Shunted Transformer 飞桨权重迁移体验

转自AI Studio，原文链接：Shunted Transformer 飞桨权重迁移体验 - 飞桨AI StudioShunted Self-Attention via Multi-Scale Token Aggregationpaper:Shunted Self-Attention via Multi-Scale Token Aggregationgithub:https://github.c

AI Studio

936人浏览 · 2022-04-22 00:23:08

AI Studio · 2022-04-22 00:23:08 发布

转自AI Studio，原文链接：Shunted Transformer 飞桨权重迁移体验 - 飞桨AI Studio

Shunted Self-Attention via Multi-Scale Token Aggregation

paper:Shunted Self-Attention via Multi-Scale Token Aggregation
github:https://github.com/OliverRensu/Shunted-Transformer

ViT模型在设计时有个特点：在相同的层中每个token的感受野相同。这限制了self-attention层捕获多尺度特征的能力，从而导致处理多尺度目标的图片时性能下降。针对这个问题，作者提出了shunted self-attention，使得每个attention层可以获取多尺度信息。
本项目使用PaddleClas实现Shunt Transformer组网，并且将官方提供的pytorch权重转换为PaddlePaddle权重，在ImageNet-1k 验证集测试其精度。

一、Shunted Self-Attention

本篇论文的核心是提出了Shunted Self-Attention，几种不同的ViT模块对比如下：

ViT: QKV维度相同，可以得到全局感受野但是计算量大
Swin：划分window，self-attention在窗口内计算减少计算量，同时引入shift操作使得感受野增加
PVT：降低KV的patch数量来降低计算量
shunted Self-Attention:在单个attention层计算时得到多尺度KV，再计算Self-Attention

计算过程如下：

上式中，i表示KV尺度的个数，MTA（multi-scale token aggregation）表示下采样率为ri的特征聚合模块（通过带步长的卷积实现），LE是深度可分离卷积层，用来增强V中相邻像素的联系。

实现代码：

class Attention(nn.Layer):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."

        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5
        self.q = nn.Linear(dim, dim, bias_attr=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.act = nn.GELU()
            if sr_ratio==8:
                self.sr1 = nn.Conv2D(dim, dim, kernel_size=8, stride=8)
                self.norm1 = nn.LayerNorm(dim)
                self.sr2 = nn.Conv2D(dim, dim, kernel_size=4, stride=4)
                self.norm2 = nn.LayerNorm(dim)
            if sr_ratio==4:
                self.sr1 = nn.Conv2D(dim, dim, kernel_size=4, stride=4)
                self.norm1 = nn.LayerNorm(dim)
                self.sr2 = nn.Conv2D(dim, dim, kernel_size=2, stride=2)
                self.norm2 = nn.LayerNorm(dim)
            if sr_ratio==2:
                self.sr1 = nn.Conv2D(dim, dim, kernel_size=2, stride=2)
                self.norm1 = nn.LayerNorm(dim)
                self.sr2 = nn.Conv2D(dim, dim, kernel_size=1, stride=1)
                self.norm2 = nn.LayerNorm(dim)
            self.kv1 = nn.Linear(dim, dim, bias_attr=qkv_bias)
            self.kv2 = nn.Linear(dim, dim, bias_attr=qkv_bias)
            self.local_conv1 = nn.Conv2D(dim//2, dim//2, kernel_size=3, padding=1, stride=1, groups=dim//2)
            self.local_conv2 = nn.Conv2D(dim//2, dim//2, kernel_size=3, padding=1, stride=1, groups=dim//2)
        else:
            self.kv = nn.Linear(dim, dim * 2, bias_attr=qkv_bias)
            self.local_conv = nn.Conv2D(dim, dim, kernel_size=3, padding=1, stride=1, groups=dim)
        self.apply(self._init_weights)

    def forward(self, x, H, W):
        B, N, C = x.shape
        q = self.q(x).reshape([B, N, self.num_heads, C // self.num_heads]).transpose([0, 2, 1, 3])
        if self.sr_ratio > 1:
                x_ = x.transpose([0, 2, 1]).reshape([B, C, H, W])
                x_1 = self.act(self.norm1(self.sr1(x_).reshape([B, C, -1]).transpose([0, 2, 1])))
                x_2 = self.act(self.norm2(self.sr2(x_).reshape([B, C, -1]).transpose([0, 2, 1])))
                kv1 = self.kv1(x_1).reshape([B, -1, 2, self.num_heads//2, C // self.num_heads]).transpose([2, 0, 3, 1, 4])
                kv2 = self.kv2(x_2).reshape([B, -1, 2, self.num_heads//2, C // self.num_heads]).transpose([2, 0, 3, 1, 4])
                k1, v1 = kv1[0], kv1[1] #B head N C
                k2, v2 = kv2[0], kv2[1]
                attn1 = (q[:, :self.num_heads//2] @ k1.transpose([0, 1, 3, 2])) * self.scale
                attn1 = F.softmax(attn1, axis=-1)
                attn1 = self.attn_drop(attn1)
                v1 = v1 + self.local_conv1(v1.transpose([0, 2, 1, 3]).reshape([B, -1, C//2]).
                                        transpose([0, 2, 1]).reshape([B,C//2, H//self.sr_ratio, W//self.sr_ratio])).\
                    reshape([B, C//2, -1]).reshape([B, self.num_heads//2, C // self.num_heads, -1]).transpose([0, 1, 3, 2])
                x1 = (attn1 @ v1).transpose([0, 2, 1, 3]).reshape([B, N, C//2])
                attn2 = (q[:, self.num_heads // 2:] @ k2.transpose([0, 1, 3, 2])) * self.scale
                attn2 = F.softmax(attn2, axis=-1)
                attn2 = self.attn_drop(attn2)
                v2 = v2 + self.local_conv2(v2.transpose([0, 2, 1, 3]).reshape([B, -1, C//2]).
                                        transpose([0, 2, 1]).reshape([B, C//2, H*2//self.sr_ratio, W*2//self.sr_ratio])).\
                    reshape([B, C//2, -1]).reshape([B, self.num_heads//2, C // self.num_heads, -1]).transpose([0, 1, 3, 2])
                x2 = (attn2 @ v2).transpose([0, 2, 1, 3]).reshape([B, N, C//2])

                x = paddle.concat([x1, x2], axis=-1)
        else:
            kv = self.kv(x).reshape([B, -1, 2, self.num_heads, C // self.num_heads]).transpose([2, 0, 3, 1, 4])
            k, v = kv[0], kv[1]

            attn = (q @ k.transpose([0, 1, 3, 2])) * self.scale
            attn = F.softmax(attn, axis=-1)
            attn = self.attn_drop(attn)

            x = (attn @ v).transpose([0, 2, 1, 3]).reshape([B, N, C]) + self.local_conv(v.transpose([0, 2, 1, 3]).reshape([B, N, C]).
                                        transpose([0, 2, 1]).reshape([B,C, H, W])).reshape([B, C, N]).transpose([0, 2, 1])
        x = self.proj(x)
        x = self.proj_drop(x)

        return x

二、Detail-specific Feedforward Layers

在MLP中加入了Detail Specific分支（depth-wise卷积）来增强相邻像素的联系，与PVT的MLP不同是有了残差连接。
PS：源码中GELU的位置和残差连接的位置顺序与图相反，参考下方代码。

代码如下：

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.dwconv = DWConv(hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x, H, W):
        x = self.fc1(x)
        x = self.act(x + self.dwconv(x, H, W))  # 残差连接，这里和图画的顺序不一样，图应该画错了
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x

class DWConv(nn.Module):
    def __init__(self, dim=768):
        super(DWConv, self).__init__()
        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)

    def forward(self, x, H, W):
        B, N, C = x.shape
        x = x.transpose(1, 2).view(B, C, H, W)
        x = self.dwconv(x)
        x = x.flatten(2).transpose(1, 2)
        return x

三、网络结构

网络结构如图所示，整体结构与大部分模型相同，区别在于内部的Transfmer block做出了上述改进，此外，该网络未使用cls_token和pos_embedding。

四、实验结果

在ImageNet-1k上表现如下：

五、快速体验

使用paddleclas组网，并将官方repo提供的shunt_s和shunt_b权重由pytorch转换为paddle，在iamgenet-1k上验证其效果，结果如下表：

模型	分辨率	acc-top1(torch)	acc_top1(paddle)
shunt_t	224x224	79.8%	官方未提供权重
shunt_s	224x224	82.9%	82.87%
shunt_b	224x224	84.0%	83.826%

In [ ]

# step 1: tar dataset
%cd ~/
!mkdir ~/data/data96753/val
!tar -xf ~/data/data96753/ILSVRC2012_img_val.tar -C ~/data/data96753/val

In [ ]

# step 2: unzip weight
%cd ~/data/data139670/
!unzip -oq shunt_weight.zip

In [ ]

# step 3: ImageNet-1K val  shunt_s
%cd ~/PaddleClas/
!python3 tools/eval.py -c ./ppcls/configs/ImageNet/shunt/shunt_s.yaml \
    -o Global.pretrained_model=/home/aistudio/data/data139670/shunt_s

In [ ]

# step 3: ImageNet-1K val  shunt_b
%cd ~/PaddleClas/
!python3 tools/eval.py -c ./ppcls/configs/ImageNet/shunt/shunt_b.yaml \
    -o Global.pretrained_model=/home/aistudio/data/data139670/shunt_b