复现篇：Vision Transform复现及讲解

PatchEmbedding层

$PatchEmbedding$PatchEmbedding主要是将输入的图片进行"分词",将图片转换成一个一个的小块传入，在NLP里就对应$sequence$sequence，具体来说，例如下图所示，我们传进去的是$\text{（}3,28,28\text{）}$（3,28,28）的图片，经过Embedding层之后就会变成$16\text{个}7\ast 7$16个7∗7 的小块，之后通过flatten将$\text{（}7,7,3\text{）}$（7,7,3）打平成147个像素点，于是就变成$\text{（}16,147\text{）}$（16,147），16就是我们所说的$nu{m}_{p}atchs$numpatchs,$147$147再接上全连接层后对应的就是$embe{d}_{d}im$embeddim,在传进$transforms$transforms之前需要将每一个$token$token附上每一块的位置信息和分类模块，这里叫$positionembedding$positionembedding和$classtoken$classtoken,二者值的确定都是可学习的参数，需要经过神经网络的学习进行优化

import paddleimport paddle.nn as nnclass PatchEmbedding(nn.Layer):
    def __init__(self, image_size=224, patch_size=16, in_channels=3, embed_dim=768, dropout=0.):
        super().__init__()
        n_patches = (image_size // patch_size) * (image_size // patch_size)
        self.patch_embedding = nn.Conv2D(in_channels=in_channels,
                                         out_channels=embed_dim,
                                         kernel_size=patch_size,
                                         stride=patch_size)
        self.dropout = nn.Dropout(dropout)

        self.class_token = paddle.create_parameter(
                shape=[1, 1, embed_dim],
                dtype='float32',
                default_initializer=nn.initializer.Constant(0.))
        self.position_embedding = paddle.create_parameter(
                shape=[1, n_patches+1, embed_dim],
                dtype='float32',
                default_initializer=nn.initializer.TruncatedNormal(std=.02))    def forward(self, x):
        # [n, c, h, w]
        clss_tokens = self.class_token.expand([x.shape[0], -1, -1]) # for batch，直接在第一个class_token后添加第一个X，之后将其拼接到X后
        x = self.patch_embedding(x)  # [n, embed_dim, h', w']
        x = x.flatten(2)
        x = x.transpose([0, 2, 1])
        x = paddle.concat([clss_tokens, x], axis=1)        return x

attention层

$transform$transform里面最重要的部分可以说就是$attention$attention层，$attention$attention层的思路也是起源于NLP，为了找到每一个$patch$patch与它附近的小块的信息，$attention$attention的目的就是使每一个$token$token注意到其他部分的信息，也叫自注意力机制。具体地，如下图所示，X是Embedding的输出，可以看到他的patch数量是3，也就是说有3个token，这里看到图中的$W^q{W}^k{W}^v$WqWkWv和$P$P都是可学习的参数,其中我们将$QKV$QKV合成一个大的矩阵，目的是后续直接将该矩阵切成三份，$QK$QK的作用就是和其他的$token$token做运算之后经过$scaleforsoftmax$scaleforsoftmax，收集它们的信息，之后在和$V$V增加网络的复杂度

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class Attention(nn.Layer):
    """multi-head self attention"""
    def __init__(self, embed_dim, num_heads, qkv_bias=True, dropout=0., attention_dropout=0.):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = int(embed_dim / num_heads)
        self.all_head_dim = self.head_dim * num_heads
        self.scales = self.head_dim ** -0.5

        self.qkv = nn.Linear(embed_dim,
                             self.all_head_dim * 3)

        self.proj = nn.Linear(embed_dim, embed_dim)

        self.dropout = nn.Dropout(dropout)
        self.attention_dropout = nn.Dropout(attention_dropout)
        self.softmax = nn.Softmax(axis=-1)    def transpose_multihead(self, x):
        # x: [N, num_patches, all_head_dim] -> [N, n_heads, num_patches, head_dim]
        new_shape = x.shape[:-1] + [self.num_heads, self.head_dim]
        x = x.reshape(new_shape)
        x = x.transpose([0, 2, 1, 3])        return x    def forward(self, x):
        B, N, _ = x.shape        # x -> [N, num_patches, dim]
        # x -> q, k, v
        qkv = self.qkv(x).chunk(3, axis=-1)  # 切分qkv
        q, k, v = map(self.transpose_multihead, qkv)

        attn = paddle.matmul(q, k, transpose_y=True) # q * k'
        attn = attn * self.scales
        attn = self.softmax(attn)
        attn = self.attention_dropout(attn)

        out = paddle.matmul(attn, v) 
        out = out.transpose([0, 2, 1, 3])
        out = out.reshape([B, N, -1])
        out = self.proj(out)        
        return out

Encoder层

可以看到，在实现最重要的$attention$attention之后,还需要实现$MLP$MLP,$layearnormalize$layearnormalize以及残差层链接，组成$Encoder$Encoder层，五个$Encoder$Encoder组成一个$transform$transform,在每个$Encoder$Encoder以及每个$MLP$MLP,$layearnormalize$layearnormalize都不会改变数据的维度，目的是我们增加层数时不会变更太多

class Identity(nn.Layer):
    def __init__(self):
        super().__init__()    def forward(self, x):
        return xclass Mlp(nn.Layer):
    def __init__(self, embed_dim, mlp_ratio, dropout=0.):
        super().__init__()
        self.fc1 = nn.Linear(embed_dim, int(embed_dim * mlp_ratio))
        self.fc2 = nn.Linear(int(embed_dim * mlp_ratio), embed_dim)
        self.act = nn.GELU()
        self.dropout = nn.Dropout(dropout)    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.dropout(x)
        x = self.fc2(x)
        x = self.dropout(x)        return xclass EncoderLayer(nn.Layer):
    def __init__(self, embed_dim=768, num_heads=4, qkv_bias=True, mlp_ratio=4.0, dropout=0., attention_dropout=0.):
        super().__init__()
        self.attn_norm = nn.LayerNorm(embed_dim)
        self.attn = Attention(embed_dim, num_heads)
        self.mlp_norm = nn.LayerNorm(embed_dim)
        self.mlp = Mlp(embed_dim, mlp_ratio)    def forward(self, x):
        # TODO
        h = x  # residual
        x = self.attn_norm(x)
        x = self.attn(x)
        x = x + h

        h = x
        x = self.mlp_norm(x)
        x = self.mlp(x)
        x = x + h        return x        

class Encoder(nn.Layer):
    def __init__(self, embed_dim, depth):
        super().__init__()
        layer_list = []        for i in range(depth):
            encoder_layer = EncoderLayer()
            layer_list.append(encoder_layer)
        self.layers = nn.LayerList(layer_list)
        self.norm = nn.LayerNorm(embed_dim)    def forward(self, x):
        for layer in self.layers:
            x = layer(x)
        x = self.norm(x)        return x  


class VisualTransformer(nn.Layer):
    def __init__(self,
                 image_size=224,
                 patch_size=16,
                 in_channels=3,
                 num_classes=1000,
                 embed_dim=768,
                 depth=3,
                 num_heads=8,
                 mlp_ratio=4,
                 qkv_bias=True,
                 dropout=0.,
                 attention_dropout=0.,
                 droppath=0.):
        super().__init__()
        self.patch_embedding = PatchEmbedding(image_size, patch_size, in_channels, embed_dim)
        self.encoder = Encoder(embed_dim, depth)
        self.classifier = nn.Linear(embed_dim, num_classes)    def forward(self, x):
        # x:[N, C, H, W]
        x = self.patch_embedding(x)  # [N, embed_dim, h', w']
        # x = x.flatten(2)  # [N, embed_dim, h'*w'] h'*w'=num_patches
        # x = x.transpose([0, 2, 1])  # [N, num_patches, embed_dim]
        print(x.shape)
        x = self.encoder(x)        print(x.shape)
        x = self.classifier(x[:, 0])        return xdef main():
    t = paddle.randn([4, 3, 224, 224])
    vit = VisualTransformer()
    out=vit(t)    print('aaaaaaaaaa',out.shape)
    paddle.summary(vit, (4, 3, 224, 224)) #查看网络详细信息if __name__ == "__main__":
    main()