【实战干货】用Pytorch轻松实现28个视觉Transformer（附代码解读） 点击上方，选择星标或置顶，不定期资源大放送！ 阅读大概需要15分钟 Follow小博主，每天更新前沿干货 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 modelslayersutilitiesoptimizersschedulersdata-loaders / augmentationstraining / 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)fusednameoptimizers 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 mathimport loggingfrom functools import partialfrom collections import OrderedDict import torchimport torch.nn as nnimport torch.nn.functional as F from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STDfrom .helpers import load_pretrainedfrom .layers import StdConv2dSame, DropPath, to_2tuple, trunc_normal_from .resnet import resnet26d, resnet50dfrom .resnetv2 import ResNetV2from.registryimportregister_model2. 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)returnx5. 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 x6. 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 x7. 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)returnx2) 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 x8. 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_classesself.num_features = self.embed_dim = embed_dim # num_features for consistency with other modelsnorm_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_patches8.3 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 layerif 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 headself.head=nn.Linear(self.num_features,num_classes)ifnum_classes0elsenn.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)returnx9. 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_distDeiT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_patchesself.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 2, self.embed_dim))self.head_dist=nn.Linear(self.embed_dim,self.num_classes)ifself.num_classes0elsenn.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) / 210. 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)returnposemb11. _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 model12. 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_224 DeiT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, _cfgfrom timm.models.registry import register_modelfromtimm.models.layersimporttrunc_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 重磅！DLer-计算机视觉交流3群已成立！ 大家好，这是DLer-计算机视觉微信交流3群！欢迎各位Cver加入DLer-计算机视觉微信交流大家庭。 本群旨在学习交流图像分类、目标检测、目标跟踪、点云与语义分割、GAN、超分辨率、人脸检测与识别、动作行为与时空运动、模型压缩和量化剪枝、迁移学习、人体姿态估计等内容。希望能给大家提供一个更精准的研讨交流平台！！！ 进群请备注：研究方向+学校/公司+昵称（如图像分类+上交+小明） ??长按识别添加，即可进群！ 喜欢您就点个在看！