华为提出使用金字塔结构改进Transformer，涨点明显（Pytorch逐行解读）

Transformer在计算机视觉任务方面取得了很大的进展。Transformer-in-Transformer (TNT)体系结构利用内部Transformer和外部Transformer来提取局部和全局表示。在这项工作中，通过引入2种先进的设计来提出新的TNT Baseline：
Pyramid Architecture
Convolutional Stem
新的“PyramidTNT”通过建立层次表示，显著地改进了原来的TNT。PyramidTNT相较于之前最先进的Vision Transformer具有更好的性能，如Swin-Transformer。

1 简介

Vision Transformer为计算机视觉提供了一种新的解决思路。从ViT开始，提出了一系列改进Vision Transformer体系结构的工作。

PVT介绍了Vision Transformer的金字塔网络体系结构
T2T-ViT-14 递归地将相邻的Token聚合为一个Token，以提取局部结构，减少Token的数量
TNT 利用 inner Transformer和outer Transformer来建模 word-level 和 sentence-level 的视觉表示
Swin-Transformer提出了一种分层Transformer，其表示由Shifted windows来进行计算

随着近年来的研究进展，Vision Transformer的性能已经可以优于卷积神经网络(CNN)。而本文的这项工作是建立了基于TNT框架的改进的 Vision Transformer Baseline。这里主要引入了两个主要的架构修改：

Pyramid Architecture：逐渐降低分辨率，提取多尺度表示
Convolutional Stem：修补Stem和稳定训练

这里作者还使用了几个其他技巧来进一步提高效率。新的Transformer被命名为PyramidTNT。

对图像分类和目标检测的实验证明了金字塔检测的优越性。具体来说，PyramidTNT-S在只有3.3B FLOPs的情况下获得了82.0%的ImageNet分类准确率，明显优于原来的TNT-S和Swin-T。

对于COCO检测，PyramidTNT-S比现有的Transformer和MLP检测模型以更少的计算成本实现42.0的mAP。

2 本文方法

2.1 Convolutional Stem

class Stem(nn.Module):
    """ 
    Image to Visual Word Embedding
    """
    def __init__(self, img_size=224, in_chans=3, outer_dim=768, inner_dim=24):
        super().__init__()
        img_size = to_2tuple(img_size)
        self.img_size = img_size
        self.inner_dim = inner_dim
        self.num_patches = img_size[0] // 8 * img_size[1] // 8
        self.num_words = 16
        
        self.common_conv = nn.Sequential(
            nn.Conv2d(in_chans, inner_dim*2, 3, stride=2, padding=1),
            nn.BatchNorm2d(inner_dim*2),
            nn.ReLU(inplace=True),
        )
        # 利用 inner Transformer来建模 word-level
        self.inner_convs = nn.Sequential(
            nn.Conv2d(inner_dim*2, inner_dim, 3, stride=1, padding=1),
            nn.BatchNorm2d(inner_dim),
            nn.ReLU(inplace=False),
        )
        # 利用outer Transformer来建模 sentence-level 的视觉表示
        self.outer_convs = nn.Sequential(
            nn.Conv2d(inner_dim*2, inner_dim*4, 3, stride=2, padding=1),
            nn.BatchNorm2d(inner_dim*4),
            nn.ReLU(inplace=True),
            nn.Conv2d(inner_dim*4, inner_dim*8, 3, stride=2, padding=1),
            nn.BatchNorm2d(inner_dim*8),
            nn.ReLU(inplace=True),
            nn.Conv2d(inner_dim*8, outer_dim, 3, stride=1, padding=1),
            nn.BatchNorm2d(outer_dim),
            nn.ReLU(inplace=False),
        )
        
        self.unfold = nn.Unfold(kernel_size=4, padding=0, stride=4)

    def forward(self, x):
        B, C, H, W = x.shape
        H_out, W_out = H // 8, W // 8
        H_in, W_in = 4, 4
        x = self.common_conv(x)
        # inner_tokens建模word level表征
        inner_tokens = self.inner_convs(x) # B, C, H, W
        inner_tokens = self.unfold(inner_tokens).transpose(1, 2) # B, N, Ck2
        inner_tokens = inner_tokens.reshape(B * H_out * W_out, self.inner_dim, H_in*W_in).transpose(1, 2) # B*N, C, 4*4
        # outer_tokens建模 sentence level表征
        outer_tokens = self.outer_convs(x) # B, C, H_out, W_out
        outer_tokens = outer_tokens.permute(0, 2, 3, 1).reshape(B, H_out * W_out, -1)
        return inner_tokens, outer_tokens, (H_out, W_out), (H_in, W_in)

2.2 Pyramid Architecture

原始的TNT网络在继ViT之后的每个块中保持相同数量的token。visual words和visual sentences的数量从下到上保持不变。

本文受PVT的启发，为TNT构建了4个不同数量的Token阶段，如图1(b)。所示在这4个阶段中，visual words的空间形状分别设置为H/2×W/2、H/4×W/4、H/8×W/8、H/16×W/16；visual sentences的空间形状分别设置为H/8×W/8、H/16×W/16、H/32×W/32、H/64×W/64。下采样操作是通过stride=2的卷积来实现的。每个阶段由几个TNT块组成，TNT块在word-level 和 sentence-level特征上操作。最后，利用全局平均池化操作，将输出的visual sentences融合成一个向量作为图像表示。

class SentenceAggregation(nn.Module):
    """ 
    Sentence Aggregation
    """
    def __init__(self, dim_in, dim_out, stride=2, act_layer=nn.GELU):
        super().__init__()
        self.stride = stride
        self.norm = nn.LayerNorm(dim_in)
        self.conv = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, kernel_size=2*stride-1, padding=stride-1, stride=stride),
        )
        
    def forward(self, x, H, W):
        B, N, C = x.shape # B, N, C
        x = self.norm(x)
        x = x.transpose(1, 2).reshape(B, C, H, W)
        x = self.conv(x)
        H, W = math.ceil(H / self.stride), math.ceil(W / self.stride)
        x = x.reshape(B, -1, H * W).transpose(1, 2)
        return x, H, W


class WordAggregation(nn.Module):
    """ 
    Word Aggregation
    """
    def __init__(self, dim_in, dim_out, stride=2, act_layer=nn.GELU):
        super().__init__()
        self.stride = stride
        self.dim_out = dim_out
        self.norm = nn.LayerNorm(dim_in)
        self.conv = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, kernel_size=2*stride-1, padding=stride-1, stride=stride),
        )

    def forward(self, x, H_out, W_out, H_in, W_in):
        B_N, M, C = x.shape # B*N, M, C
        x = self.norm(x)
        x = x.reshape(-1, H_out, W_out, H_in, W_in, C)
        
        # padding to fit (1333, 800) in detection.
        pad_input = (H_out % 2 == 1) or (W_out % 2 == 1)
        if pad_input:
            x = F.pad(x.permute(0, 3, 4, 5, 1, 2), (0, W_out % 2, 0, H_out % 2))
            x = x.permute(0, 4, 5, 1, 2, 3)            
        # patch merge
        x1 = x[:, 0::2, 0::2, :, :, :]  # B, H/2, W/2, H_in, W_in, C
        x2 = x[:, 1::2, 0::2, :, :, :]
        x3 = x[:, 0::2, 1::2, :, :, :]
        x4 = x[:, 1::2, 1::2, :, :, :]
        x = torch.cat([torch.cat([x1, x2], 3), torch.cat([x3, x4], 3)], 4) # B, H/2, W/2, 2*H_in, 2*W_in, C
        x = x.reshape(-1, 2*H_in, 2*W_in, C).permute(0, 3, 1, 2) # B_N/4, C, 2*H_in, 2*W_in
        x = self.conv(x)  # B_N/4, C, H_in, W_in
        x = x.reshape(-1, self.dim_out, M).transpose(1, 2)
        return x
    

class Stage(nn.Module):
    """ 
    PyramidTNT stage
    """
    def __init__(self, num_blocks, outer_dim, inner_dim, outer_head, inner_head, num_patches, num_words, 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, se=0, sr_ratio=1):
        super().__init__()
        blocks = []
        drop_path = drop_path if isinstance(drop_path, list) else [drop_path] * num_blocks
        
        for j in range(num_blocks):
            if j == 0:
                _inner_dim = inner_dim
            elif j == 1 and num_blocks > 6:
                _inner_dim = inner_dim
            else:
                _inner_dim = -1
            blocks.append(Block(
                outer_dim, _inner_dim, outer_head=outer_head, inner_head=inner_head,
                num_words=num_words, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop,
                attn_drop=attn_drop, drop_path=drop_path[j], act_layer=act_layer, norm_layer=norm_layer,
                se=se, sr_ratio=sr_ratio))

        self.blocks = nn.ModuleList(blocks)
        self.relative_pos = nn.Parameter(torch.randn(1, outer_head, num_patches, num_patches // sr_ratio // sr_ratio))

    def forward(self, inner_tokens, outer_tokens, H_out, W_out, H_in, W_in):
        for blk in self.blocks:
            inner_tokens, outer_tokens = blk(inner_tokens, outer_tokens, H_out, W_out, H_in, W_in, self.relative_pos)
        return inner_tokens, outer_tokens
    
    
class PyramidTNT(nn.Module):
    """ 
    PyramidTNT 
    """
    def __init__(self, configs=None, img_size=224, in_chans=3, num_classes=1000, mlp_ratio=4., qkv_bias=False,
                qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm, se=0):
        super().__init__()
        self.num_classes = num_classes
        depths = configs['depths']
        outer_dims = configs['outer_dims']
        inner_dims = configs['inner_dims']
        outer_heads = configs['outer_heads']
        inner_heads = configs['inner_heads']
        sr_ratios = [4, 2, 1, 1]
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule 
        self.num_features = outer_dims[-1]  # num_features for consistency with other models       

        self.patch_embed = Stem(
            img_size=img_size, in_chans=in_chans, outer_dim=outer_dims[0], inner_dim=inner_dims[0])
        num_patches = self.patch_embed.num_patches
        num_words = self.patch_embed.num_words
        
        self.outer_pos = nn.Parameter(torch.zeros(1, num_patches, outer_dims[0]))
        self.inner_pos = nn.Parameter(torch.zeros(1, num_words, inner_dims[0]))
        self.pos_drop = nn.Dropout(p=drop_rate)

        depth = 0
        self.word_merges = nn.ModuleList([])
        self.sentence_merges = nn.ModuleList([])
        self.stages = nn.ModuleList([])
        # 搭建PyramidTNT所需要的4个Stage
        for i in range(4):
            if i > 0:
                self.word_merges.append(WordAggregation(inner_dims[i-1], inner_dims[i], stride=2))
                self.sentence_merges.append(SentenceAggregation(outer_dims[i-1], outer_dims[i], stride=2))
            self.stages.append(Stage(depths[i], outer_dim=outer_dims[i], inner_dim=inner_dims[i],
                        outer_head=outer_heads[i], inner_head=inner_heads[i],
                        num_patches=num_patches // (2 ** i) // (2 ** i), num_words=num_words, mlp_ratio=mlp_ratio,
                        qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate,
                        drop_path=dpr[depth:depth+depths[i]], norm_layer=norm_layer, se=se, sr_ratio=sr_ratios[i])
            )
            depth += depths[i]
        
        self.norm = norm_layer(outer_dims[-1])

        # Classifier head
        self.head = nn.Linear(outer_dims[-1], num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        inner_tokens, outer_tokens, (H_out, W_out), (H_in, W_in) = self.patch_embed(x)
        inner_tokens = inner_tokens + self.inner_pos # B*N, 8*8, C
        outer_tokens = outer_tokens + self.pos_drop(self.outer_pos)  # B, N, D
        
        for i in range(4):
            if i > 0:
                inner_tokens = self.word_merges[i-1](inner_tokens, H_out, W_out, H_in, W_in)
                outer_tokens, H_out, W_out = self.sentence_merges[i-1](outer_tokens, H_out, W_out)
            inner_tokens, outer_tokens = self.stages[i](inner_tokens, outer_tokens, H_out, W_out, H_in, W_in)
        
        outer_tokens = self.norm(outer_tokens)
        return outer_tokens.mean(dim=1)

    def forward(self, x):
        # 特征提取层，可以作为Backbone用到下游任务
        x = self.forward_features(x)
        # 分类层
        x = self.head(x)
        return x

2.3 其他的Tricks

除了修改网络体系结构外，还采用了几种Vision Transformer的高级技巧。

在自注意力模块上添加相对位置编码，以更好地表示Token之间的相对位置。
前两个阶段利用Linear spatial reduction attention(LSRA)来降低长序列自注意力的计算复杂度。

3 实验

3.1 分类

表3显示了ImageNet-1K分类结果。与原来的TNT相比，PyramidTNT实现了更好的图像分类精度。例如，与TNT-S相比，使用少1.9B的TNT-S的Top-1精度高0.5%。这里还将PyramidTNT与其他具有代表性的CNN、MLP和基于Transformer的模型进行了比较。从结果中可以看到PyramidTNT是最先进的Vision Transformer。

3.2 目标检测

表4报告了“1x”训练计划下的目标检测和实例分割的结果。PyramidTNT-S在One-Stage和Two-Stage检测器上都显著优于其他Backbone，且计算成本相似。例如，基于PyramidTNT-S的RetinaNet达到了42.0 AP和57.7AP-L，分别高出使用Swin-Transformer的模型0.5AP和2.2APL。

这些结果表明，PyramidTNT体系结构可以更好地捕获大型物体的全局信息。金字塔的简单的上采样策略和较小的空间形状使AP-S从一个大规模的推广。

3.3 实例分割

PyramidTNT-S在Mask R-CNN和Cascade Mask R-CNN上的AP-m可以获得更好的AP-b和AP-m，显示出更好的特征表示能力。例如，在ParamidTNN约束上，MaskR-CNN-S超过Hire-MLPS 的0.9AP-b。

4 参考
[1].PyramidTNT:Improved Transformer-in-Transformer Baselines with Pyramid Architecture

原文：集智书童
作者：ChaucerG

推荐阅读

更多嵌入式AI技术相关内容请关注嵌入式AI专栏。