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fix: change to relative import

pull/2226/head
root 2022-08-17 14:34:06 +00:00 committed by Tingquan Gao
parent d82e27fd17
commit 6cb17cfe44
71 changed files with 668 additions and 174 deletions
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2
__init__.py
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@ -14,4 +14,4 @@
__all__ = ['PaddleClas']
from .paddleclas import PaddleClas
from ppcls.arch.backbone import *
from .ppcls.arch.backbone import *

10
ppcls/arch/__init__.py
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@ -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"]

110
ppcls/arch/backbone/__init__.py
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@ -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)

2
ppcls/arch/backbone/base/theseus_layer.py
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@ -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):

4
ppcls/arch/backbone/legendary_models/esnet.py
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@ -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":

4
ppcls/arch/backbone/legendary_models/hrnet.py
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@ -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":

4
ppcls/arch/backbone/legendary_models/inception_v3.py
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@ -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":

4
ppcls/arch/backbone/legendary_models/mobilenet_v1.py
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@ -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":

5
ppcls/arch/backbone/legendary_models/mobilenet_v3.py
View File

@ -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":

7
ppcls/arch/backbone/legendary_models/pp_hgnet.py
View File

@ -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,
@ -230,7 +231,7 @@ class PPHGNet(TheseusLayer):
k]
self.stages.append(
HG_Stage(in_channels, mid_channels, out_channels, block_num,
layer_num, downsample))
layer_num, downsample))
self.avg_pool = AdaptiveAvgPool2D(1)
if self.use_last_conv:

40
ppcls/arch/backbone/legendary_models/pp_lcnet.py
View File

@ -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)

5
ppcls/arch/backbone/legendary_models/pp_lcnet_v2.py
View File

@ -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":

8
ppcls/arch/backbone/legendary_models/resnet.py
View File

@ -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,

6
ppcls/arch/backbone/legendary_models/swin_transformer.py
View File

@ -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":

4
ppcls/arch/backbone/legendary_models/vgg.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/alexnet.py
View File

@ -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":

232
ppcls/arch/backbone/model_zoo/convnext.py 100644
View File

@ -0,0 +1,232 @@
# 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

2
ppcls/arch/backbone/model_zoo/cspnet.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/cswin_transformer.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/darknet.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/densenet.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/distilled_vision_transformer.py
View File

@ -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":

4
ppcls/arch/backbone/model_zoo/dla.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/dpn.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/efficientnet.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/ghostnet.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/googlenet.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/gvt.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/hardnet.py
View File

@ -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':

2
ppcls/arch/backbone/model_zoo/inception_v4.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/levit.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/mixnet.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/mobilenet_v2.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/mobilevit.py
View File

@ -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":

263
ppcls/arch/backbone/model_zoo/peleenet.py 100644
View File

@ -0,0 +1,263 @@
# 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

2
ppcls/arch/backbone/model_zoo/pvt_v2.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/rednet.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/regnet.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/repvgg.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/res2net.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/res2net_vd.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/resnest.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/resnet_vc.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/resnext.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/resnext101_wsl.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/resnext_vd.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/rexnet.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/se_resnet_vd.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/se_resnext.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/se_resnext_vd.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/shufflenet_v2.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/squeezenet.py
View File

@ -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":

4
ppcls/arch/backbone/model_zoo/tnt.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/van.py
View File

@ -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

2
ppcls/arch/backbone/model_zoo/vision_transformer.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/xception.py
View File

@ -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":

2
ppcls/arch/backbone/model_zoo/xception_deeplab.py
View File

@ -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":

2
ppcls/arch/backbone/variant_models/pp_lcnet_variant.py
View File

@ -1,7 +1,7 @@
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"]

2
ppcls/arch/backbone/variant_models/resnet_variant.py
View File

@ -1,5 +1,5 @@
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"]

2
ppcls/arch/backbone/variant_models/vgg_variant.py
View File

@ -1,6 +1,6 @@
import paddle
from paddle.nn import Sigmoid
from ppcls.arch.backbone.legendary_models.vgg import VGG19
from ..legendary_models.vgg import VGG19
__all__ = ["VGG19Sigmoid"]

2
ppcls/arch/gears/bnneck.py
View File

@ -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):

2
ppcls/arch/gears/fc.py
View File

@ -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):

4
ppcls/arch/slim/__init__.py
View File

@ -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

3
ppcls/arch/slim/prune.py
View File

@ -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(

2
ppcls/arch/slim/quant.py
View File

@ -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.

4
ppcls/utils/check.py
View File

@ -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():

5
ppcls/utils/config.py
View File

@ -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']

2
ppcls/utils/download.py
View File

@ -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']

12
ppcls/utils/gallery2fc.py
View File

@ -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):

4
ppcls/utils/model_zoo.py
View File

@ -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']

2
ppcls/utils/save_load.py
View File

@ -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']