【实战干货】用Pytorch轻松实现28个视觉Transformer（附代码解读）
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作者丨科技猛兽
转自丨极市平台
【导读】本文将介绍一个优秀的PyTorch开源库——timm库，并对其中的vision transformer.py代码进行了详细解读。万字长文，建议先点击收藏！
Transformer 架构早已在自然语言处理任务中得到广泛应用，但在计算机视觉领域中仍然受到限制。在计算机视觉领域，目前已有大量工作表明模型对 CNN 的依赖不是必需的，当直接应用于图像块序列时，Transformer 也能很好地执行图像分类任务。
本文将简要介绍了优秀的 PyTorch Image Model 库：timm库。与此同时，将会为大家详细介绍其中的视觉Transformer代码以及一个优秀的视觉Transformer 的PyTorch实现，以帮助大家更快地开展相关实验。
什么是timm库？
PyTorchImageModels，简称timm，是一个巨大的PyTorch代码集合，包括了一系列：
image models
layers
utilities
optimizers
schedulers
data-loaders / augmentations
training / validation scripts
旨在将各种SOTA模型整合在一起，并具有复现ImageNet训练结果的能力。
timm库作者是来自加拿大温哥华的Ross Wightman。
作者github链接：
https://github.com/rwightman
timm库链接：
https://github.com/rwightman/pytorch-image-models
所有的PyTorch模型及其对应arxiv链接如下：
Big Transfer ResNetV2 (BiT) - https://arxiv.org/abs/1912.11370
CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929
DeiT (Vision Transformer) - https://arxiv.org/abs/2012.12877
DenseNet - https://arxiv.org/abs/1608.06993
DLA - https://arxiv.org/abs/1707.06484
DPN (Dual-Path Network) - https://arxiv.org/abs/1707.01629
EfficientNet (MBConvNet Family)
EfficientNet NoisyStudent (B0-B7, L2) - https://arxiv.org/abs/1911.04252
EfficientNet AdvProp (B0-B8) - https://arxiv.org/abs/1911.09665
EfficientNet (B0-B7) - https://arxiv.org/abs/1905.11946
EfficientNet-EdgeTPU (S, M, L) - https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html
FBNet-C - https://arxiv.org/abs/1812.03443
MixNet - https://arxiv.org/abs/1907.09595
MNASNet B1, A1 (Squeeze-Excite), and Small - https://arxiv.org/abs/1807.11626
MobileNet-V2 - https://arxiv.org/abs/1801.04381
Single-Path NAS - https://arxiv.org/abs/1904.02877
GPU-Efficient Networks - https://arxiv.org/abs/2006.14090
HRNet - https://arxiv.org/abs/1908.07919
Inception-V3 - https://arxiv.org/abs/1512.00567
Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
MobileNet-V3 (MBConvNet w/ Efficient Head) - https://arxiv.org/abs/1905.02244
NASNet-A - https://arxiv.org/abs/1707.07012
NFNet-F - https://arxiv.org/abs/2102.06171
NF-RegNet / NF-ResNet - https://arxiv.org/abs/2101.08692
PNasNet - https://arxiv.org/abs/1712.00559
RegNet - https://arxiv.org/abs/2003.13678
RepVGG - https://arxiv.org/abs/2101.03697
ResNet/ResNeXt
ResNet (v1b/v1.5) - https://arxiv.org/abs/1512.03385
ResNeXt - https://arxiv.org/abs/1611.05431
'Bag of Tricks' / Gluon C, D, E, S variations - https://arxiv.org/abs/1812.01187
Weakly-supervised (WSL) Instagram pretrained / ImageNet tuned ResNeXt101 - https://arxiv.org/abs/1805.00932
Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet/ResNeXts - https://arxiv.org/abs/1905.00546
ECA-Net (ECAResNet) - https://arxiv.org/abs/1910.03151v4
Squeeze-and-Excitation Networks (SEResNet) - https://arxiv.org/abs/1709.01507
Res2Net - https://arxiv.org/abs/1904.01169
ResNeSt - https://arxiv.org/abs/2004.08955
ReXNet - https://arxiv.org/abs/2007.00992
SelecSLS - https://arxiv.org/abs/1907.00837
Selective Kernel Networks - https://arxiv.org/abs/1903.06586
TResNet - https://arxiv.org/abs/2003.13630
Vision Transformer - https://arxiv.org/abs/2010.11929
VovNet V2 and V1 - https://arxiv.org/abs/1911.06667
Xception - https://arxiv.org/abs/1610.02357
Xception (Modified Aligned, Gluon) - https://arxiv.org/abs/1802.02611
Xception (Modified Aligned, TF) - https://arxiv.org/abs/1802.02611
timm库特点
所有的模型都有默认的API：
accessing/changing the classifier -
get_classifierandreset_classifier只对features做前向传播 -
forward_features
所有模型都支持多尺度特征提取 (feature pyramids) (通过create_model函数)：
create_model(name, features_only=True, out_indices=..., output_stride=...)
out_indices 指定返回哪个feature maps to return, 从0开始，out_indices[i]对应着 C(i + 1) feature level。
output_stride 通过dilated convolutions控制网络的output stride。大多数网络默认 stride 32 。
所有的模型都有一致的pretrained weight loader，adapts last linear if necessary。
训练方式支持：
NVIDIA DDP w/ a single GPU per process, multiple processes with APEX present (AMP mixed-precision optional)
PyTorch DistributedDataParallel w/ multi-gpu, single process (AMP disabled as it crashes when enabled)
PyTorch w/ single GPU single process (AMP optional)
动态的全局池化方式可以选择： average pooling, max pooling, average + max, or concat([average, max])，默认是adaptive average。
Schedulers：
Schedulers 包括step,cosinew/ restarts,tanhw/ restarts,plateau 。
Optimizer：
rmsprop_tfadapted from PyTorch RMSProp by myself. Reproduces much improved Tensorflow RMSProp behaviour.radamby Liyuan Liu (https://arxiv.org/abs/1908.03265)novogradby Masashi Kimura (https://arxiv.org/abs/1905.11286)lookaheadadapted from impl by Liam (https://arxiv.org/abs/1907.08610)fusedoptimizers by name with NVIDIA Apex installedadampandsgdpby Naver ClovAI (https://arxiv.org/abs/2006.08217)adafactoradapted from FAIRSeq impl (https://arxiv.org/abs/1804.04235)adahessianby David Samuel (https://arxiv.org/abs/2006.00719)
timm库 vision_transformer.py代码解读
代码来自：
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
对应的论文是ViT，是除了官方开源的代码之外的又一个优秀的PyTorch implement。
An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://arxiv.org/abs/2010.11929
另一篇工作DeiT也大量借鉴了timm库这份代码的实现：
Training data-efficient image transformers & distillation through attention
Training data-efficient image transformers & distillation through attention
https://arxiv.org/abs/2012.12877
vision_transformer.py：
代码中定义的变量的含义如下：
img_size：tuple 类型，里面是int类型，代表输入的图片大小，默认是 224。
patch_size：tuple 类型，里面是int类型，代表Patch的大小，默认是 16。
in_chans：int 类型，代表输入图片的channel数，默认是3。
num_classes：int 类型classification head的分类数，比如CIFAR100就是100，默认是 1000。
embed_dim：int 类型Transformer的embedding dimension，默认是 768。
depth：int 类型，Transformer的Block的数量，默认是 12。
num_heads：int 类型，attention heads的数量，默认是12。
mlp_ratio：int 类型，mlp hidden dim/embedding dim的值，默认是 4。
qkv_bias：bool 类型，attention模块计算qkv时需要bias吗，默认是 True。
qk_scale： 一般设置成 None 就行。
drop_rate：float 类型，dropout rate，默认是 0。
attn_drop_rate：float 类型，attention模块的dropout rate，默认是 0。
drop_path_rate：float 类型，默认是 0。
hybrid_backbone：nn.Module 类型，在把图片转换成Patch之前，需要先通过一个Backbone吗？默认是 None。
如果是None，就直接把图片转化成Patch。
如果不是None，就先通过这个Backbone，再转化成Patch。
norm_layer：nn.Module 类型，归一化层类型，默认是 None。
1. 导入必要的库和模型：
import math
import logging
from functools import partial
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import load_pretrained
from .layers import StdConv2dSame, DropPath, to_2tuple, trunc_normal_
from .resnet import resnet26d, resnet50d
from .resnetv2 import ResNetV2
from .registry import register_model
2. 定义一个字典，代表标准的模型，如果需要更改模型超参数只需要改变_cfg的传入的参数即可。
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head',
**kwargs
}
3. default_cfgs代表支持的所有模型，也定义成字典的形式：
vit_small_patch16_224里面的small代表小模型。
ViT的第一步要把图片分成一个个patch，然后把这些patch组合在一起作为对图像的序列化操作，比如一张224 × 224的图片分成大小为16 × 16的patch，那一共可以分成196个。所以这个图片就序列化成了(196, 256)的tensor。所以这里的：
16： 就代表patch的大小。
224： 就代表输入图片的大小。
按照这个命名方式，支持的模型有：vit_base_patch16_224，vit_base_patch16_384等等。后面的vit_deit_base_patch16_224等等模型代表DeiT这篇论文的模型。
default_cfgs = { # patch models (my experiments) 'vit_small_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth', ),
# patch models (weights ported from official Google JAX impl) 'vit_base_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), ), 'vit_base_patch32_224': _cfg( url='', # no official model weights for this combo, only for in21k mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_base_patch16_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0), 'vit_base_patch32_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0), 'vit_large_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_large_patch32_224': _cfg( url='', # no official model weights for this combo, only for in21k mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_large_patch16_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0), 'vit_large_patch32_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
# patch models, imagenet21k (weights ported from official Google JAX impl) 'vit_base_patch16_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch16_224_in21k-e5005f0a.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_base_patch32_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch32_224_in21k-8db57226.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_large_patch16_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch16_224_in21k-606da67d.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_large_patch32_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), 'vit_huge_patch14_224_in21k': _cfg( url='', # FIXME I have weights for this but > 2GB limit for github release binaries num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
# hybrid models (weights ported from official Google JAX impl) 'vit_base_resnet50_224_in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth', num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=0.9, first_conv='patch_embed.backbone.stem.conv'), 'vit_base_resnet50_384': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth', input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0, first_conv='patch_embed.backbone.stem.conv'),
# hybrid models (my experiments) 'vit_small_resnet26d_224': _cfg(), 'vit_small_resnet50d_s3_224': _cfg(), 'vit_base_resnet26d_224': _cfg(), 'vit_base_resnet50d_224': _cfg(),
# deit models (FB weights) 'vit_deit_tiny_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth'), 'vit_deit_small_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth'), 'vit_deit_base_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth',), 'vit_deit_base_patch16_384': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth', input_size=(3, 384, 384), crop_pct=1.0), 'vit_deit_tiny_distilled_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth'), 'vit_deit_small_distilled_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth'), 'vit_deit_base_distilled_patch16_224': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth', ), 'vit_deit_base_distilled_patch16_384': _cfg( url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth', input_size=(3, 384, 384), crop_pct=1.0),}
4. FFN实现：
class Mlp(nn.Module): def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop)
def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x
5. Attention实现：
在python 3.5以后，@是一个操作符，表示矩阵-向量乘法
A@x 就是矩阵-向量乘法A*x: np.dot(A, x)。
class Attention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
attn = (q @ k.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x)
# x: (B, N, C) return x
6. 包含Attention和Add & Norm的Block实现：
图1：Block类对应结构
不同之处是：
先进行Norm，再Attention；先进行Norm，再通过FFN (MLP)。
class Block(nn.Module): def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x): x = x + self.drop_path(self.attn(self.norm1(x))) x = x + self.drop_path(self.mlp(self.norm2(x))) return x
7. 接下来要把图片转换成Patch，一种做法是直接把Image转化成Patch，另一种做法是把Backbone输出的特征转化成Patch。
1) 直接把Image转化成Patch：
输入的x的维度是：(B, C, H, W)
输出的PatchEmbedding的维度是：(B, 14*14, 768)，768表示embed_dim，14*14表示一共有196个Patches。
class PatchEmbed(nn.Module): """ Image to Patch Embedding """ def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) self.img_size = img_size self.patch_size = patch_size self.num_patches = num_patches
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x): B, C, H, W = x.shape # FIXME look at relaxing size constraints assert H == self.img_size[0] and W == self.img_size[1], \ f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." x = self.proj(x).flatten(2).transpose(1, 2)
# x: (B, 14*14, 768) return x
2) 把Backbone输出的特征转化成Patch：
输入的x的维度是：(B, C, H, W)
得到Backbone输出的维度是：(B, feature_size, feature_size, feature_dim)
输出的PatchEmbedding的维度是：(B, feature_size, feature_size, embed_dim)，一共有feature_size * feature_size个Patches。
class HybridEmbed(nn.Module): """ CNN Feature Map Embedding Extract feature map from CNN, flatten, project to embedding dim. """ def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768): super().__init__() assert isinstance(backbone, nn.Module) img_size = to_2tuple(img_size) self.img_size = img_size self.backbone = backbone if feature_size is None: with torch.no_grad(): # FIXME this is hacky, but most reliable way of determining the exact dim of the output feature # map for all networks, the feature metadata has reliable channel and stride info, but using # stride to calc feature dim requires info about padding of each stage that isn't captured. training = backbone.training if training: backbone.eval() o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1])) if isinstance(o, (list, tuple)): o = o[-1] # last feature if backbone outputs list/tuple of features feature_size = o.shape[-2:] feature_dim = o.shape[1] backbone.train(training) else: feature_size = to_2tuple(feature_size) if hasattr(self.backbone, 'feature_info'): feature_dim = self.backbone.feature_info.channels()[-1] else: feature_dim = self.backbone.num_features self.num_patches = feature_size[0] * feature_size[1] self.proj = nn.Conv2d(feature_dim, embed_dim, 1)
def forward(self, x): x = self.backbone(x) if isinstance(x, (list, tuple)): x = x[-1] # last feature if backbone outputs list/tuple of features x = self.proj(x).flatten(2).transpose(1, 2) return x
8. 以上是ViT所需的所有模块的定义，下面是VisionTransformer 这个类的实现：
8.1 使用这个类时需要传入的变量，其含义已经在本小节一开始介绍。
class VisionTransformer(nn.Module):
""" Vision Transformer
A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` -
https://arxiv.org/abs/2010.11929
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0., hybrid_backbone=None, norm_layer=None):
8.2 得到分块后的Patch的数量：
super().__init__()
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
if hybrid_backbone is not None:
self.patch_embed = HybridEmbed(
hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
else:
self.patch_embed = PatchEmbed(
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
8.3 class token：
一开始定义成(1, 1, 768)，之后再变成(B, 1, 768)。
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
8.4 定义位置编码：
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
8.5 把12个Block连接起来：
self.pos_drop = nn.Dropout(p=drop_rate)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay ruleself.blocks = nn.ModuleList([ Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer) for i in range(depth)])self.norm = norm_layer(embed_dim)
8.6 表示层和分类头：
表示层输出维度是representation_size，分类头输出维度是num_classes。
# Representation layer
if representation_size:
self.num_features = representation_size
self.pre_logits = nn.Sequential(OrderedDict([
('fc', nn.Linear(embed_dim, representation_size)),
('act', nn.Tanh())
]))
else:
self.pre_logits = nn.Identity()
# Classifier head
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
8.7 初始化各个模块：
函数trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.)的目的是用截断的正态分布绘制的值填充输入张量，我们只需要输入均值mean，标准差std，下界a，上界b即可。
self.apply(self._init_weights)表示对各个模块的权重进行初始化。apply函数的代码是：
for module in self.children(): module.apply(fn) fn(self) return self
递归地将fn应用于每个子模块，相当于在递归调用fn，即_init_weights这个函数。
也就是把模型的所有子模块的nn.Linear和nn.LayerNorm层都初始化掉。
trunc_normal_(self.pos_embed, std=.02)trunc_normal_(self.cls_token, std=.02)self.apply(self._init_weights)
def _init_weights(self, m):if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0)elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0)
8.8 最后就是整个ViT模型的forward实现：
def forward_features(self, x): B = x.shape[0] x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks x = torch.cat((cls_tokens, x), dim=1) x = x + self.pos_embed x = self.pos_drop(x)
for blk in self.blocks: x = blk(x)
x = self.norm(x)[:, 0] x = self.pre_logits(x) return x
def forward(self, x): x = self.forward_features(x) x = self.head(x) return x
9. 下面是Training data-efficient image transformers & distillation through attention这篇论文的DeiT这个类的实现：
整体结构与ViT相似，继承了上面的VisionTransformer类。
class DistilledVisionTransformer(VisionTransformer):
再额外定义以下3个变量：
distillation token：dist_token
新的位置编码：pos_embed
蒸馏分类头：head_dist
DeiT相关介绍可以参考：Vision Transformer 超详细解读 (原理分析+代码解读) (三)。
self.dist_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
num_patches = self.patch_embed.num_patches
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 2, self.embed_dim))
self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if self.num_classes > 0 else nn.Identity()
初始化新定义的变量：
trunc_normal_(self.dist_token, std=.02)trunc_normal_(self.pos_embed, std=.02)self.head_dist.apply(self._init_weights)
前向函数：
def forward_features(self, x): B = x.shape[0] x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks dist_token = self.dist_token.expand(B, -1, -1) x = torch.cat((cls_tokens, dist_token, x), dim=1)
x = x + self.pos_embed x = self.pos_drop(x)
for blk in self.blocks: x = blk(x)
x = self.norm(x) return x[:, 0], x[:, 1]
def forward(self, x): x, x_dist = self.forward_features(x) x = self.head(x) x_dist = self.head_dist(x_dist) if self.training: return x, x_dist else: # during inference, return the average of both classifier predictions return (x + x_dist) / 2
10. 对位置编码进行插值：
posemb代表未插值的位置编码权值，posemb_tok为位置编码的token部分，posemb_grid为位置编码的插值部分。
首先把要插值部分posemb_grid给reshape成(1, gs_old, gs_old, -1)的形式，再插值成(1, gs_new, gs_new, -1)的形式，最后与token部分在第1维度拼接在一起，得到插值后的位置编码posemb。
def resize_pos_embed(posemb, posemb_new):
# Rescale the grid of position embeddings when loading from state_dict. Adapted from
# https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
_logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
ntok_new = posemb_new.shape[1]
if True:
posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:]
ntok_new -= 1
else:
posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
gs_old = int(math.sqrt(len(posemb_grid)))
gs_new = int(math.sqrt(ntok_new))
_logger.info('Position embedding grid-size from %s to %s', gs_old, gs_new)
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
posemb_grid = F.interpolate(posemb_grid, size=(gs_new, gs_new), mode='bilinear')
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new * gs_new, -1)
posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
return posemb
11. _create_vision_transformer函数用于创建vision transformer：
checkpoint_filter_fn的作用是加载预训练权重。
def checkpoint_filter_fn(state_dict, model): """ convert patch embedding weight from manual patchify + linear proj to conv""" out_dict = {} if 'model' in state_dict: # For deit models state_dict = state_dict['model'] for k, v in state_dict.items(): if 'patch_embed.proj.weight' in k and len(v.shape) < 4: # For old models that I trained prior to conv based patchification O, I, H, W = model.patch_embed.proj.weight.shape v = v.reshape(O, -1, H, W) elif k == 'pos_embed' and v.shape != model.pos_embed.shape: # To resize pos embedding when using model at different size from pretrained weights v = resize_pos_embed(v, model.pos_embed) out_dict[k] = v return out_dict
def _create_vision_transformer(variant, pretrained=False, distilled=False, **kwargs): default_cfg = default_cfgs[variant] default_num_classes = default_cfg['num_classes'] default_img_size = default_cfg['input_size'][-1]
num_classes = kwargs.pop('num_classes', default_num_classes) img_size = kwargs.pop('img_size', default_img_size) repr_size = kwargs.pop('representation_size', None) if repr_size is not None and num_classes != default_num_classes: # Remove representation layer if fine-tuning. This may not always be the desired action, # but I feel better than doing nothing by default for fine-tuning. Perhaps a better interface? _logger.warning("Removing representation layer for fine-tuning.") repr_size = None
model_cls = DistilledVisionTransformer if distilled else VisionTransformer model = model_cls(img_size=img_size, num_classes=num_classes, representation_size=repr_size, **kwargs) model.default_cfg = default_cfg
if pretrained: load_pretrained( model, num_classes=num_classes, in_chans=kwargs.get('in_chans', 3), filter_fn=partial(checkpoint_filter_fn, model=model)) return model
12. 定义和注册vision transformer模型：
@ 指装饰器。
@register_model代表注册器，注册这个新定义的模型。
model_kwargs是一个存有模型所有超参数的字典。
最后使用上面定义的_create_vision_transformer函数创建模型。
@register_modeldef vit_base_patch16_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer. """ model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs) model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **model_kwargs) return model
一共可以选择的模型包括：
ViT系列：
vit_small_patch16_224
vit_base_patch16_224
vit_base_patch32_224
vit_base_patch16_384
vit_base_patch32_384
vit_large_patch16_224
vit_large_patch32_224
vit_large_patch16_384
vit_large_patch32_384
vit_base_patch16_224_in21k
vit_base_patch32_224_in21k
vit_large_patch16_224_in21k
vit_large_patch32_224_in21k
vit_huge_patch14_224_in21k
vit_base_resnet50_224_in21k
vit_base_resnet50_384
vit_small_resnet26d_224
vit_small_resnet50d_s3_224
vit_base_resnet26d_224
vit_base_resnet50d_224DeiT系列：
vit_deit_tiny_patch16_224
vit_deit_small_patch16_224
vit_deit_base_patch16_224
vit_deit_base_patch16_384
vit_deit_tiny_distilled_patch16_224
vit_deit_small_distilled_patch16_224
vit_deit_base_distilled_patch16_224
vit_deit_base_distilled_patch16_384
以上就是对timm库 vision_transformer.py代码的分析。
如何使用timm库以及 vision_transformer.py代码搭建自己的模型？
在搭建我们自己的视觉Transformer模型时，我们可以按照下面的步骤操作：首先
继承timm库的VisionTransformer这个类。
添加上自己模型独有的一些变量。
重写forward函数。
通过timm库的注册器注册新模型。
我们以ViT模型的改进版DeiT为例：
首先，DeiT的所有模型列表如下：
__all__ = [
'deit_tiny_patch16_224', 'deit_small_patch16_224', 'deit_base_patch16_224',
'deit_tiny_distilled_patch16_224', 'deit_small_distilled_patch16_224',
'deit_base_distilled_patch16_224', 'deit_base_patch16_384',
'deit_base_distilled_patch16_384',
]
导入VisionTransformer这个类，注册器register_model，以及初始化函数trunc_normal_：
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_
DeiT的class名称是DistilledVisionTransformer，它直接继承了VisionTransformer这个类：
class DistilledVisionTransformer(VisionTransformer):
添加上自己模型独有的一些变量：
def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.dist_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim)) num_patches = self.patch_embed.num_patches # 位置编码不是ViT中的(b, N, 256), 而变成了(b, N+2, 256), 原因是还有class token和distillation token. self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 2, self.embed_dim)) self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if self.num_classes > 0 else nn.Identity()
trunc_normal_(self.dist_token, std=.02) trunc_normal_(self.pos_embed, std=.02) self.head_dist.apply(self._init_weights)
重写forward函数：
def forward_features(self, x): # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py # with slight modifications to add the dist_token B = x.shape[0]
x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks dist_token = self.dist_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, dist_token, x), dim=1)
x = x + self.pos_embed x = self.pos_drop(x)
for blk in self.blocks: x = blk(x)
x = self.norm(x)
return x[:, 0], x[:, 1]
def forward(self, x): x, x_dist = self.forward_features(x) x = self.head(x) x_dist = self.head_dist(x_dist) if self.training: return x, x_dist else: # during inference, return the average of both classifier predictions return (x + x_dist) / 2
通过timm库的注册器注册新模型：
@register_modeldef deit_base_patch16_224(pretrained=False, **kwargs): model = VisionTransformer( patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) model.default_cfg = _cfg() if pretrained: checkpoint = torch.hub.load_state_dict_from_url( url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth", map_location="cpu", check_hash=True ) model.load_state_dict(checkpoint["model"]) return model
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