深度学习之图像分类(十八)– Vision Transformer(ViT)网络详解

深度学习之图像分类(十八)Vision Transformer(ViT)网络详解

目录

*
深度学习之图像分类(十八)Vision Transformer(ViT)网络详解

+ 1. 前言
+ 2. ViT 模型架构
+
* 2.1 Embedding 层
* 2.2 Transformer Encoder 层
* 2.3 MLP Head 层
* 2.4 ViT B/16
* 2.5 ViT 模型参数
+ 3. Hybrid 混合模型
+ 4. 代码

上节有讲 Transformer 中的 Self-Attention 结构。本节学习 Vision Transformer(vit) 详解。学习视频源于 Bilibili,参考博客 Vision Transformer详解

深度学习之图像分类(十八)-- Vision Transformer(ViT)网络详解

; 1. 前言

ViT 其原始论文为 An Image Is Worth 16×16 Words: Transformers For Image Recognition At Scale。首先看一下 ViT 模型的效果,在 ImageNet 1k 上最高能达到 88.55 的准确率,关键是现在自家的数据集上进行了预训练,三亿数据量啊。

深度学习之图像分类(十八)-- Vision Transformer(ViT)网络详解

; 2. ViT 模型架构

其原始架构图如下所示,可以看到首先输入图片分为很多 patch,论文中为 16。将 patch 输入一个 Linear Projection of Flattened Patches 这个 Embedding 层,就会得到一个个向量,通常就称作 token。紧接着在一系列 token 的前面加上加上一个新的 token(类别token,有点像输入给 Transformer Decoder 的 START,就是对应着 * 那个位置),此外还需要加上位置的信息,对应着 0~9。然后输入到 Transformer Encoder 中,对应着右边的图,将 block 重复堆叠 L 次。Transformer Encoder 有多少个输入就有多少个输出。最后只进行分类,所以将 class 位置对应的输出输入 MLP Head 进行预测分类输出。

深度学习之图像分类(十八)-- Vision Transformer(ViT)网络详解

2.1 Embedding 层

接下来我们对每个模块进行细讲,首先是 Embedding 层。对于标准的 Transformer 模块,要求的输入是 token 向量的序列,即二维矩阵 [num_token, token_dim]。在具体的代码实现过程中呢,我们实际是通过一个卷积层来实现以 ViT-B/16 为例,使用卷积核大小为 16 × 16 16 \times 16 1 6 ×1 6, stride 为 16,卷积核个数为 768 来实现的,即 [224,224,3] –> [14,14,768] –> [196, 768]。即一共 196 个token,每个 token 向量长度为 768。此外我们还需要加上一个类别的 token,为此我们实际上是初始化了一个可训练的参数 [1, 768],将其与 token 序列进行拼接得到 Cat([1, 768], [196,768]) –> [197, 768]。然后再叠加上位置编码 Position Embedding: [197,768] –> [197, 768]。

深度学习之图像分类(十八)-- Vision Transformer(ViT)网络详解

我们再详细考虑下 Position Embedding,如果不是用 Position Embedding 得到的结果是 0.61382,使用一维的位置编码得到的结果是 0.64206,明显比不使用位置编码高了三个百分点。使用 2D 以及相对位置编码其实和 1D 差不多啊。论文中也提到说 the difference in how to encoder spatial information is less important,即位置编码的差异其实不是特别重要。1D 的话,简单效果好参数少,所以默认使用 1D 的位置编码。

深度学习之图像分类(十八)-- Vision Transformer(ViT)网络详解

论文中有给这样一个图,我们训练得到的位置编码与其他位置编码之间的余弦相似度。这里的 patches 大小是 32 × 32 32 \times 32 3 2 ×3 2 的,224 / 32 = 7 224/32=7 2 2 4 /3 2 =7,所以这里的大小是 7 × 7 7 \times 7 7 ×7。这张图怎么理解呢?我们会在每个 token 上叠加一个位置编码,中间那个图的 49 个小图中,每个小图其实也是 7 × 7 7 \times 7 7 ×7 的。左上角第一行第一个 patch 的位置编码与自己的位置编码是一样的,所以余弦相似度是1,所以左上角是黄色。然后在与其他位置编码进行计算。就得到了左上角的小图。其他的也都是类似的规律。注意, 这个是学出来的

深度学习之图像分类(十八)-- Vision Transformer(ViT)网络详解

; 2.2 Transformer Encoder 层

Transformer Encoder 就是将 Encoder Block 重复堆叠 L 次。我们来看看单个 Encoder Block。首先输入一个 Norm 层,这里的 Norm 指的是 Layer Normalization 层(有论文比较了 BN 在 transformer 中为什么不好,不如 LN |这里先 Norm 再 Multihead Attention 也是有论文研究的,原始的 Transformer 先 Attention 再 Norm,此外这个先 Norm 再操作和 DenseNet 的先 BN 再 Conv 异曲同工)。经过 LN 后经过 Multi-Head Attention,然后源码经过 Dropout 层,有些复现大神使用的是 DropPath 方法,根据以往的经验可能使用后者会更好一点。然后残差之后经过 LN,MLP Block,Dropout/DropPath 之后残差即可。

MLP Block 其实也很简单,就是一个全连接,GELU 激活函数,Dropout,全连接,Dropout。需要注意第一个全连接层的节点个数是输入向量长度的 4 倍,第二个全连接层会还原会原来的大小。

深度学习之图像分类(十八)-- Vision Transformer(ViT)网络详解

有一个地方要注意,看源码才知道,在 Transformer Encoder 前有个 Dropout 层,在之后有一个 Layer Norm 层,这些在图中还没有画出来的。 在 Transformer Encoder 前有个 Dropout 层,对此我的理解是在原图上随机加 Mask 遮挡,然后依然要进行分类

2.3 MLP Head 层

在训练 ImageNet21K 时候是由 Linear + tanh 激活函数 + Linear 构成的。但是迁移到 ImageNet1k 之后或者做迁移学习时,其实只需要一个 Linear 就足够了。(获得类别概率需要一个 softmax)

深度学习之图像分类(十八)-- Vision Transformer(ViT)网络详解

; 2.4 ViT B/16

我们来从头梳理一次 ViT B/16 的结构,假设输入图为 224 × 224 × 3 224 \times 224 \times 3 2 2 4 ×2 2 4 ×3,首先经过一个卷积层,然后进行高度和宽度方向的展平处理。紧接着 concat 一个 class token,再加上 Position Embedding 的相加操作,这里的 Position Embedding 也是可训练的参数。经过 Dropout 之后输入 12 个堆叠的 Encoder Block。Encoder 输出经过 LN 得到的输出为 197 × 768 197 \times 768 1 9 7 ×7 6 8,即是不变的。然后我们提取第一个 class token 对应的输出,切片之后即变成了 1 × 768 1 \times 768 1 ×7 6 8,将其输入 MLP Head 中。如果在 ImageNet21K 预训练的时候,Pre-Logits 就是一个全连接层,tanh 激活函数。如果是在 ImageNet1k 或者自己的数据集上的时候训练的时候,可以不要这个 Pre-Logits。

深度学习之图像分类(十八)-- Vision Transformer(ViT)网络详解

2.5 ViT 模型参数

我们来看看论文给出的 ViT 模型的参数。ViT B 对应的就是 ViT-Base,ViT L 对应的是 ViT-Large,ViT H 对应的是 ViT-Huge。patch size 是图片切片大小(源码中还有 32 × 32 32 \times 32 3 2 ×3 2 的);layers 则是 encoder block 堆叠的次数;Hidden size 是 token 向量的长度;MLP size 是 Hidden size 的四倍,即 Encoder block 中 MLP block 第一个全连接层节点个数;Heads 则是 Multi-head Attention 中 heads 的个数。

深度学习之图像分类(十八)-- Vision Transformer(ViT)网络详解

; 3. Hybrid 混合模型

我们来看看 CNN 和 Transformer 的混合模型。首先用传统的神经网络 backbone 来提取特征,然后再通过 ViT 模型进一步得到最终的结果。这里的特征提取部分采用的是 ResNet50 网络,但是和原来的有所不同,第一点是采用 stdConv2d,第二点则是使用GN而非BN,第三点是将 stage4 中的 3 个 block 移动到 stage3 中。R50 backbone 的输出为 14 × 14 × 1024 14 \times 14 \times 1024 1 4 ×1 4 ×1 0 2 4,然后通过 1 × 1 1 \times 1 1 ×1 卷积变为 14 × 14 × 768 14 \times 14 \times 768 1 4 ×1 4 ×7 6 8,然后进行展平处理就得到 token 了。之后就是和 ViT 一摸一样的了。

深度学习之图像分类(十八)-- Vision Transformer(ViT)网络详解

结果可见,混合模型比纯 transformer 模型的效果会好一些,这也是迁移学习之后的结果。在少量微调中混合模型占有,但是随着迭代次数的上升,纯 transformer 也能达到混合模型的效果,例如 14 个 epoches 时 ViT-L/16 和 Res50x1+ViT-L/16 就基本一样了。

深度学习之图像分类(十八)-- Vision Transformer(ViT)网络详解

4. 代码

代码出处见 此处

"""
original code from rwightman:
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
from functools import partial
from collections import OrderedDict

import torch
import torch.nn as nn

def drop_path(x, drop_prob: float = 0., training: bool = False):
"""
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...

    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
    'survival rate' as the argument.

"""
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()
    output = x.div(keep_prob) * random_tensor
    return output

class DropPath(nn.Module):
"""
    Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).

"""
    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)

class PatchEmbed(nn.Module):
"""
    2D Image to Patch Embedding
"""
    def __init__(self, img_size=224, patch_size=16, in_c=3, embed_dim=768, norm_layer=None):
        super().__init__()
        img_size = (img_size, img_size)
        patch_size = (patch_size, patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.num_patches = self.grid_size[0] * self.grid_size[1]

        self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=patch_size, stride=patch_size)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        B, C, H, W = x.shape
        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 = self.norm(x)
        return x

class Attention(nn.Module):
    def __init__(self,
                 dim,
                 num_heads=8,
                 qkv_bias=False,
                 qk_scale=None,
                 attn_drop_ratio=0.,
                 proj_drop_ratio=0.):
        super(Attention, self).__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        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_ratio)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop_ratio)

    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]

        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)
        return x

class Mlp(nn.Module):
"""
    MLP as used in Vision Transformer, MLP-Mixer and related networks
"""
    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

class Block(nn.Module):
    def __init__(self,
                 dim,
                 num_heads,
                 mlp_ratio=4.,
                 qkv_bias=False,
                 qk_scale=None,
                 drop_ratio=0.,
                 attn_drop_ratio=0.,
                 drop_path_ratio=0.,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm):
        super(Block, self).__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
                              attn_drop_ratio=attn_drop_ratio, proj_drop_ratio=drop_ratio)

        self.drop_path = DropPath(drop_path_ratio) if drop_path_ratio > 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_ratio)

    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

class VisionTransformer(nn.Module):
    def __init__(self, img_size=224, patch_size=16, in_c=3, num_classes=1000,
                 embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.0, qkv_bias=True,
                 qk_scale=None, representation_size=None, distilled=False, drop_ratio=0.,
                 attn_drop_ratio=0., drop_path_ratio=0., embed_layer=PatchEmbed, norm_layer=None,
                 act_layer=None):
"""
        Args:
            img_size (int, tuple): input image size
            patch_size (int, tuple): patch size
            in_c (int): number of input channels
            num_classes (int): number of classes for classification head
            embed_dim (int): embedding dimension
            depth (int): depth of transformer
            num_heads (int): number of attention heads
            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
            qkv_bias (bool): enable bias for qkv if True
            qk_scale (float): override default qk scale of head_dim ** -0.5 if set
            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
            distilled (bool): model includes a distillation token and head as in DeiT models
            drop_ratio (float): dropout rate
            attn_drop_ratio (float): attention dropout rate
            drop_path_ratio (float): stochastic depth rate
            embed_layer (nn.Module): patch embedding layer
            norm_layer: (nn.Module): normalization layer
"""
        super(VisionTransformer, self).__init__()
        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim
        self.num_tokens = 2 if distilled else 1
        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
        act_layer = act_layer or nn.GELU

        self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_c=in_c, embed_dim=embed_dim)
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_ratio)

        dpr = [x.item() for x in torch.linspace(0, drop_path_ratio, depth)]
        self.blocks = nn.Sequential(*[
            Block(dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
                  drop_ratio=drop_ratio, attn_drop_ratio=attn_drop_ratio, drop_path_ratio=dpr[i],
                  norm_layer=norm_layer, act_layer=act_layer)
            for i in range(depth)
        ])
        self.norm = norm_layer(embed_dim)

        if representation_size and not distilled:
            self.has_logits = True
            self.num_features = representation_size
            self.pre_logits = nn.Sequential(OrderedDict([
                ("fc", nn.Linear(embed_dim, representation_size)),
                ("act", nn.Tanh())
            ]))
        else:
            self.has_logits = False
            self.pre_logits = nn.Identity()

        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
        self.head_dist = None
        if distilled:
            self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()

        nn.init.trunc_normal_(self.pos_embed, std=0.02)
        if self.dist_token is not None:
            nn.init.trunc_normal_(self.dist_token, std=0.02)

        nn.init.trunc_normal_(self.cls_token, std=0.02)
        self.apply(_init_vit_weights)

    def forward_features(self, x):

        x = self.patch_embed(x)

        cls_token = self.cls_token.expand(x.shape[0], -1, -1)
        if self.dist_token is None:
            x = torch.cat((cls_token, x), dim=1)
        else:
            x = torch.cat((cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1)

        x = self.pos_drop(x + self.pos_embed)
        x = self.blocks(x)
        x = self.norm(x)
        if self.dist_token is None:
            return self.pre_logits(x[:, 0])
        else:
            return x[:, 0], x[:, 1]

    def forward(self, x):
        x = self.forward_features(x)
        if self.head_dist is not None:
            x, x_dist = self.head(x[0]), self.head_dist(x[1])
            if self.training and not torch.jit.is_scripting():

                return x, x_dist
            else:
                return (x + x_dist) / 2
        else:
            x = self.head(x)
        return x

def _init_vit_weights(m):
"""
    ViT weight initialization
    :param m: module
"""
    if isinstance(m, nn.Linear):
        nn.init.trunc_normal_(m.weight, std=.01)
        if m.bias is not None:
            nn.init.zeros_(m.bias)
    elif isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode="fan_out")
        if m.bias is not None:
            nn.init.zeros_(m.bias)
    elif isinstance(m, nn.LayerNorm):
        nn.init.zeros_(m.bias)
        nn.init.ones_(m.weight)

def vit_base_patch16_224_in21k(num_classes: int = 21843, has_logits: bool = True):
"""
    ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).

    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.

    weights ported from official Google JAX impl:
    https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch16_224_in21k-e5005f0a.pth
"""
    model = VisionTransformer(img_size=224,
                              patch_size=16,
                              embed_dim=768,
                              depth=12,
                              num_heads=12,
                              representation_size=768 if has_logits else None,
                              num_classes=num_classes)
    return model

def vit_base_patch32_224_in21k(num_classes: int = 21843, has_logits: bool = True):
"""
    ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).

    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.

    weights ported from official Google JAX impl:
    https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch32_224_in21k-8db57226.pth
"""
    model = VisionTransformer(img_size=224,
                              patch_size=32,
                              embed_dim=768,
                              depth=12,
                              num_heads=12,
                              representation_size=768 if has_logits else None,
                              num_classes=num_classes)
    return model

def vit_large_patch16_224_in21k(num_classes: int = 21843, has_logits: bool = True):
"""
    ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).

    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.

    weights ported from official Google JAX impl:
    https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch16_224_in21k-606da67d.pth
"""
    model = VisionTransformer(img_size=224,
                              patch_size=16,
                              embed_dim=1024,
                              depth=24,
                              num_heads=16,
                              representation_size=1024 if has_logits else None,
                              num_classes=num_classes)
    return model

def vit_large_patch32_224_in21k(num_classes: int = 21843, has_logits: bool = True):
"""
    ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).

    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.

    weights ported from official Google JAX impl:
    https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth
"""
    model = VisionTransformer(img_size=224,
                              patch_size=32,
                              embed_dim=1024,
                              depth=24,
                              num_heads=16,
                              representation_size=1024 if has_logits else None,
                              num_classes=num_classes)
    return model

def vit_huge_patch14_224_in21k(num_classes: int = 21843, has_logits: bool = True):
"""
    ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).

    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.

    NOTE: converted weights not currently available, too large for github release hosting.

"""
    model = VisionTransformer(img_size=224,
                              patch_size=14,
                              embed_dim=1280,
                              depth=32,
                              num_heads=16,
                              representation_size=1280 if has_logits else None,
                              num_classes=num_classes)
    return model

Original: https://blog.csdn.net/baidu_36913330/article/details/120198840
Author: 木卯_THU
Title: 深度学习之图像分类(十八)– Vision Transformer(ViT)网络详解

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