self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=patch_size, stride=patch_size)

x = self.proj(x).flatten(2).transpose(1, 2)

class PatchEmbed(nn.Module):
    """
    Image --> Patch Embedding --> Linear Proj --> Pos Embedding
    Image size -> [224,224,3]
    Patch size -> 16*16
    Patch num -> (224^2)/(16^2)=196
    Patch dim -> 16*16*3 =768
    Patch Embedding: [224,224,3] -> [196,768]
    Linear Proj: [196,768] -> [196,768]
 	Positional Embedding: [197,768] -> [196,768]
    """
    def __init__(self, img_size=224, patch_size=16, in_c=3, embed_dim=768, norm_layer=None):
        """
        Args:
            img_size: 默认参数224
            patch_size: 默认参数是16
            in_c: 输入的通道数
            embed_dim: 16*16*3 = 768
            norm_layer: 是否使用norm层，默认为否
        """
        super().__init__()
        img_size = (img_size, img_size) # -> img_size = (224,224)
        patch_size = (patch_size, patch_size) # -> patch_size = (16,16)
        self.img_size = img_size # -> (224,224)
        self.patch_size = patch_size # -> (16,16)
        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) # -> grid_size = (14,14)
        self.num_patches = self.grid_size[0] * self.grid_size[1] # -> num_patches = 196
        # Patch+linear proj的这个操作 [224,224,3] --> [14,14,768]
        self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=patch_size, stride=patch_size)
        # 判断是否有norm_layer层，要是没有不改变输入
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        # 计算各个维度的大小
        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]})."
        
        # flatten: [B, C, H, W] -> [B, C, HW], flatten(2)代表的是从2位置开始展开
        # eg: [1,3,224,224] --> [1,768,14,14] -flatten->[1,768,196]
        # transpose: [B, C, HW] -> [B, HW, C]
        # eg: [1,768,196] -transpose-> [1,196,768]
        x = self.proj(x).flatten(2).transpose(1, 2)
        x = self.norm(x)
        return x

cls_token = nn.Parameter(torch.zeros(1, 1, 768)) # -> cls token
nn.init.trunc_normal_(self.cls_token, std=0.02) # 初始化
cls_token = cls_token.expand(x.shape[0], -1, -1) # (1,1,768) -> (128,1,768)
x = torch.cat((cls_token, x), dim=1)  # [128, 197, 768]

num_patches = 196
pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, 768))
x = x + pos_embed

self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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]  # seperate q, k, v

class Attention(nn.Module):
    def __init__(self,
                 dim,   # 输入token的dim
                 num_heads=8, # attention head的个数
                 qkv_bias=False, # 是否使用qkv bias
                 qk_scale=None,
                 attn_drop_ratio=0.,
                 proj_drop_ratio=0.):
        super(Attention, self).__init__()
        self.num_heads = num_heads
        # 计算每一个head处理的维度head_dim = dim // num_heads --> 768/8 = 96
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5 # 根下dk操作
        # 使用nn.Linear生成w_q,w_k,w_v，因为本质上每一个变换矩阵都是线性变换，
        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):
        # [batch_size, num_patches + 1, total_embed_dim]
        # total_embed_dim不是一开始展开的那个维度，是经过了一个线性变换层得到的
        B, N, C = x.shape

        # [batch_size, num_patches+1, total_embed_dim] -qkv()-> [batch_size, num_patches + 1, 3 * total_embed_dim]
        # reshape: -> [batch_size, num_patches + 1, 3, num_heads, embed_dim_per_head]
        # permute: -> [3, batch_size, num_heads, num_patches + 1, embed_dim_per_head]
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        # q,k,v = [batch_size, num_heads, num_patches + 1, embed_dim_per_head]
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        # transpose(-2,-1)在最后两个维度进行操作，输入的形状[batch_size,num_heads,num_patches+1,embed_dim_per_head]
        # transpose: -> [batch_size, num_heads, embed_dim_per_head, num_patches + 1]
        # @: multiply -> [batch_size, num_heads, num_patches + 1, num_patches + 1]
        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        # @: multiply -> [batch_size, num_heads, num_patches + 1, embed_dim_per_head]
        # transpose: -> [batch_size, num_patches + 1, num_heads, embed_dim_per_head]
        # reshape: -> [batch_size, num_patches + 1, total_embed_dim]
        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):
    """
    in_features --> hidden_features --> out_features
    论文实现时：in_features.shape = out_features.shape
    """
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        # 用or实现了或操作，当hidden_features/out_features为默认值None时
        # 此时out_features/hidden_features=None or in_features = in_features
        # 当对out_features或hidden_features进行输入时，or操作将会默认选择or前面的
        # 此时out_features/hidden_features = out_features/hidden_features
        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):
        # in_features --> hidden_features --> out_features
        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):
    """
    每一个Encoder Block的构成
    每个Encode Block的流程：norm1 --> Multi-Head Attention --> norm2 --> MLP
    """
    def __init__(self,
                 dim, # 输入mlp的维度
                 num_heads, # Multi-Head-Attention的头个数
                 mlp_ratio=4., # hidden_features / in_features = mlp_ratio
                 qkv_bias=False, # q,k,v的生成是否使用bias
                 qk_scale=None,
                 drop_ratio=0., # dropout的比例
                 attn_drop_ratio=0., # 注意力dropout的比例
                 drop_path_ratio=0.,
                 act_layer=nn.GELU, # 激活函数默认使用GELU
                 norm_layer=nn.LayerNorm): # Norm默认使用LayerNorm
        super(Block, self).__init__()
        # 第一层normalization
        self.norm1 = norm_layer(dim)
        # self.attention层的实现
        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()
        # 第二层normalization
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio) # hidden_dim = dim * mlp_ratio
        # mlp实现
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop_ratio)

    def forward(self, x):
        # 实现了两个残差连接
        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
        # 每个patch的图像维度 = embed_dim
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
        # token的个数为1
        self.num_tokens = 2 if distilled else 1
        # 设置激活函数和norm函数
        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
        act_layer = act_layer or nn.GELU
        # 对应的将图片打成patch的操作
        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
        # 设置分类的cls_token
        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        # distilled 是Deit中的 这里为None
        self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
        # pos_embedding 为一个可以学习的参数
        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)]  # stochastic depth decay rule
        # 使用nn.Sequential进行构建，ViT中深度为12
        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)

        # Representation layer
        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()

        # Classifier head(s)
        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()

        # Weight init
        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):
        # [B, C, H, W] -> [B, num_patches, embed_dim]
        x = self.patch_embed(x)  # [B, 196, 768]
        # [1, 1, 768] -> [B, 1, 768]
        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)  # [B, 197, 768]
        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():
                # during inference, return the average of both classifier predictions
                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)