1.项目配置说明

        参数说明：

数据集：  

       --name cifar10-100_500

       --dataset cifar10

哪个版本的模型： 

       --model_type ViT-B_16

预训练权重： 

       --pretrained_dir checkpoint/ViT-B_16.npz

2.patch embeding与position_embedding

        对于图像编码，以VIT - B/16为例，首先用卷积核大小为16*16、步长为16的卷积，对图像进行变换，此时图像维度变成16 * 768 * 14 * 14，再变换维度为[16, 196, 768]，然后将维度为16*1*768的0patch相连。

        对于位置编码，构建一个1 * 197 * 768的向量

        最后，将图像编码与位置编码相加就完成了本次编码。

代码如下：

class Embeddings(nn.Module):
    """Construct the embeddings from patch, position embeddings.
    """
    def __init__(self, config, img_size, in_channels=3):
        super(Embeddings, self).__init__()
        self.hybrid = None
        img_size = _pair(img_size)

        # patch_size 大小 与 patch数量  n_patches
        if config.patches.get("grid") is not None:
            grid_size = config.patches["grid"]
            patch_size = (img_size[0] // 16 // grid_size[0], img_size[1] // 16 // grid_size[1])
            n_patches = (img_size[0] // 16) * (img_size[1] // 16)
            self.hybrid = True
        else:
            patch_size = _pair(config.patches["size"])
            n_patches = (img_size[0] // patch_size[0]) * (img_size[1] // patch_size[1])
            self.hybrid = False

        # 使用混合模型
        if self.hybrid:
            self.hybrid_model = ResNetV2(block_units=config.resnet.num_layers,
                                         width_factor=config.resnet.width_factor)
            in_channels = self.hybrid_model.width * 16
        # patch_embeding 16 * 768 * 14 * 14
        self.patch_embeddings = Conv2d(in_channels=in_channels,
                                       out_channels=config.hidden_size,
                                       kernel_size=patch_size,
                                       stride=patch_size)
        # 初始化 position_embeddings: 1 * 197 * 768
        self.position_embeddings = nn.Parameter(torch.zeros(1, n_patches+1, config.hidden_size))
        # 初始化第 0 个patch，表示分类特征 1*1*768
        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        # dropout层
        self.dropout = Dropout(config.transformer["dropout_rate"])

    def forward(self, x):
        print(x.shape)
        B = x.shape[0]
        # 拓展cls_tokens的维度:16 *1*768
        cls_tokens = self.cls_token.expand(B, -1, -1)
        print(cls_tokens.shape)
        # 混合模型
        if self.hybrid:
            x = self.hybrid_model(x)
        # 编码:16 * 768 * 14 * 14
        x = self.patch_embeddings(x)
        print(x.shape)
        # 变换维度:16 * 768 * 14 * 14-->[16, 768, 196]
        x = x.flatten(2)
        print(x.shape)
        # [16, 768, 196] --> [16, 196, 768]
        x = x.transpose(-1, -2)
        print(x.shape)
        # 加入分类特征patch
        x = torch.cat((cls_tokens, x), dim=1)
        print(x.shape)

        # 加入位置编码
        embeddings = x + self.position_embeddings
        print(embeddings.shape)
        # dropout层
        embeddings = self.dropout(embeddings)
        print(embeddings.shape)
        return embeddings

3.ecoder 

多头注意力模块:

        首先构建q,k,v三个辅助向量，因为我们采用多头注意力机制(12个)，首先，我们需要将q,k,v维度从16, 197, 768转换成16, 12, 197, 64，然后获得q,k的相似性qk，因为获得的是两两之间的关系，所以维度为16, 12, 197, 197，消除量纲，经过softmax后，得到提取到的特征向量qkv，维度为16, 12, 197, 64，再将维度还原成16, 197, 768

class Attention(nn.Module):
    def __init__(self, config, vis):
        super(Attention, self).__init__()
        self.vis = vis
        # heads数量
        self.num_attention_heads = config.transformer["num_heads"]
        # 每个head的向量维度
        self.attention_head_size = int(config.hidden_size / self.num_attention_heads)
        # 总head_size
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        # query向量
        self.query = Linear(config.hidden_size, self.all_head_size)
        # key向量
        self.key = Linear(config.hidden_size, self.all_head_size)
        # value向量
        self.value = Linear(config.hidden_size, self.all_head_size)
        # 全连接层
        self.out = Linear(config.hidden_size, config.hidden_size)
        # dropout层
        self.attn_dropout = Dropout(config.transformer["attention_dropout_rate"])
        self.proj_dropout = Dropout(config.transformer["attention_dropout_rate"])

        self.softmax = Softmax(dim=-1)

    def transpose_for_scores(self, x):
        # 维度:16, 197, 768-->16,197,12,64
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        # print(new_x_shape)
        x = x.view(*new_x_shape)
        # print(x.shape)
        # print(x.permute(0, 2, 1, 3).shape)
        # 16,197,12,64 --> 16, 12, 197, 64
        return x.permute(0, 2, 1, 3)

    def forward(self, hidden_states):
        # print(hidden_states.shape)
        # q,k,v:16, 197, 768
        mixed_query_layer = self.query(hidden_states)
        # print(mixed_query_layer.shape)
        mixed_key_layer = self.key(hidden_states)
        # print(mixed_key_layer.shape)
        mixed_value_layer = self.value(hidden_states)
        # print(mixed_value_layer.shape)
        # q,k,v:16, 197, 768-->16, 12, 197, 64
        query_layer = self.transpose_for_scores(mixed_query_layer)
        # print(query_layer.shape)
        key_layer = self.transpose_for_scores(mixed_key_layer)
        # print(key_layer.shape)
        value_layer = self.transpose_for_scores(mixed_value_layer)
        # print(value_layer.shape)
        # q,k的相似性:16, 12, 197, 197
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        # print(attention_scores.shape)
        # 消除量纲
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        # print(attention_scores.shape)
        attention_probs = self.softmax(attention_scores)
        # print(attention_probs.shape)
        weights = attention_probs if self.vis else None
        attention_probs = self.attn_dropout(attention_probs)
        # print(attention_probs.shape)
        # print(value_layer.shape)
        # 特征向量：qkv:16, 12, 197, 64
        context_layer = torch.matmul(attention_probs, value_layer)
        # print(context_layer.shape)
        # 16, 12, 197, 64-->16, 12, 197, 64
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        # print(context_layer.shape)
        # 16, 12, 197, 64-->16, 197, 768
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)
        # print(context_layer.shape)
        # 全连接层:16, 197, 768
        attention_output = self.out(context_layer)
        # print(attention_output.shape)
        # dropout层
        attention_output = self.proj_dropout(attention_output)
        # print(attention_output.shape)
        return attention_output, weights

      transformer encoder 

        对于输入的x,首先经过层归一化后，输入多头注意力机制，对结果进行残差连接，再经过层归一化，经过两层全连接，残差连接后，得到一个模块结果，堆叠L层，输出最终结果 

class Block(nn.Module):
    def __init__(self, config, vis):
        super(Block, self).__init__()
        # 序列的大小:768
        self.hidden_size = config.hidden_size
        # 层归一化
        self.attention_norm = LayerNorm(config.hidden_size, eps=1e-6)
        self.ffn_norm = LayerNorm(config.hidden_size, eps=1e-6)
        # MLP层
        self.ffn = Mlp(config)
        # 多头注意力机制
        self.attn = Attention(config, vis)

    def forward(self, x):
        # print(x.shape)
        # 16, 197, 768
        h = x
        # 层归一化
        x = self.attention_norm(x)
        # print(x.shape)
        # 多头注意力机制
        x, weights = self.attn(x)
        # 残差连接
        x = x + h
        # print(x.shape)

        h = x
        # 层归一化
        x = self.ffn_norm(x)
        # print(x.shape)
        # MLP层
        x = self.ffn(x)
        # print(x.shape)
        # 残差连接
        x = x + h
        # print(x.shape)
        return x, weights

整体架构

        对于输入x，进行patch embeding和position embeding后，此时维度为16*197*768，输入encoder中，经过L层的编码模块，取出第0个patch的编码结果(表示分类特征),输入分类层，得到预测结果。

class VisionTransformer(nn.Module):
    def __init__(self, config, img_size=224, num_classes=21843, zero_head=False, vis=False):
        super(VisionTransformer, self).__init__()
        self.num_classes = num_classes
        self.zero_head = zero_head
        self.classifier = config.classifier

        self.transformer = Transformer(config, img_size, vis)
        self.head = Linear(config.hidden_size, num_classes)

    def forward(self, x, labels=None):
        x, attn_weights = self.transformer(x)
        print(x.shape)
        # X.shape:16, 197, 768   logits.shape:16, 10
        logits = self.head(x[:, 0])
        print(logits.shape)
        # 交叉熵
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_classes), labels.view(-1))
            return loss
        else:
            return logits, attn_weights