeXt50_vd_32x4d, ResNeXt50_vd_64x4d, ResNeXt101_vd_32x4d, ResNeXt101_vd_64x4d, ResNeXt152_vd_32x4d, ResNeXt152_vd_64x4d
+from .model_zoo.res2net import Res2Net50_26w_4s, Res2Net50_14w_8s
+from .model_zoo.res2net_vd import Res2Net50_vd_26w_4s, Res2Net101_vd_26w_4s, Res2Net200_vd_26w_4s
+from .model_zoo.se_resnet_vd import SE_ResNet18_vd, SE_ResNet34_vd, SE_ResNet50_vd
+from .model_zoo.se_resnext_vd import SE_ResNeXt50_vd_32x4d, SE_ResNeXt50_vd_32x4d, SENet154_vd
+from .model_zoo.se_resnext import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_64x4d
+from .model_zoo.dpn import DPN68, DPN92, DPN98, DPN107, DPN131
+from .model_zoo.densenet import DenseNet121, DenseNet161, DenseNet169, DenseNet201, DenseNet264
+from .model_zoo.efficientnet import EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7, EfficientNetB0_small
+from .model_zoo.resnest import ResNeSt50_fast_1s1x64d, ResNeSt50, ResNeSt101
+from .model_zoo.googlenet import GoogLeNet
+from .model_zoo.mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x0_75, MobileNetV2, MobileNetV2_x1_5, MobileNetV2_x2_0
+from .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 .model_zoo.ghostnet import GhostNet_x0_5, GhostNet_x1_0, GhostNet_x1_3
+from .model_zoo.alexnet import AlexNet
+from .model_zoo.inception_v4 import InceptionV4
+from .model_zoo.xception import Xception41, Xception65, Xception71
+from .model_zoo.xception_deeplab import Xception41_deeplab, Xception65_deeplab
+from .model_zoo.resnext101_wsl import ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl
+from .model_zoo.squeezenet import SqueezeNet1_0, SqueezeNet1_1
+from .model_zoo.darknet import DarkNet53
+from .model_zoo.regnet import RegNetX_200MF, RegNetX_4GF, RegNetX_32GF, RegNetY_200MF, RegNetY_4GF, RegNetY_32GF
+from .model_zoo.vision_transformer import 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
+from .model_zoo.distilled_vision_transformer import 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 .legendary_models.swin_transformer import SwinTransformer_tiny_patch4_window7_224, SwinTransformer_small_patch4_window7_224, SwinTransformer_base_patch4_window7_224, SwinTransformer_base_patch4_window12_384, SwinTransformer_large_patch4_window7_224, SwinTransformer_large_patch4_window12_384
+from .model_zoo.cswin_transformer import CSWinTransformer_tiny_224, CSWinTransformer_small_224, CSWinTransformer_base_224, CSWinTransformer_large_224, CSWinTransformer_base_384, CSWinTransformer_large_384
+from .model_zoo.mixnet import MixNet_S, MixNet_M, MixNet_L
+from .model_zoo.rexnet import ReXNet_1_0, ReXNet_1_3, ReXNet_1_5, ReXNet_2_0, ReXNet_3_0
+from .model_zoo.gvt import pcpvt_small, pcpvt_base, pcpvt_large, alt_gvt_small, alt_gvt_base, alt_gvt_large
+from .model_zoo.levit import LeViT_128S, LeViT_128, LeViT_192, LeViT_256, LeViT_384
+from .model_zoo.dla import DLA34, DLA46_c, DLA46x_c, DLA60, DLA60x, DLA60x_c, DLA102, DLA102x, DLA102x2, DLA169
+from .model_zoo.rednet import RedNet26, RedNet38, RedNet50, RedNet101, RedNet152
+from .model_zoo.tnt import TNT_small
+from .model_zoo.hardnet import HarDNet68, HarDNet85, HarDNet39_ds, HarDNet68_ds
+from .model_zoo.cspnet import CSPDarkNet53
+from .model_zoo.pvt_v2 import PVT_V2_B0, PVT_V2_B1, PVT_V2_B2_Linear, PVT_V2_B2, PVT_V2_B3, PVT_V2_B4, PVT_V2_B5
+from .model_zoo.mobilevit import MobileViT_XXS, MobileViT_XS, MobileViT_S
+from .model_zoo.repvgg import RepVGG_A0, RepVGG_A1, RepVGG_A2, RepVGG_B0, RepVGG_B1, RepVGG_B2, RepVGG_B1g2, RepVGG_B1g4, RepVGG_B2g4, RepVGG_B3g4
+from .model_zoo.van import VAN_tiny
+from .variant_models.resnet_variant import ResNet50_last_stage_stride1
+from .variant_models.vgg_variant import VGG19Sigmoid
+from .variant_models.pp_lcnet_variant import PPLCNet_x2_5_Tanh
+from .model_zoo.adaface_ir_net import AdaFace_IR_18, AdaFace_IR_34, AdaFace_IR_50, AdaFace_IR_101, AdaFace_IR_152, AdaFace_IR_SE_50, AdaFace_IR_SE_101, AdaFace_IR_SE_152, AdaFace_IR_SE_200
 
 
 # help whl get all the models' api (class type) and components' api (func type)

ppcls/arch/backbone/base/theseus_layer.py

@@ -15,7 +15,7 @@
 from typing import Tuple, List, Dict, Union, Callable, Any
 
 from paddle import nn
-from ppcls.utils import logger
+from ....utils import logger
 
 
 class Identity(nn.Layer):

ppcls/arch/backbone/legendary_models/esnet.py

@@ -22,8 +22,8 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D
 from paddle.nn.initializer import KaimingNormal
 from paddle.regularizer import L2Decay
 
-from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ..base.theseus_layer import TheseusLayer
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "ESNet_x0_25":

ppcls/arch/backbone/legendary_models/hrnet.py

@@ -25,8 +25,8 @@ from paddle import ParamAttr
 from paddle.nn.functional import upsample
 from paddle.nn.initializer import Uniform
 
-from ppcls.arch.backbone.base.theseus_layer import TheseusLayer, Identity
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ..base.theseus_layer import TheseusLayer, Identity
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "HRNet_W18_C":

ppcls/arch/backbone/legendary_models/inception_v3.py

@@ -23,8 +23,8 @@ from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
 from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 from paddle.nn.initializer import Uniform
 
-from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ..base.theseus_layer import TheseusLayer
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "InceptionV3":

ppcls/arch/backbone/legendary_models/mobilenet_v1.py

@@ -22,8 +22,8 @@ from paddle.nn import Conv2D, BatchNorm, Linear, ReLU, Flatten
 from paddle.nn import AdaptiveAvgPool2D
 from paddle.nn.initializer import KaimingNormal
 
-from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ..base.theseus_layer import TheseusLayer
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "MobileNetV1_x0_25":

ppcls/arch/backbone/legendary_models/mobilenet_v3.py

@@ -21,8 +21,9 @@ import paddle.nn as nn
 from paddle import ParamAttr
 from paddle.nn import AdaptiveAvgPool2D, BatchNorm, Conv2D, Dropout, Linear
 from paddle.regularizer import L2Decay
-from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+
+from ..base.theseus_layer import TheseusLayer
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "MobileNetV3_small_x0_35":

ppcls/arch/backbone/legendary_models/pp_hgnet.py

@@ -20,8 +20,8 @@ from paddle.nn import Conv2D, BatchNorm2D, ReLU, AdaptiveAvgPool2D, MaxPool2D
 from paddle.regularizer import L2Decay
 from paddle import ParamAttr
 
-from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ..base.theseus_layer import TheseusLayer
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "PPHGNet_tiny":
@@ -199,6 +199,7 @@ class PPHGNet(TheseusLayer):
     Returns:
         model: nn.Layer. Specific PPHGNet model depends on args.
     """
+
     def __init__(self,
                  stem_channels,
                  stage_config,

ppcls/arch/backbone/legendary_models/pp_lcnet.py

@@ -20,8 +20,9 @@ from paddle import ParamAttr
 from paddle.nn import AdaptiveAvgPool2D, BatchNorm2D, Conv2D, Dropout, Linear
 from paddle.regularizer import L2Decay
 from paddle.nn.initializer import KaimingNormal
-from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+
+from ..base.theseus_layer import TheseusLayer
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "PPLCNet_x0_25":
@@ -229,64 +230,59 @@ class PPLCNet(TheseusLayer):
             stride=stride_list[0],
             lr_mult=self.lr_mult_list[0])
 
-        self.blocks2 = nn.Sequential(*[
+        self.blocks2 = nn.Sequential(* [
             DepthwiseSeparable(
                 num_channels=make_divisible(in_c * scale),
                 num_filters=make_divisible(out_c * scale),
                 dw_size=k,
                 stride=s,
                 use_se=se,
-                lr_mult=self.lr_mult_list[1])
-            for i, (k, in_c, out_c, s, se
-                    ) in enumerate(self.net_config["blocks2"])
+                lr_mult=self.lr_mult_list[1]) for i, (k, in_c, out_c, s, se) in
+            enumerate(self.net_config["blocks2"])
         ])
 
-        self.blocks3 = nn.Sequential(*[
+        self.blocks3 = nn.Sequential(* [
             DepthwiseSeparable(
                 num_channels=make_divisible(in_c * scale),
                 num_filters=make_divisible(out_c * scale),
                 dw_size=k,
                 stride=s,
                 use_se=se,
-                lr_mult=self.lr_mult_list[2])
-            for i, (k, in_c, out_c, s, se
-                    ) in enumerate(self.net_config["blocks3"])
+                lr_mult=self.lr_mult_list[2]) for i, (k, in_c, out_c, s, se) in
+            enumerate(self.net_config["blocks3"])
         ])
 
-        self.blocks4 = nn.Sequential(*[
+        self.blocks4 = nn.Sequential(* [
             DepthwiseSeparable(
                 num_channels=make_divisible(in_c * scale),
                 num_filters=make_divisible(out_c * scale),
                 dw_size=k,
                 stride=s,
                 use_se=se,
-                lr_mult=self.lr_mult_list[3])
-            for i, (k, in_c, out_c, s, se
-                    ) in enumerate(self.net_config["blocks4"])
+                lr_mult=self.lr_mult_list[3]) for i, (k, in_c, out_c, s, se) in
+            enumerate(self.net_config["blocks4"])
         ])
 
-        self.blocks5 = nn.Sequential(*[
+        self.blocks5 = nn.Sequential(* [
             DepthwiseSeparable(
                 num_channels=make_divisible(in_c * scale),
                 num_filters=make_divisible(out_c * scale),
                 dw_size=k,
                 stride=s,
                 use_se=se,
-                lr_mult=self.lr_mult_list[4])
-            for i, (k, in_c, out_c, s, se
-                    ) in enumerate(self.net_config["blocks5"])
+                lr_mult=self.lr_mult_list[4]) for i, (k, in_c, out_c, s, se) in
+            enumerate(self.net_config["blocks5"])
         ])
 
-        self.blocks6 = nn.Sequential(*[
+        self.blocks6 = nn.Sequential(* [
             DepthwiseSeparable(
                 num_channels=make_divisible(in_c * scale),
                 num_filters=make_divisible(out_c * scale),
                 dw_size=k,
                 stride=s,
                 use_se=se,
-                lr_mult=self.lr_mult_list[5])
-            for i, (k, in_c, out_c, s, se
-                    ) in enumerate(self.net_config["blocks6"])
+                lr_mult=self.lr_mult_list[5]) for i, (k, in_c, out_c, s, se) in
+            enumerate(self.net_config["blocks6"])
         ])
 
         self.avg_pool = AdaptiveAvgPool2D(1)

ppcls/arch/backbone/legendary_models/pp_lcnet_v2.py

@@ -21,8 +21,9 @@ from paddle import ParamAttr
 from paddle.nn import AdaptiveAvgPool2D, BatchNorm2D, Conv2D, Dropout, Linear
 from paddle.regularizer import L2Decay
 from paddle.nn.initializer import KaimingNormal
-from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+
+from ..base.theseus_layer import TheseusLayer
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "PPLCNetV2_base":

ppcls/arch/backbone/legendary_models/resnet.py

@@ -26,9 +26,9 @@ from paddle.nn.initializer import Uniform
 from paddle.regularizer import L2Decay
 import math
 
-from ppcls.utils import logger
-from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils import logger
+from ..base.theseus_layer import TheseusLayer
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "ResNet18":
@@ -328,7 +328,7 @@ class ResNet(TheseusLayer):
                    [32, 32, 3, 1], [32, 64, 3, 1]]
         }
 
-        self.stem = nn.Sequential(*[
+        self.stem = nn.Sequential(* [
             ConvBNLayer(
                 num_channels=in_c,
                 num_filters=out_c,

ppcls/arch/backbone/legendary_models/swin_transformer.py

@@ -21,9 +21,9 @@ import paddle.nn as nn
 import paddle.nn.functional as F
 from paddle.nn.initializer import TruncatedNormal, Constant
 
-from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
-from ppcls.arch.backbone.model_zoo.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
+from ..model_zoo.vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity
+from ..base.theseus_layer import TheseusLayer
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "SwinTransformer_tiny_patch4_window7_224":

ppcls/arch/backbone/legendary_models/vgg.py

@@ -20,8 +20,8 @@ import paddle.nn as nn
 from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
 from paddle.nn import MaxPool2D
 
-from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ..base.theseus_layer import TheseusLayer
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "VGG11":

ppcls/arch/backbone/model_zoo/alexnet.py

@@ -23,7 +23,7 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 from paddle.nn.initializer import Uniform
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "AlexNet":

ppcls/arch/backbone/model_zoo/convnext.py

+# copyright (c) 2022 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.
+#
+# Code was heavily based on https://github.com/facebookresearch/ConvNeXt
+
+import paddle
+import paddle.nn as nn
+from paddle.nn.initializer import TruncatedNormal, Constant
+
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+
+MODEL_URLS = {
+    "ConvNeXt_tiny": "",  # TODO
+}
+
+__all__ = list(MODEL_URLS.keys())
+
+trunc_normal_ = TruncatedNormal(std=.02)
+zeros_ = Constant(value=0.)
+ones_ = Constant(value=1.)
+
+
+def drop_path(x, drop_prob=0., training=False):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ...
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = paddle.to_tensor(1 - drop_prob)
+    shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
+    random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
+    random_tensor = paddle.floor(random_tensor)  # binarize
+    output = x.divide(keep_prob) * random_tensor
+    return output
+
+
+class DropPath(nn.Layer):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+
+
+class ChannelsFirstLayerNorm(nn.Layer):
+    r""" LayerNorm that supports two data formats: channels_last (default) or channels_first. 
+    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with 
+    shape (batch_size, height, width, channels) while channels_first corresponds to inputs 
+    with shape (batch_size, channels, height, width).
+    """
+
+    def __init__(self, normalized_shape, epsilon=1e-5):
+        super().__init__()
+        self.weight = self.create_parameter(
+            shape=[normalized_shape], default_initializer=ones_)
+        self.bias = self.create_parameter(
+            shape=[normalized_shape], default_initializer=zeros_)
+        self.epsilon = epsilon
+        self.normalized_shape = [normalized_shape]
+
+    def forward(self, x):
+        u = x.mean(1, keepdim=True)
+        s = (x - u).pow(2).mean(1, keepdim=True)
+        x = (x - u) / paddle.sqrt(s + self.epsilon)
+        x = self.weight[:, None, None] * x + self.bias[:, None, None]
+        return x
+
+
+class Block(nn.Layer):
+    r""" ConvNeXt Block. There are two equivalent implementations:
+    (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
+    (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
+    We use (2) as we find it slightly faster in PyTorch
+
+    Args:
+        dim (int): Number of input channels.
+        drop_path (float): Stochastic depth rate. Default: 0.0
+        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
+    """
+
+    def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
+        super().__init__()
+        self.dwconv = nn.Conv2D(
+            dim, dim, 7, padding=3, groups=dim)  # depthwise conv
+        self.norm = nn.LayerNorm(dim, epsilon=1e-6)
+        # pointwise/1x1 convs, implemented with linear layers
+        self.pwconv1 = nn.Linear(dim, 4 * dim)
+        self.act = nn.GELU()
+        self.pwconv2 = nn.Linear(4 * dim, dim)
+        if layer_scale_init_value > 0:
+            self.gamma = self.create_parameter(
+                shape=[dim],
+                default_initializer=Constant(value=layer_scale_init_value))
+        else:
+            self.gamma = None
+        self.drop_path = DropPath(
+            drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x):
+        input = x
+        x = self.dwconv(x)
+        x = x.transpose([0, 2, 3, 1])  # (N, C, H, W) -> (N, H, W, C)
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.pwconv2(x)
+        if self.gamma is not None:
+            x = self.gamma * x
+        x = x.transpose([0, 3, 1, 2])  # (N, H, W, C) -> (N, C, H, W)
+
+        x = input + self.drop_path(x)
+        return x
+
+
+class ConvNeXt(nn.Layer):
+    r""" ConvNeXt
+        A PaddlePaddle impl of : `A ConvNet for the 2020s`  -
+          https://arxiv.org/pdf/2201.03545.pdf
+
+    Args:
+        in_chans (int): Number of input image channels. Default: 3
+        class_num (int): Number of classes for classification head. Default: 1000
+        depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
+        dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
+        drop_path_rate (float): Stochastic depth rate. Default: 0.
+        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
+        head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
+    """
+
+    def __init__(self,
+                 in_chans=3,
+                 class_num=1000,
+                 depths=[3, 3, 9, 3],
+                 dims=[96, 192, 384, 768],
+                 drop_path_rate=0.,
+                 layer_scale_init_value=1e-6,
+                 head_init_scale=1.):
+        super().__init__()
+
+        # stem and 3 intermediate downsampling conv layers
+        self.downsample_layers = nn.LayerList()
+        stem = nn.Sequential(
+            nn.Conv2D(
+                in_chans, dims[0], 4, stride=4),
+            ChannelsFirstLayerNorm(
+                dims[0], epsilon=1e-6))
+        self.downsample_layers.append(stem)
+        for i in range(3):
+            downsample_layer = nn.Sequential(
+                ChannelsFirstLayerNorm(
+                    dims[i], epsilon=1e-6),
+                nn.Conv2D(
+                    dims[i], dims[i + 1], 2, stride=2), )
+            self.downsample_layers.append(downsample_layer)
+
+        # 4 feature resolution stages, each consisting of multiple residual blocks
+        self.stages = nn.LayerList()
+        dp_rates = [
+            x.item() for x in paddle.linspace(0, drop_path_rate, sum(depths))
+        ]
+        cur = 0
+        for i in range(4):
+            stage = nn.Sequential(* [
+                Block(
+                    dim=dims[i],
+                    drop_path=dp_rates[cur + j],
+                    layer_scale_init_value=layer_scale_init_value)
+                for j in range(depths[i])
+            ])
+            self.stages.append(stage)
+            cur += depths[i]
+
+        self.norm = nn.LayerNorm(dims[-1], epsilon=1e-6)  # final norm layer
+        self.head = nn.Linear(dims[-1], class_num)
+
+        self.apply(self._init_weights)
+        self.head.weight.set_value(self.head.weight * head_init_scale)
+        self.head.bias.set_value(self.head.bias * head_init_scale)
+
+    def _init_weights(self, m):
+        if isinstance(m, (nn.Conv2D, nn.Linear)):
+            trunc_normal_(m.weight)
+            if m.bias is not None:
+                zeros_(m.bias)
+
+    def forward_features(self, x):
+        for i in range(4):
+            x = self.downsample_layers[i](x)
+            x = self.stages[i](x)
+        # global average pooling, (N, C, H, W) -> (N, C)
+        return self.norm(x.mean([-2, -1]))
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.head(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 ConvNeXt_tiny(pretrained=False, use_ssld=False, **kwargs):
+    model = ConvNeXt(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)
+    _load_pretrained(
+        pretrained, model, MODEL_URLS["ConvNeXt_tiny"], use_ssld=use_ssld)
+    return model

ppcls/arch/backbone/model_zoo/cspnet.py

@@ -20,7 +20,7 @@ import paddle.nn as nn
 import paddle.nn.functional as F
 from paddle import ParamAttr
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "CSPDarkNet53":

ppcls/arch/backbone/model_zoo/cswin_transformer.py

@@ -21,7 +21,7 @@ import paddle
 import paddle.nn as nn
 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
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "CSWinTransformer_tiny_224":

ppcls/arch/backbone/model_zoo/darknet.py

@@ -23,7 +23,7 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 from paddle.nn.initializer import Uniform
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "DarkNet53":

ppcls/arch/backbone/model_zoo/densenet.py

@@ -28,7 +28,7 @@ from paddle.nn.initializer import Uniform
 
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "DenseNet121":

ppcls/arch/backbone/model_zoo/distilled_vision_transformer.py

@@ -19,7 +19,7 @@ import paddle
 import paddle.nn as nn
 from .vision_transformer import VisionTransformer, Identity, trunc_normal_, zeros_
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "DeiT_tiny_patch16_224":

ppcls/arch/backbone/model_zoo/dla.py

@@ -23,8 +23,8 @@ import paddle.nn.functional as F
 
 from paddle.nn.initializer import Normal, Constant
 
-from ppcls.arch.backbone.base.theseus_layer import Identity
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ..base.theseus_layer import Identity
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "DLA34":

ppcls/arch/backbone/model_zoo/dpn.py

@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
 
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "DPN68":

ppcls/arch/backbone/model_zoo/efficientnet.py

@@ -26,7 +26,7 @@ import collections
 import re
 import copy
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "EfficientNetB0_small":

ppcls/arch/backbone/model_zoo/ghostnet.py

@@ -24,7 +24,7 @@ from paddle.nn import Conv2D, BatchNorm, AdaptiveAvgPool2D, Linear
 from paddle.regularizer import L2Decay
 from paddle.nn.initializer import Uniform, KaimingNormal
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "GhostNet_x0_5":

ppcls/arch/backbone/model_zoo/googlenet.py

@@ -24,7 +24,7 @@ from paddle.nn.initializer import Uniform
 
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "GoogLeNet":

ppcls/arch/backbone/model_zoo/gvt.py

@@ -25,7 +25,7 @@ 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
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "pcpvt_small":

ppcls/arch/backbone/model_zoo/hardnet.py

@@ -18,7 +18,7 @@
 import paddle
 import paddle.nn as nn
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     'HarDNet39_ds':

ppcls/arch/backbone/model_zoo/inception_v4.py

@@ -23,7 +23,7 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 from paddle.nn.initializer import Uniform
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "InceptionV4":

ppcls/arch/backbone/model_zoo/levit.py

@@ -27,7 +27,7 @@ from paddle.regularizer import L2Decay
 
 from .vision_transformer import trunc_normal_, zeros_, ones_, Identity
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "LeViT_128S":

ppcls/arch/backbone/model_zoo/mixnet.py

@@ -20,7 +20,7 @@ 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
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "MixNet_S":

ppcls/arch/backbone/model_zoo/mobilenet_v2.py

@@ -28,7 +28,7 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "MobileNetV2_x0_25":

ppcls/arch/backbone/model_zoo/mobilevit.py

@@ -23,7 +23,7 @@ import paddle.nn.functional as F
 from paddle.nn.initializer import KaimingUniform, TruncatedNormal, Constant
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "MobileViT_XXS":

ppcls/arch/backbone/model_zoo/peleenet.py

+# copyright (c) 2022 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.
+#
+# Code was heavily based on https://github.com/Robert-JunWang/PeleeNet
+# reference: https://arxiv.org/pdf/1804.06882.pdf
+
+import math
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle.nn.initializer import Normal, Constant
+
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+
+MODEL_URLS = {
+    "PeleeNet": ""  # TODO
+}
+
+__all__ = MODEL_URLS.keys()
+
+normal_ = lambda x, mean=0, std=1: Normal(mean, std)(x)
+constant_ = lambda x, value=0: Constant(value)(x)
+zeros_ = Constant(value=0.)
+ones_ = Constant(value=1.)
+
+
+class _DenseLayer(nn.Layer):
+    def __init__(self, num_input_features, growth_rate, bottleneck_width,
+                 drop_rate):
+        super(_DenseLayer, self).__init__()
+
+        growth_rate = int(growth_rate / 2)
+        inter_channel = int(growth_rate * bottleneck_width / 4) * 4
+
+        if inter_channel > num_input_features / 2:
+            inter_channel = int(num_input_features / 8) * 4
+            print('adjust inter_channel to ', inter_channel)
+
+        self.branch1a = BasicConv2D(
+            num_input_features, inter_channel, kernel_size=1)
+        self.branch1b = BasicConv2D(
+            inter_channel, growth_rate, kernel_size=3, padding=1)
+
+        self.branch2a = BasicConv2D(
+            num_input_features, inter_channel, kernel_size=1)
+        self.branch2b = BasicConv2D(
+            inter_channel, growth_rate, kernel_size=3, padding=1)
+        self.branch2c = BasicConv2D(
+            growth_rate, growth_rate, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        branch1 = self.branch1a(x)
+        branch1 = self.branch1b(branch1)
+
+        branch2 = self.branch2a(x)
+        branch2 = self.branch2b(branch2)
+        branch2 = self.branch2c(branch2)
+
+        return paddle.concat([x, branch1, branch2], 1)
+
+
+class _DenseBlock(nn.Sequential):
+    def __init__(self, num_layers, num_input_features, bn_size, growth_rate,
+                 drop_rate):
+        super(_DenseBlock, self).__init__()
+        for i in range(num_layers):
+            layer = _DenseLayer(num_input_features + i * growth_rate,
+                                growth_rate, bn_size, drop_rate)
+            setattr(self, 'denselayer%d' % (i + 1), layer)
+
+
+class _StemBlock(nn.Layer):
+    def __init__(self, num_input_channels, num_init_features):
+        super(_StemBlock, self).__init__()
+
+        num_stem_features = int(num_init_features / 2)
+
+        self.stem1 = BasicConv2D(
+            num_input_channels,
+            num_init_features,
+            kernel_size=3,
+            stride=2,
+            padding=1)
+        self.stem2a = BasicConv2D(
+            num_init_features,
+            num_stem_features,
+            kernel_size=1,
+            stride=1,
+            padding=0)
+        self.stem2b = BasicConv2D(
+            num_stem_features,
+            num_init_features,
+            kernel_size=3,
+            stride=2,
+            padding=1)
+        self.stem3 = BasicConv2D(
+            2 * num_init_features,
+            num_init_features,
+            kernel_size=1,
+            stride=1,
+            padding=0)
+        self.pool = nn.MaxPool2D(kernel_size=2, stride=2)
+
+    def forward(self, x):
+        out = self.stem1(x)
+
+        branch2 = self.stem2a(out)
+        branch2 = self.stem2b(branch2)
+        branch1 = self.pool(out)
+
+        out = paddle.concat([branch1, branch2], 1)
+        out = self.stem3(out)
+
+        return out
+
+
+class BasicConv2D(nn.Layer):
+    def __init__(self, in_channels, out_channels, activation=True, **kwargs):
+        super(BasicConv2D, self).__init__()
+        self.conv = nn.Conv2D(
+            in_channels, out_channels, bias_attr=False, **kwargs)
+        self.norm = nn.BatchNorm2D(out_channels)
+        self.activation = activation
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.norm(x)
+        if self.activation:
+            return F.relu(x)
+        else:
+            return x
+
+
+class PeleeNetDY(nn.Layer):
+    r"""PeleeNet model class, based on
+    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf> and
+     "Pelee: A Real-Time Object Detection System on Mobile Devices" <https://arxiv.org/pdf/1804.06882.pdf>`
+
+    Args:
+        growth_rate (int or list of 4 ints) - how many filters to add each layer (`k` in paper)
+        block_config (list of 4 ints) - how many layers in each pooling block
+        num_init_features (int) - the number of filters to learn in the first convolution layer
+        bottleneck_width (int or list of 4 ints) - multiplicative factor for number of bottle neck layers
+          (i.e. bn_size * k features in the bottleneck layer)
+        drop_rate (float) - dropout rate after each dense layer
+        class_num (int) - number of classification classes
+    """
+
+    def __init__(self,
+                 growth_rate=32,
+                 block_config=[3, 4, 8, 6],
+                 num_init_features=32,
+                 bottleneck_width=[1, 2, 4, 4],
+                 drop_rate=0.05,
+                 class_num=1000):
+
+        super(PeleeNetDY, self).__init__()
+
+        self.features = nn.Sequential(* [('stemblock', _StemBlock(
+            3, num_init_features)), ])
+
+        if type(growth_rate) is list:
+            growth_rates = growth_rate
+            assert len(growth_rates) == 4, \
+                'The growth rate must be the list and the size must be 4'
+        else:
+            growth_rates = [growth_rate] * 4
+
+        if type(bottleneck_width) is list:
+            bottleneck_widths = bottleneck_width
+            assert len(bottleneck_widths) == 4, \
+                'The bottleneck width must be the list and the size must be 4'
+        else:
+            bottleneck_widths = [bottleneck_width] * 4
+
+        # Each denseblock
+        num_features = num_init_features
+        for i, num_layers in enumerate(block_config):
+            block = _DenseBlock(
+                num_layers=num_layers,
+                num_input_features=num_features,
+                bn_size=bottleneck_widths[i],
+                growth_rate=growth_rates[i],
+                drop_rate=drop_rate)
+            setattr(self.features, 'denseblock%d' % (i + 1), block)
+            num_features = num_features + num_layers * growth_rates[i]
+
+            setattr(
+                self.features,
+                'transition%d' % (i + 1),
+                BasicConv2D(
+                    num_features,
+                    num_features,
+                    kernel_size=1,
+                    stride=1,
+                    padding=0))
+
+            if i != len(block_config) - 1:
+                setattr(
+                    self.features,
+                    'transition%d_pool' % (i + 1),
+                    nn.AvgPool2D(
+                        kernel_size=2, stride=2))
+                num_features = num_features
+
+        # Linear layer
+        self.classifier = nn.Linear(num_features, class_num)
+        self.drop_rate = drop_rate
+
+        self.apply(self._initialize_weights)
+
+    def forward(self, x):
+        features = self.features(x)
+        out = F.avg_pool2d(
+            features, kernel_size=features.shape[2:4]).flatten(1)
+        if self.drop_rate > 0:
+            out = F.dropout(out, p=self.drop_rate, training=self.training)
+        out = self.classifier(out)
+        return out
+
+    def _initialize_weights(self, m):
+        if isinstance(m, nn.Conv2D):
+            n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
+            normal_(m.weight, std=math.sqrt(2. / n))
+            if m.bias is not None:
+                zeros_(m.bias)
+        elif isinstance(m, nn.BatchNorm2D):
+            ones_(m.weight)
+            zeros_(m.bias)
+        elif isinstance(m, nn.Linear):
+            normal_(m.weight, std=0.01)
+            zeros_(m.bias)
+
+
+def _load_pretrained(pretrained, model, model_url, use_ssld):
+    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 PeleeNet(pretrained=False, use_ssld=False, **kwargs):
+    model = PeleeNetDY(**kwargs)
+    _load_pretrained(pretrained, model, MODEL_URLS["PeleeNet"], use_ssld)
+    return model

ppcls/arch/backbone/model_zoo/pvt_v2.py

@@ -24,7 +24,7 @@ from paddle.nn.initializer import TruncatedNormal, Constant
 
 from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity, drop_path
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "PVT_V2_B0":

ppcls/arch/backbone/model_zoo/rednet.py

@@ -20,7 +20,7 @@ import paddle.nn as nn
 
 from paddle.vision.models import resnet
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "RedNet26":

ppcls/arch/backbone/model_zoo/regnet.py

@@ -29,7 +29,7 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 from paddle.nn.initializer import Uniform
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "RegNetX_200MF":

ppcls/arch/backbone/model_zoo/repvgg.py

@@ -19,7 +19,7 @@ import paddle.nn as nn
 import paddle
 import numpy as np
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "RepVGG_A0":

ppcls/arch/backbone/model_zoo/res2net.py

@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
 
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "Res2Net50_26w_4s":

ppcls/arch/backbone/model_zoo/res2net_vd.py

@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
 
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "Res2Net50_vd_26w_4s":

ppcls/arch/backbone/model_zoo/resnest.py

@@ -30,7 +30,7 @@ from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
 from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 from paddle.regularizer import L2Decay
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "ResNeSt50_fast_1s1x64d":

ppcls/arch/backbone/model_zoo/resnet_vc.py

@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
 
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "ResNet50_vc":

ppcls/arch/backbone/model_zoo/resnext.py

@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
 
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "ResNeXt50_32x4d":

ppcls/arch/backbone/model_zoo/resnext101_wsl.py

@@ -22,7 +22,7 @@ from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
 from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 from paddle.nn.initializer import Uniform
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "ResNeXt101_32x8d_wsl":

ppcls/arch/backbone/model_zoo/resnext_vd.py

@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
 
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "ResNeXt50_vd_32x4d":

ppcls/arch/backbone/model_zoo/rexnet.py

@@ -24,7 +24,7 @@ from paddle import ParamAttr
 import paddle.nn as nn
 from math import ceil
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "ReXNet_1_0":

ppcls/arch/backbone/model_zoo/se_resnet_vd.py

@@ -28,7 +28,7 @@ from paddle.nn.initializer import Uniform
 
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "SE_ResNet18_vd":

ppcls/arch/backbone/model_zoo/se_resnext.py

@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
 
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "SE_ResNeXt50_32x4d":

ppcls/arch/backbone/model_zoo/se_resnext_vd.py

@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
 
 import math
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "SE_ResNeXt50_vd_32x4d":

ppcls/arch/backbone/model_zoo/shufflenet_v2.py

@@ -24,7 +24,7 @@ from paddle.nn import Layer, Conv2D, MaxPool2D, AdaptiveAvgPool2D, BatchNorm, Li
 from paddle.nn.initializer import KaimingNormal
 from paddle.nn.functional import swish
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "ShuffleNetV2_x0_25":

ppcls/arch/backbone/model_zoo/squeezenet.py

@@ -21,7 +21,7 @@ import paddle.nn.functional as F
 from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
 from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "SqueezeNet1_0":

ppcls/arch/backbone/model_zoo/tnt.py

@@ -23,8 +23,8 @@ import paddle.nn as nn
 
 from paddle.nn.initializer import TruncatedNormal, Constant
 
-from ppcls.arch.backbone.base.theseus_layer import Identity
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ..base.theseus_layer import Identity
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "TNT_small":

ppcls/arch/backbone/model_zoo/van.py

@@ -21,7 +21,7 @@ import paddle
 import paddle.nn as nn
 from paddle.nn.initializer import TruncatedNormal, Constant
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "VAN_tiny": "",  # TODO

ppcls/arch/backbone/model_zoo/vision_transformer.py

@@ -22,7 +22,7 @@ import paddle
 import paddle.nn as nn
 from paddle.nn.initializer import TruncatedNormal, Constant, Normal
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "ViT_small_patch16_224":

ppcls/arch/backbone/model_zoo/xception.py

@@ -24,7 +24,7 @@ from paddle.nn.initializer import Uniform
 import math
 import sys
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "Xception41":

ppcls/arch/backbone/model_zoo/xception_deeplab.py

@@ -21,7 +21,7 @@ import paddle.nn.functional as F
 from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
 from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 
-from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
 
 MODEL_URLS = {
     "Xception41_deeplab":

ppcls/arch/backbone/variant_models/pp_lcnet_variant.py

 import paddle
 from paddle.nn import Sigmoid
 from paddle.nn import Tanh
-from ppcls.arch.backbone.legendary_models.pp_lcnet import PPLCNet_x2_5
+from ..legendary_models.pp_lcnet import PPLCNet_x2_5
 
 __all__ = ["PPLCNet_x2_5_Tanh"]
 

ppcls/arch/backbone/variant_models/resnet_variant.py

 from paddle.nn import Conv2D
-from ppcls.arch.backbone.legendary_models.resnet import ResNet50, MODEL_URLS, _load_pretrained
+from ..legendary_models.resnet import ResNet50, MODEL_URLS, _load_pretrained
 
 __all__ = ["ResNet50_last_stage_stride1"]
 

ppcls/arch/backbone/variant_models/vgg_variant.py

 import paddle
 from paddle.nn import Sigmoid
-from ppcls.arch.backbone.legendary_models.vgg import VGG19
+from ..legendary_models.vgg import VGG19
 
 __all__ = ["VGG19Sigmoid"]
 

ppcls/arch/gears/bnneck.py

@@ -17,7 +17,7 @@ from __future__ import absolute_import, division, print_function
 import paddle
 import paddle.nn as nn
 
-from ppcls.arch.utils import get_param_attr_dict
+from ..utils import get_param_attr_dict
 
 
 class BNNeck(nn.Layer):

ppcls/arch/gears/fc.py

@@ -19,7 +19,7 @@ from __future__ import print_function
 import paddle
 import paddle.nn as nn
 
-from ppcls.arch.utils import get_param_attr_dict
+from ..utils import get_param_attr_dict
 
 
 class FC(nn.Layer):

ppcls/arch/slim/__init__.py

@@ -12,5 +12,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ppcls.arch.slim.prune import prune_model
-from ppcls.arch.slim.quant import quantize_model
+from .prune import prune_model
+from .quant import quantize_model

ppcls/arch/slim/prune.py

@@ -14,7 +14,7 @@
 
 from __future__ import absolute_import, division, print_function
 import paddle
-from ppcls.utils import logger
+from ...utils import logger
 
 
 def prune_model(config, model):
@@ -37,7 +37,6 @@ def prune_model(config, model):
         model.pruner = None
 
 
-
 def _prune_model(config, model):
     from paddleslim.analysis import dygraph_flops as flops
     logger.info("FLOPs before pruning: {}GFLOPs".format(

ppcls/arch/slim/quant.py

@@ -14,7 +14,7 @@
 
 from __future__ import absolute_import, division, print_function
 import paddle
-from ppcls.utils import logger
+from ...utils import logger
 
 QUANT_CONFIG = {
     # weight preprocess type, default is None and no preprocessing is performed.

ppcls/utils/check.py

@@ -22,8 +22,8 @@ import sys
 import paddle
 from paddle import is_compiled_with_cuda
 
-from ppcls.arch.utils import get_architectures, similar_architectures, get_blacklist_model_in_static_mode
-from ppcls.utils import logger
+from ..arch.utils import get_architectures, similar_architectures, get_blacklist_model_in_static_mode
+from . import logger
 
 
 def check_version():

ppcls/utils/config.py

@@ -16,8 +16,9 @@ import os
 import copy
 import argparse
 import yaml
-from ppcls.utils import logger
-from ppcls.utils import check
+from . import logger
+from . import check
+
 __all__ = ['get_config']
 
 

ppcls/utils/download.py

@@ -28,7 +28,7 @@ import time
 from collections import OrderedDict
 from tqdm import tqdm
 
-from ppcls.utils import logger
+from . import logger
 
 __all__ = ['get_weights_path_from_url']
 

ppcls/utils/gallery2fc.py

@@ -20,12 +20,12 @@ import sys
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(os.path.abspath(os.path.join(__dir__, '../../')))
 
-from ppcls.arch import build_model
-from ppcls.utils.config import parse_config, parse_args
-from ppcls.utils.save_load import load_dygraph_pretrain
-from ppcls.utils.logger import init_logger
-from ppcls.data import create_operators
-from ppcls.arch.slim import quantize_model
+from ..arch import build_model
+from .config import parse_config, parse_args
+from .save_load import load_dygraph_pretrain
+from .logger import init_logger
+from ..data import create_operators
+from ..arch.slim import quantize_model
 
 
 class GalleryLayer(paddle.nn.Layer):

ppcls/utils/model_zoo.py

@@ -23,8 +23,8 @@ import tarfile
 import tqdm
 import zipfile
 
-from ppcls.arch.utils import similar_architectures
-from ppcls.utils import logger
+from ..arch.utils import similar_architectures
+from . import logger
 
 __all__ = ['get']
 

ppcls/utils/save_load.py

@@ -20,7 +20,7 @@ import errno
 import os
 
 import paddle
-from ppcls.utils import logger
+from . import logger
 from .download import get_weights_path_from_url
 
 __all__ = ['init_model', 'save_model', 'load_dygraph_pretrain']