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Merge pull request #220 from wqz960/PaddleClas_resnest
add ResNeSt, config and docs
This commit is contained in:
littletomatodonkey
GitHub
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78
configs/ResNeSt/ResNeSt50.yaml
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78
configs/ResNeSt/ResNeSt50.yaml
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@ -0,0 +1,78 @@
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mode: 'train'
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ARCHITECTURE:
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name: 'ResNeSt50'
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pretrained_model: ""
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model_save_dir: "./output/"
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classes_num: 1000
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total_images: 1281167
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save_interval: 1
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validate: True
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valid_interval: 1
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epochs: 300
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topk: 5
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image_shape: [3, 224, 224]
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use_mix: True
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ls_epsilon: 0.1
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LEARNING_RATE:
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function: 'CosineWarmup'
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params:
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lr: 0.1
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OPTIMIZER:
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function: 'Momentum'
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params:
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momentum: 0.9
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regularizer:
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function: 'L2'
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factor: 0.000070
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TRAIN:
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batch_size: 256
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num_workers: 4
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file_list: "./dataset/ILSVRC2012/train_list.txt"
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data_dir: "./dataset/ILSVRC2012/"
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shuffle_seed: 0
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transforms:
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- DecodeImage:
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to_rgb: True
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to_np: False
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channel_first: False
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- RandCropImage:
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size: 224
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- RandFlipImage:
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flip_code: 1
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- AutoAugment:
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- NormalizeImage:
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scale: 1./255.
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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- ToCHWImage:
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mix:
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- CutmixOperator:
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alpha: 0.2
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VALID:
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batch_size: 64
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num_workers: 4
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file_list: "./dataset/ILSVRC2012/val_list.txt"
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data_dir: "./dataset/ILSVRC2012/"
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shuffle_seed: 0
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transforms:
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- DecodeImage:
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to_rgb: True
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to_np: False
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channel_first: False
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- ResizeImage:
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resize_short: 256
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- CropImage:
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size: 224
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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- ToCHWImage:
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9
docs/en/models/ResNeSt_RegNet_en.md
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9
docs/en/models/ResNeSt_RegNet_en.md
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@ -0,0 +1,9 @@
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## Overview
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The ResNeSt series was proposed in 2020. The original resnet network structure has been improved by introducing K groups and adding an attention module similar to SEBlock in different groups, the accuracy is greater than that of the basic model ResNet, but the parameter amount and flops are almost the same as the basic ResNet.
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## Accuracy, FLOPs and Parameters
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| Models | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPS<br>(G) | Parameters<br>(M) |
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|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
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| ResNeSt50 | 0.8102 | 0.9542| 0.8113 | -|5.39 | 27.5 |
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14
docs/zh_CN/models/ResNeSt_RegNet.md
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14
docs/zh_CN/models/ResNeSt_RegNet.md
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# ResNeSt以及RegNet网络
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## 概述
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ResNeSt系列模型是在2020年提出的，在原有的resnet网络结构上做了改进，通过引入K个Group和在不同Group中加入类似于SEBlock的attention模块，使得精度相比于基础模型ResNet有了大幅度的提高，且参数量和flops与基础的ResNet基本保持一致。
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## 精度、FLOPS和参数量
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| Models | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPS<br>(G) | Parameters<br>(M) |
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|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
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| ResNeSt50 | 0.8102 | 0.9542| 0.8113 | -|5.39 | 27.5 |
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@ -47,6 +47,7 @@ from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44
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from .darts_gs import DARTS_GS_6M, DARTS_GS_4M
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from .resnet_acnet import ResNet18_ACNet, ResNet34_ACNet, ResNet50_ACNet, ResNet101_ACNet, ResNet152_ACNet
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from .ghostnet import GhostNet_x0_5, GhostNet_x1_0, GhostNet_x1_3
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from .resnest import ResNeSt50, ResNeSt101, ResNeSt200, ResNeSt269, ResNeSt50_fast_1s1x64d, ResNeSt50_fast_2s1x64d, ResNeSt50_fast_4s1x64d, ResNeSt50_fast_1s2x40d, ResNeSt50_fast_2s2x40d, ResNeSt50_fast_2s2x40d, ResNeSt50_fast_4s2x40d, ResNeSt50_fast_1s4x24d
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# distillation model
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from .distillation_models import ResNet50_vd_distill_MobileNetV3_large_x1_0, ResNeXt101_32x16d_wsl_distill_ResNet50_vd
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648
ppcls/modeling/architectures/resnest.py
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648
ppcls/modeling/architectures/resnest.py
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle.fluid as fluid
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from paddle.fluid.initializer import MSRA, ConstantInitializer
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from paddle.fluid.param_attr import ParamAttr
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from paddle.fluid.regularizer import L2DecayRegularizer
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import math
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__all__ = [
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'ResNeSt50', 'ResNeSt101', 'ResNeSt200', 'ResNeSt269',
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'ResNeSt50_fast_1s1x64d', 'ResNeSt50_fast_2s1x64d',
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'ResNeSt50_fast_4s1x64d', 'ResNeSt50_fast_1s2x40d',
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'ResNeSt50_fast_2s2x40d', 'ResNeSt50_fast_2s2x40d',
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'ResNeSt50_fast_4s2x40d', 'ResNeSt50_fast_1s4x24d'
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]
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class ResNeSt():
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def __init__(self,
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layers,
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radix=1,
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groups=1,
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bottleneck_width=64,
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dilated=False,
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dilation=1,
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deep_stem=False,
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stem_width=64,
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avg_down=False,
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rectify_avg=False,
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avd=False,
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avd_first=False,
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final_drop=0.0,
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last_gamma=False,
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bn_decay=0.0):
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self.cardinality = groups
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self.bottleneck_width = bottleneck_width
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# ResNet-D params
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self.inplanes = stem_width * 2 if deep_stem else 64
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self.avg_down = avg_down
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self.last_gamma = last_gamma
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# ResNeSt params
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self.radix = radix
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self.avd = avd
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self.avd_first = avd_first
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self.deep_stem = deep_stem
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self.stem_width = stem_width
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self.layers = layers
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self.final_drop = final_drop
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self.dilated = dilated
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self.dilation = dilation
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self.bn_decay = bn_decay
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self.rectify_avg = rectify_avg
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def net(self, input, class_dim=1000):
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if self.deep_stem:
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x = self.conv_bn_layer(
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x=input,
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num_filters=self.stem_width,
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filters_size=3,
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stride=2,
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groups=1,
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act="relu",
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name="conv1")
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x = self.conv_bn_layer(
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x=x,
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num_filters=self.stem_width,
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filters_size=3,
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stride=1,
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groups=1,
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act="relu",
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name="conv2")
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x = self.conv_bn_layer(
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x=x,
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num_filters=self.stem_width * 2,
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filters_size=3,
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stride=1,
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groups=1,
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act="relu",
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name="conv3")
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else:
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x = self.conv_bn_layer(
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x=input,
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num_filters=64,
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filters_size=7,
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stride=2,
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act="relu",
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name="conv1")
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x = fluid.layers.pool2d(
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input=x,
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pool_size=3,
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pool_type="max",
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pool_stride=2,
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pool_padding=1)
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x = self.resnest_layer(
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x=x,
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planes=64,
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blocks=self.layers[0],
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is_first=False,
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name="layer1")
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x = self.resnest_layer(
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x=x,
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planes=128,
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blocks=self.layers[1],
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stride=2,
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name="layer2")
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if self.dilated or self.dilation == 4:
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x = self.resnest_layer(
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x=x,
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planes=256,
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blocks=self.layers[2],
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stride=1,
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dilation=2,
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name="layer3")
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x = self.resnest_layer(
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x=x,
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planes=512,
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blocks=self.layers[3],
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stride=1,
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dilation=4,
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name="layer4")
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elif self.dilation == 2:
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x = self.resnest_layer(
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x=x,
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planes=256,
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blocks=self.layers[2],
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stride=2,
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dilation=1,
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name="layer3")
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x = self.resnest_layer(
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x=x,
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planes=512,
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blocks=self.layers[3],
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stride=1,
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dilation=2,
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name="layer4")
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else:
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x = self.resnest_layer(
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x=x,
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planes=256,
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blocks=self.layers[2],
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stride=2,
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name="layer3")
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x = self.resnest_layer(
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x=x,
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planes=512,
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blocks=self.layers[3],
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stride=2,
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name="layer4")
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x = fluid.layers.pool2d(
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input=x, pool_type="avg", global_pooling=True)
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x = fluid.layers.dropout(
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x=x, dropout_prob=self.final_drop)
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stdv = 1.0 / math.sqrt(x.shape[1] * 1.0)
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x = fluid.layers.fc(
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input=x,
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size=class_dim,
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param_attr=ParamAttr(
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name="fc_weights",
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initializer=fluid.initializer.Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(name="fc_offset"))
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return x
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def conv_bn_layer(self,
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x,
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num_filters,
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filters_size,
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stride=1,
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groups=1,
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act=None,
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name=None):
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x = fluid.layers.conv2d(
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input=x,
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num_filters=num_filters,
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filter_size=filters_size,
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stride=stride,
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padding=(filters_size - 1) // 2,
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groups=groups,
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act=None,
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param_attr=ParamAttr(
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initializer=MSRA(), name=name + "_weight"),
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bias_attr=False)
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x = fluid.layers.batch_norm(
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input=x,
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act=act,
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param_attr=ParamAttr(
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name=name + "_scale",
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regularizer=L2DecayRegularizer(
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regularization_coeff=self.bn_decay)),
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bias_attr=ParamAttr(
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name=name + "_offset",
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regularizer=L2DecayRegularizer(
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regularization_coeff=self.bn_decay)),
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moving_mean_name=name + "_mean",
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moving_variance_name=name + "_variance")
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return x
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def rsoftmax(self, x, radix, cardinality):
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batch, r, h, w = x.shape
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if radix > 1:
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x = fluid.layers.reshape(
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x=x,
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shape=[
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0, cardinality, radix, int(r * h * w / cardinality / radix)
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])
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x = fluid.layers.transpose(x=x, perm=[0, 2, 1, 3])
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x = fluid.layers.softmax(input=x, axis=1)
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x = fluid.layers.reshape(x=x, shape=[0, r * h * w])
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else:
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x = fluid.layers.sigmoid(x=x)
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return x
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def splat_conv(self,
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x,
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in_channels,
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channels,
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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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bias=True,
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radix=2,
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reduction_factor=4,
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rectify_avg=False,
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name=None):
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x = self.conv_bn_layer(
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x=x,
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num_filters=channels * radix,
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filters_size=kernel_size,
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stride=stride,
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groups=groups * radix,
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act="relu",
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name=name + "_splat1")
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batch, rchannel = x.shape[:2]
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if radix > 1:
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splited = fluid.layers.split(input=x, num_or_sections=radix, dim=1)
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gap = fluid.layers.sum(x=splited)
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else:
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gap = x
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gap = fluid.layers.pool2d(
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input=gap, pool_type="avg", global_pooling=True)
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inter_channels = int(max(in_channels * radix // reduction_factor, 32))
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gap = self.conv_bn_layer(
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x=gap,
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num_filters=inter_channels,
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filters_size=1,
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groups=groups,
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act="relu",
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name=name + "_splat2")
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atten = fluid.layers.conv2d(
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input=gap,
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num_filters=channels * radix,
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filter_size=1,
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stride=1,
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padding=0,
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groups=groups,
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act=None,
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param_attr=ParamAttr(
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name=name + "_splat_weights", initializer=MSRA()),
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bias_attr=False)
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atten = self.rsoftmax(
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x=atten, radix=radix, cardinality=groups)
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atten = fluid.layers.reshape(x=atten, shape=[-1, atten.shape[1], 1, 1])
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if radix > 1:
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attens = fluid.layers.split(
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input=atten, num_or_sections=radix, dim=1)
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out = fluid.layers.sum([
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fluid.layers.elementwise_mul(
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x=att, y=split) for (att, split) in zip(attens, splited)
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])
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else:
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out = fluid.layers.elementwise_mul(atten, x)
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return out
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def bottleneck(self,
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x,
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inplanes,
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planes,
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stride=1,
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radix=1,
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cardinality=1,
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bottleneck_width=64,
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avd=False,
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avd_first=False,
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dilation=1,
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is_first=False,
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rectify_avg=False,
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last_gamma=False,
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name=None):
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short = x
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group_width = int(planes * (bottleneck_width / 64.)) * cardinality
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x = self.conv_bn_layer(
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x=x,
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num_filters=group_width,
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filters_size=1,
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stride=1,
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groups=1,
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act="relu",
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name=name + "_conv1")
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if avd and avd_first and (stride > 1 or is_first):
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x = fluid.layers.pool2d(
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input=x,
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pool_size=3,
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||||
pool_type="avg",
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||||
pool_stride=stride,
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pool_padding=1)
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if radix >= 1:
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x = self.splat_conv(
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x=x,
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in_channels=group_width,
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channels=group_width,
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kernel_size=3,
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stride=1,
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padding=dilation,
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||||
dilation=dilation,
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||||
groups=cardinality,
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bias=False,
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radix=radix,
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||||
rectify_avg=rectify_avg,
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name=name + "_splatconv")
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||||
else:
|
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x = self.conv_bn_layer(
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x=x,
|
||||
num_filters=group_width,
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||||
filters_size=3,
|
||||
stride=1,
|
||||
padding=dilation,
|
||||
dilation=dialtion,
|
||||
groups=cardinality,
|
||||
act="relu",
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name=name + "_conv2")
|
||||
|
||||
if avd and avd_first == False and (stride > 1 or is_first):
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x = fluid.layers.pool2d(
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input=x,
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||||
pool_size=3,
|
||||
pool_type="avg",
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||||
pool_stride=stride,
|
||||
pool_padding=1)
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||||
x = self.conv_bn_layer(
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x=x,
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||||
num_filters=planes * 4,
|
||||
filters_size=1,
|
||||
stride=1,
|
||||
groups=1,
|
||||
act=None,
|
||||
name=name + "_conv3")
|
||||
|
||||
if stride != 1 or self.inplanes != planes * 4:
|
||||
if self.avg_down:
|
||||
if dilation == 1:
|
||||
short = fluid.layers.pool2d(
|
||||
input=short,
|
||||
pool_size=stride,
|
||||
pool_type="avg",
|
||||
pool_stride=stride,
|
||||
ceil_mode=True)
|
||||
else:
|
||||
short = fluid.layers.pool2d(
|
||||
input=short,
|
||||
pool_size=1,
|
||||
pool_type="avg",
|
||||
pool_stride=1,
|
||||
ceil_mode=True)
|
||||
short = fluid.layers.conv2d(
|
||||
input=short,
|
||||
num_filters=planes * 4,
|
||||
filter_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
groups=1,
|
||||
act=None,
|
||||
param_attr=ParamAttr(
|
||||
name=name + "_weights", initializer=MSRA()),
|
||||
bias_attr=False)
|
||||
else:
|
||||
short = fluid.layers.conv2d(
|
||||
input=short,
|
||||
num_filters=planes * 4,
|
||||
filter_size=1,
|
||||
stride=stride,
|
||||
param_attr=ParamAttr(
|
||||
name=name + "_shortcut_weights", initializer=MSRA()),
|
||||
bias_attr=False)
|
||||
|
||||
short = fluid.layers.batch_norm(
|
||||
input=short,
|
||||
act=None,
|
||||
param_attr=ParamAttr(
|
||||
name=name + "_shortcut_scale",
|
||||
regularizer=L2DecayRegularizer(
|
||||
regularization_coeff=self.bn_decay)),
|
||||
bias_attr=ParamAttr(
|
||||
name=name + "_shortcut_offset",
|
||||
regularizer=L2DecayRegularizer(
|
||||
regularization_coeff=self.bn_decay)),
|
||||
moving_mean_name=name + "_shortcut_mean",
|
||||
moving_variance_name=name + "_shortcut_variance")
|
||||
|
||||
return fluid.layers.elementwise_add(x=short, y=x, act="relu")
|
||||
|
||||
def resnest_layer(self,
|
||||
x,
|
||||
planes,
|
||||
blocks,
|
||||
stride=1,
|
||||
dilation=1,
|
||||
is_first=True,
|
||||
name=None):
|
||||
if dilation == 1 or dilation == 2:
|
||||
x = self.bottleneck(
|
||||
x=x,
|
||||
inplanes=self.inplanes,
|
||||
planes=planes,
|
||||
stride=stride,
|
||||
radix=self.radix,
|
||||
cardinality=self.cardinality,
|
||||
bottleneck_width=self.bottleneck_width,
|
||||
avd=self.avd,
|
||||
avd_first=self.avd_first,
|
||||
dilation=1,
|
||||
is_first=is_first,
|
||||
rectify_avg=self.rectify_avg,
|
||||
last_gamma=self.last_gamma,
|
||||
name=name + "_bottleneck_0")
|
||||
elif dilation == 4:
|
||||
x = self.bottleneck(
|
||||
x=x,
|
||||
inplanes=self.inplanes,
|
||||
planes=planes,
|
||||
stride=stride,
|
||||
radix=self.radix,
|
||||
cardinality=self.cardinality,
|
||||
bottleneck_width=self.bottleneck_width,
|
||||
avd=self.avd,
|
||||
avd_first=self.avd_first,
|
||||
dilation=2,
|
||||
is_first=is_first,
|
||||
rectify_avg=self.rectify_avg,
|
||||
last_gamma=self.last_gamma,
|
||||
name=name + "_bottleneck_0")
|
||||
else:
|
||||
raise RuntimeError("=>unknown dilation size")
|
||||
|
||||
self.inplanes = planes * 4
|
||||
for i in range(1, blocks):
|
||||
name = name + "_bottleneck_" + str(i)
|
||||
x = self.bottleneck(
|
||||
x=x,
|
||||
inplanes=self.inplanes,
|
||||
planes=planes,
|
||||
radix=self.radix,
|
||||
cardinality=self.cardinality,
|
||||
bottleneck_width=self.bottleneck_width,
|
||||
avd=self.avd,
|
||||
avd_first=self.avd_first,
|
||||
dilation=dilation,
|
||||
rectify_avg=self.rectify_avg,
|
||||
last_gamma=self.last_gamma,
|
||||
name=name)
|
||||
return x
|
||||
|
||||
|
||||
def ResNeSt50(**args):
|
||||
model = ResNeSt(
|
||||
layers=[3, 4, 6, 3],
|
||||
radix=2,
|
||||
groups=1,
|
||||
bottleneck_width=64,
|
||||
deep_stem=True,
|
||||
stem_width=32,
|
||||
avg_down=True,
|
||||
avd=True,
|
||||
avd_first=False,
|
||||
final_drop=0.0,
|
||||
**args)
|
||||
return model
|
||||
|
||||
|
||||
def ResNeSt101(**args):
|
||||
model = ResNeSt(
|
||||
layers=[3, 4, 23, 3],
|
||||
radix=2,
|
||||
groups=1,
|
||||
bottleneck_width=64,
|
||||
deep_stem=True,
|
||||
stem_width=64,
|
||||
avg_down=True,
|
||||
avd=True,
|
||||
avd_first=False,
|
||||
final_drop=0.0,
|
||||
**args)
|
||||
return model
|
||||
|
||||
|
||||
def ResNeSt200(**args):
|
||||
model = ResNeSt(
|
||||
layers=[3, 24, 36, 3],
|
||||
radix=2,
|
||||
groups=1,
|
||||
bottleneck_width=64,
|
||||
deep_stem=True,
|
||||
stem_width=64,
|
||||
avg_down=True,
|
||||
avd=True,
|
||||
avd_first=False,
|
||||
final_drop=0.2,
|
||||
**args)
|
||||
return model
|
||||
|
||||
|
||||
def ResNeSt269(**args):
|
||||
model = ResNeSt(
|
||||
layers=[3, 30, 48, 8],
|
||||
radix=2,
|
||||
groups=1,
|
||||
bottleneck_width=64,
|
||||
deep_stem=True,
|
||||
stem_width=64,
|
||||
avg_down=True,
|
||||
avd=True,
|
||||
avd_first=False,
|
||||
final_drop=0.2,
|
||||
**args)
|
||||
return model
|
||||
|
||||
|
||||
def ResNeSt50_fast_1s1x64d(**args):
|
||||
model = ResNeSt(
|
||||
layers=[3, 4, 6, 3],
|
||||
radix=1,
|
||||
groups=1,
|
||||
bottleneck_width=64,
|
||||
deep_stem=True,
|
||||
stem_width=32,
|
||||
avg_down=True,
|
||||
avd=True,
|
||||
avd_first=True,
|
||||
final_drop=0.0,
|
||||
**args)
|
||||
return model
|
||||
|
||||
|
||||
def ResNeSt50_fast_2s1x64d(**args):
|
||||
model = ResNeSt(
|
||||
layers=[3, 4, 6, 3],
|
||||
radix=2,
|
||||
groups=1,
|
||||
bottleneck_width=64,
|
||||
deep_stem=True,
|
||||
stem_width=32,
|
||||
avg_down=True,
|
||||
avd=True,
|
||||
avd_first=True,
|
||||
final_drop=0.0,
|
||||
**args)
|
||||
return model
|
||||
|
||||
|
||||
def ResNeSt50_fast_4s1x64d(**args):
|
||||
model = ResNeSt(
|
||||
layers=[3, 4, 6, 3],
|
||||
radix=2,
|
||||
groups=1,
|
||||
bottleneck_width=64,
|
||||
deep_stem=True,
|
||||
stem_width=32,
|
||||
avg_down=True,
|
||||
avd=True,
|
||||
avd_first=True,
|
||||
final_drop=0.0,
|
||||
**args)
|
||||
return model
|
||||
|
||||
|
||||
def ResNeSt50_fast_1s2x40d(**args):
|
||||
model = ResNeSt(
|
||||
layers=[3, 4, 6, 3],
|
||||
radix=1,
|
||||
groups=2,
|
||||
bottleneck_width=40,
|
||||
deep_stem=True,
|
||||
stem_width=32,
|
||||
avg_down=True,
|
||||
avd=True,
|
||||
avd_first=True,
|
||||
final_drop=0.0,
|
||||
**args)
|
||||
return model
|
||||
|
||||
|
||||
def ResNeSt50_fast_2s2x40d(**args):
|
||||
model = ResNeSt(
|
||||
layers=[3, 4, 6, 3],
|
||||
radix=2,
|
||||
groups=2,
|
||||
bottleneck_width=40,
|
||||
deep_stem=True,
|
||||
stem_width=32,
|
||||
avg_down=True,
|
||||
avd=True,
|
||||
avd_first=True,
|
||||
final_drop=0.0,
|
||||
**args)
|
||||
return model
|
||||
|
||||
|
||||
def ResNeSt50_fast_4s2x40d(**args):
|
||||
model = ResNeSt(
|
||||
layers=[3, 4, 6, 3],
|
||||
radix=4,
|
||||
groups=2,
|
||||
bottleneck_width=40,
|
||||
deep_stem=True,
|
||||
stem_width=32,
|
||||
avg_down=True,
|
||||
avd=True,
|
||||
avd_first=True,
|
||||
final_drop=0.0,
|
||||
**args)
|
||||
return model
|
||||
|
||||
|
||||
def ResNeSt50_fast_1s4x24d(**args):
|
||||
model = ResNeSt(
|
||||
layers=[3, 4, 6, 3],
|
||||
radix=1,
|
||||
groups=4,
|
||||
bottleneck_width=24,
|
||||
deep_stem=True,
|
||||
stem_width=32,
|
||||
avg_down=True,
|
||||
avd=True,
|
||||
avd_first=True,
|
||||
final_drop=0.0,
|
||||
**args)
|
||||
return model
|
||||
Loading…
x
Reference in New Issue
Block a user