【技术追踪】SAM（Segment Anything Model）代码解析与结构绘制之Mask Decoder

论文：Segment Anything
代码：https://github.com/facebookresearch/segment-anything

系列篇：
（1）【技术追踪】SAM（Segment Anything Model）代码解析与结构绘制之Image Encoder
（2）【技术追踪】SAM（Segment Anything Model）代码解析与结构绘制之Prompt Encoder

本篇示例依然采用系列篇中的狗狗图像运行代码，预测部分代码如下：

input_point = np.array([[1300, 800]])   # 输入point的坐标
input_label = np.array([1])   # label=1表示前景, label=0表示背景
# 输入box的坐标,(700,400)为左上角坐标, (1900,1100)为右下角坐标
input_box = np.array([[700, 400, 1900, 1100]])   
# 调用预测函数
masks, scores, logits = predictor.predict(
    point_coords=input_point,
    point_labels=input_label,
    box=input_box,
    multimask_output=True,
)
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1. Mask Decoder代码解析

（1）输入参数

在【segment_anything/predictor.py --> SamPredictor类 -->predict_torch函数】中调用了mask_decoder实现mask预测，如下所示：

low_res_masks, iou_predictions = self.model.mask_decoder(
            image_embeddings=self.features,
            image_pe=self.model.prompt_encoder.get_dense_pe(),
            sparse_prompt_embeddings=sparse_embeddings,
            dense_prompt_embeddings=dense_embeddings,
            multimask_output=multimask_output,
        )
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①参数self.features为input_image经过image_encoder嵌入后的向量，本例中大小为 ${[1, 256, 64, 64]}$ ；

②参数sparse_embeddings为prompt point和prompt box经过prompt_encoder得到的嵌入向量，本例中其大小为 ${[1, 3, 256]}$ ；

③参数dense_embeddings在本例中为无prompt mask输入时采用 nn.Embedding 的预定义嵌入向量，其大小为 ${[1, 256, 64, 64]}$ ；

④参数multimask_output是bool型参数，默认为True，支持多mask输出；

⑤参数self.model.prompt_encoder.get_dense_pe()调用PositionEmbeddingRandom实现位置编码，其大小为 ${[1, 256, 64, 64]}$ ；

  def get_dense_pe(self) -> torch.Tensor:
        return self.pe_layer(self.image_embedding_size).unsqueeze(0)
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（2）MaskDecoder类

位置：【segment_anything/modeling/mask_decoder.py -->MaskDecoder类】
作用： 初始化网络结构，并调用predict_masks函数实现mask和iou预测

先看MaskDecoder的 $_ _ {\_\_init\_\_}$ 初始化函数和 ${forward}$ 函数：

class MaskDecoder(nn.Module):
    def __init__(
        self,
        *,
        transformer_dim: int,
        transformer: nn.Module,
        num_multimask_outputs: int = 3,
        activation: Type[nn.Module] = nn.GELU,
        iou_head_depth: int = 3,
        iou_head_hidden_dim: int = 256,
    ) -> None:
       
        super().__init__()
        self.transformer_dim = transformer_dim   # transformer的通道维度 = 256
        self.transformer = transformer  # 用于mask预测的transformer = TwoWayTransformer

        self.num_multimask_outputs = num_multimask_outputs  # 消除歧义时需要的mask数量 = 3

        self.iou_token = nn.Embedding(1, transformer_dim)  # (1, 256)
        self.num_mask_tokens = num_multimask_outputs + 1   # mask数目加1 = 4
        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)  # (4, 256)
        # 以反卷积实现4倍上采样
        self.output_upscaling = nn.Sequential(
            nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
            LayerNorm2d(transformer_dim // 4),
            activation(),
            nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
            activation(),
        )
        # 4个mask对应的mlp
        self.output_hypernetworks_mlps = nn.ModuleList(
            [
                MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
                for i in range(self.num_mask_tokens)
            ]
        )
        # iou预测对应的mlp
        self.iou_prediction_head = MLP(
            transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
        )

    def forward(
        self,
        image_embeddings: torch.Tensor,
        image_pe: torch.Tensor,
        sparse_prompt_embeddings: torch.Tensor,
        dense_prompt_embeddings: torch.Tensor,
        multimask_output: bool,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
       
        masks, iou_pred = self.predict_masks(
            image_embeddings=image_embeddings,  # image encoder嵌入 [1, 256, 64, 64]
            image_pe=image_pe,  # 图像嵌入大小对应的位置编码 [1, 256, 64, 64]
            sparse_prompt_embeddings=sparse_prompt_embeddings,  # prompt point和box嵌入 [1, 3, 256]
            dense_prompt_embeddings=dense_prompt_embeddings,  # prompt mask嵌入[1, 256, 64, 64]
        )  # 输出mask.size()=[1,4,256,256], iou_pred.size()=[1,4]

        # Select the correct mask or masks for output
        if multimask_output:
            mask_slice = slice(1, None)   # 从索引1开始取后面全部
        else:
            mask_slice = slice(0, 1)   # 从索引0开始取到1结束
        masks = masks[:, mask_slice, :, :]  # [1, 3, 256, 256]
        iou_pred = iou_pred[:, mask_slice]  # [1, 3]

        return masks, iou_pred
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传送门：【python函数】内置函数slice()用法解析

${forward}$ 的过程中主要完成了 predict_masks 函数调用；而在 $_ _ {\_\_init\_\_}$ 函数中定义了 ${transformer}$ ， ${output\_upscaling}$ ， ${output\_hypernetworks\_mlps}$ 和 ${iou\_prediction\_head}$ 这四个玩意儿，接下来咱来瞅瞅他们是啥样的。

① transformer： 在【segment_anything/build_sam.py】中可以看到为transformer定义为TwoWayTransformer，prompt_embed_dim参数为256。

        mask_decoder=MaskDecoder(
            num_multimask_outputs=3,
            transformer=TwoWayTransformer(
                depth=2,
                embedding_dim=prompt_embed_dim,  # 256
                mlp_dim=2048,
                num_heads=8,
            ),
            transformer_dim=prompt_embed_dim,
            iou_head_depth=3,
            iou_head_hidden_dim=256,
        ),
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TwoWayTransformer 结构如下：

class TwoWayTransformer(nn.Module):
    def __init__(
        self,
        depth: int,
        embedding_dim: int,
        num_heads: int,
        mlp_dim: int,
        activation: Type[nn.Module] = nn.ReLU,
        attention_downsample_rate: int = 2,
    ) -> None:
        
        super().__init__()
        self.depth = depth   # =2
        self.embedding_dim = embedding_dim  # =256
        self.num_heads = num_heads  # =8
        self.mlp_dim = mlp_dim  # =2048
        self.layers = nn.ModuleList()

        # 2个TwoWayAttentionBlock模块
        for i in range(depth):
            self.layers.append(
                TwoWayAttentionBlock(
                    embedding_dim=embedding_dim,  # 256
                    num_heads=num_heads,  # 8
                    mlp_dim=mlp_dim,  # 2048
                    activation=activation,  # nn.ReLU
                    attention_downsample_rate=attention_downsample_rate,  # 降采样率=2
                    skip_first_layer_pe=(i == 0),  # 第1个TwoWayAttentionBlock为True, 第2个TwoWayAttentionBlock为False
                )
            )
        # 1个Attention模块
        self.final_attn_token_to_image = Attention(
            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
        )
        self.norm_final_attn = nn.LayerNorm(embedding_dim)

    def forward(
        self,
        image_embedding: Tensor,  # 图像编码:[1,256,64,64]
        image_pe: Tensor,   # 图像位置编码:[1,256,64,64]
        point_embedding: Tensor,   # iou_token,mask_tokens和sparse_prompt_embeddings的拼接向量:[1,8,256]
    ) -> Tuple[Tensor, Tensor]:
       
        # BxCxHxW -> BxHWxC == B x N_image_tokens x C
        bs, c, h, w = image_embedding.shape  # [1, 256, 64, 64]
        image_embedding = image_embedding.flatten(2).permute(0, 2, 1)  # [1,4096,256]
        image_pe = image_pe.flatten(2).permute(0, 2, 1)   # [1,4096,256]

        # Prepare queries
        queries = point_embedding  # 查询Q:[1,8,256]
        keys = image_embedding     # 键值K:[1,4096,256]

        # Apply transformer blocks and final layernorm
        for layer in self.layers:
            queries, keys = layer(
                queries=queries,
                keys=keys,
                query_pe=point_embedding,
                key_pe=image_pe,
            )  # 经过两个TwoWayAttentionBlock后, queries:[1,8,256], keys:[1,4096,256]

        # Apply the final attention layer from the points to the image
        q = queries + point_embedding  # [1,8,256]
        k = keys + image_pe  # [1,4096,256]

        attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)  # [1,8,256]
        queries = queries + attn_out  # [1,8,256]
        queries = self.norm_final_attn(queries)  # [1,8,256]

        return queries, keys
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Attention 结构如下：
以TwoWayAttentionBlock的第一个Attention模块为例，即：

# embedding_dim = 256, num_heads=8
self.self_attn = Attention(embedding_dim, num_heads) 
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Attention模块主要实现了Transformer中基本的attention机制，若参数downsample_rate不为1，则会先对维度进行下采样映射：

class Attention(nn.Module):

    def __init__(
        self,
        embedding_dim: int,   # 256
        num_heads: int,   # 8
        downsample_rate: int = 1,   # 1
    ) -> None:
        super().__init__()
        self.embedding_dim = embedding_dim   # 256
        self.internal_dim = embedding_dim // downsample_rate   # 256
        self.num_heads = num_heads   # 8
        assert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."

        self.q_proj = nn.Linear(embedding_dim, self.internal_dim)   # (256,256)
        self.k_proj = nn.Linear(embedding_dim, self.internal_dim)   # (256,256)
        self.v_proj = nn.Linear(embedding_dim, self.internal_dim)   # (256,256)
        self.out_proj = nn.Linear(self.internal_dim, embedding_dim)   # (256,256)

    def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
        b, n, c = x.shape
        x = x.reshape(b, n, num_heads, c // num_heads)
        return x.transpose(1, 2)  # B x N_heads x N_tokens x C_per_head

    def _recombine_heads(self, x: Tensor) -> Tensor:
        b, n_heads, n_tokens, c_per_head = x.shape
        x = x.transpose(1, 2)
        return x.reshape(b, n_tokens, n_heads * c_per_head)  # B x N_tokens x C

    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
        # Input projections
        # 输入q:[1,8,256];k:[1,8,256];v:[1,8,256]
        q = self.q_proj(q)  # [1,8,256]
        k = self.k_proj(k)  # [1,8,256]
        v = self.v_proj(v)  # [1,8,256]

        # Separate into heads
        q = self._separate_heads(q, self.num_heads)  # [1,8,8,32]
        k = self._separate_heads(k, self.num_heads)  # [1,8,8,32]
        v = self._separate_heads(v, self.num_heads)  # [1,8,8,32]

        _, _, _, c_per_head = q.shape   # 每个head的维度c_per_head=32
        # attention机制-----------------------------------------------------------------------
        # 每个head实现q乘k的转置: [1,8,8,32]@[1,8,32,8]->[1,8,8,8]
        attn = q @ k.permute(0, 1, 3, 2)  # B x N_heads x N_tokens x N_tokens
        attn = attn / math.sqrt(c_per_head)  # q @ k(^T) / 根号d
        attn = torch.softmax(attn, dim=-1)  # [1,8,8,8]
        # -----------------------------------------------------------------------------------
        # Get output
        out = attn @ v   # softmax( q @ k(^T) / 根号d ) @ v ---> [1,8,8,32]
        out = self._recombine_heads(out)  # [1,8,256]
        out = self.out_proj(out)  # [1,8,256]
 
        return out
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为避免代码看的太晕，把Attention可视化一下，没错，就是最基本的Multi-head Attention啦~

在这里插入图片描述

TwoWayAttentionBlock 结构如下：
以TwoWayTransformer的第一个TwoWayAttentionBlock模块为例，即：

TwoWayAttentionBlock(
                    embedding_dim=embedding_dim,  # 256
                    num_heads=num_heads,  # 8
                    mlp_dim=mlp_dim,  # 2048
                    activation=activation,  # nn.ReLU
                    attention_downsample_rate=attention_downsample_rate,  # 降采样率=2
                    skip_first_layer_pe=(i == 0),  # 第1个TwoWayAttentionBlock为True
                    )
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TwoWayAttentionBlock模块：

class TwoWayAttentionBlock(nn.Module):
    def __init__(
        self,
        embedding_dim: int,
        num_heads: int,
        mlp_dim: int = 2048,
        activation: Type[nn.Module] = nn.ReLU,
        attention_downsample_rate: int = 2,
        skip_first_layer_pe: bool = False,
    ) -> None:
        
        super().__init__()
        self.self_attn = Attention(embedding_dim, num_heads)   # embedding_dim=256, num_heads=8
        self.norm1 = nn.LayerNorm(embedding_dim)  # 256

        self.cross_attn_token_to_image = Attention(
            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
        )   # embedding_dim=256, num_heads=8, attention_downsample_rate=2
        self.norm2 = nn.LayerNorm(embedding_dim)  # 256

        # embedding_dim=256, mlp_dim=2048, activation=nn.ReLU
        self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
        self.norm3 = nn.LayerNorm(embedding_dim)  # 256

        self.norm4 = nn.LayerNorm(embedding_dim)  # 256
        self.cross_attn_image_to_token = Attention(
            embedding_dim, num_heads, downsample_rate=attention_downsample_rate
        )   # embedding_dim=256, num_heads=8, attention_downsample_rate=2

        self.skip_first_layer_pe = skip_first_layer_pe  # True

    def forward(
        self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
    ) -> Tuple[Tensor, Tensor]:
        # 输入queries:[1,8,256], keys:[1,4096,256], query_pe:[1,8,256], key_pe:[1,4096,256]
        # Self attention block
        if self.skip_first_layer_pe:
            queries = self.self_attn(q=queries, k=queries, v=queries)  # [1,8,256]
        else:
            q = queries + query_pe
            attn_out = self.self_attn(q=q, k=q, v=queries)
            queries = queries + attn_out
        queries = self.norm1(queries)  # [1,8,256]

        # Cross attention block, tokens attending to image embedding
        q = queries + query_pe  # [1,8,256]
        k = keys + key_pe  # [1,4096,256]
        attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)  # [1,8,256]
        queries = queries + attn_out  # [1,8,256]
        queries = self.norm2(queries)  # [1,8,256]

        # MLP block
        mlp_out = self.mlp(queries)   # [1,8,256]
        queries = queries + mlp_out   # [1,8,256]
        queries = self.norm3(queries)  # [1,8,256]

        # Cross attention block, image embedding attending to tokens
        q = queries + query_pe    # [1,8,256]
        k = keys + key_pe   # [1,4096,256]
        attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)  # [1,4096,256]
        keys = keys + attn_out  # [1,4096,256]
        keys = self.norm4(keys)  # [1,4096,256]

        return queries, keys
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可以看到TwoWayTransformer的结构以及token维度变化并不复杂，但其交错的 ${Q}$ ， ${K}$ ， ${V}$ 确实令人眼花缭乱：
在这里插入图片描述

TwoWayTransformer中的MLP：

class MLPBlock(nn.Module):
    def __init__(
        self,
        embedding_dim: int,
        mlp_dim: int,
        act: Type[nn.Module] = nn.GELU,
    ) -> None:
        super().__init__()
        # embedding_dim=256, mlp_dim=2048
        self.lin1 = nn.Linear(embedding_dim, mlp_dim)  
        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
        self.act = act()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.lin2(self.act(self.lin1(x)))
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MLP为简单的线性、激活、线性结构：
在这里插入图片描述

② output_upscaling：

Sequential(
  (0): ConvTranspose2d(256, 64, kernel_size=(2, 2), stride=(2, 2))
  (1): LayerNorm2d()
  (2): GELU(approximate='none')
  (3): ConvTranspose2d(64, 32, kernel_size=(2, 2), stride=(2, 2))
  (4): GELU(approximate='none')
)
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output_upscaling模块由两个反卷积、两个GELU激活和一个LayerNorm组成，实现了特征图的四倍上采样，在 predict_masks函数 中将 ${[1,256,64,64]}$ 上采样至 ${[1,32,256,256]}$ 。

src = src.transpose(1, 2).view(b, c, h, w)   # reshape: [1,4096,256]-> [1,256,64,64]
upscaled_embedding = self.output_upscaling(src) # [1,32,256,256]
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③ output_hypernetworks_mlps：

ModuleList(
  (0-3): 4 x MLP(
    (layers): ModuleList(
      (0-1): 2 x Linear(in_features=256, out_features=256, bias=True)
      (2): Linear(in_features=256, out_features=32, bias=True)
    )
  )
)
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output_hypernetworks_mlps由4个MLP组成，在 predict_masks函数 中将 ${[1,256]}$ 下采样至 ${[1,32]}$ 。与TwoWayAttentionBlock中的MLP不同，其结构稍稍多一丢丢：

class MLP(nn.Module):
    def __init__(
            self,
            input_dim: int,   # 256
            hidden_dim: int,  # 256
            output_dim: int,  # 32
            num_layers: int,  # 3
            sigmoid_output: bool = False,  # False
    ) -> None:
        super().__init__()
        self.num_layers = num_layers  # 3
        h = [hidden_dim] * (num_layers - 1)  # [256,256]
        self.layers = nn.ModuleList(
            # [input_dim] + h: [256,256,256], h + [output_dim]:[256,256,32]
            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
        )
        self.sigmoid_output = sigmoid_output

    def forward(self, x):
        for i, layer in enumerate(self.layers):
        	# i<2经线性层后relu激活
            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)  
        if self.sigmoid_output:
            x = F.sigmoid(x)
        return x
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在这里插入图片描述

④ iou_prediction_head：

MLP(
  (layers): ModuleList(
    (0-1): 2 x Linear(in_features=256, out_features=256, bias=True)
    (2): Linear(in_features=256, out_features=4, bias=True)
  )
)
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iou_prediction_head用以实现iou预测，由1个MLP完成，其结构与output_hypernetworks_mlps中的MLP一样，只是最终将 ${[1,256]}$ 映射至 ${[1,4]}$ ，分别代表非multimask预测时的1个mask和multimask预测时的3个mask的iou。

（3）predict_masks函数

位置：【segment_anything/modeling/mask_decoder.py --> MaskDecoder类 --> predict_masks函数】
作用： 利用上述 ${transformer}$ ， ${output\_upscaling}$ ， ${output\_hypernetworks\_mlps}$ 和 ${iou\_prediction\_head}$ 四个模块，实现mask和iou预测

此时此刻，首先来重温一下，传入predict_masks函数的参数分别是什么：

① image_embeddings：image encoder嵌入，大小为 ${[1, 256, 64, 64]}$ ；
② image_pe：图像嵌入大小对应的位置编码，大小同为 ${[1, 256, 64, 64]}$ ；
③ sparse_prompt_embeddings：prompt point和box嵌入，大小为 ${[1, 3, 256]}$ ；
④ dense_prompt_embeddings：prompt mask嵌入，大小为 ${[1, 256, 64, 64]}$ ；

def predict_masks(
        self,
        image_embeddings: torch.Tensor,  # [1, 256, 64, 64]
        image_pe: torch.Tensor,  # [1, 256, 64, 64]
        sparse_prompt_embeddings: torch.Tensor,  # [1, 3, 256]
        dense_prompt_embeddings: torch.Tensor,  # [1, 256, 64, 64]
) -> Tuple[torch.Tensor, torch.Tensor]:
    """Predicts masks. See 'forward' for more details."""
    # Concatenate output tokens
    # 拼接iou的token和mask的token: [1,256]+[4,256]->[5,256]
    output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
    output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)  # [1,5,256]
    # iou的token和mask的token + prompt point和box嵌入
    tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)  # [1,8,256]

    # Expand per-image data in batch direction to be per-mask
    src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)  # 按batch重复: [1,256,64,64]
    src = src + dense_prompt_embeddings  # [1,256,64,64]
    pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)  # 按batch重复: [1,256,64,64]
    b, c, h, w = src.shape  # 1,256,64,64

    # Run the transformer
    # src是image encoder嵌入和prompt mask嵌入
    # pos_src是图像嵌入大小对应的位置编码
    # tokens是iou的token和mask的token + prompt point和box嵌入
    hs, src = self.transformer(src, pos_src, tokens)  # hs:[1,8,256], src:[1,4096,256]
    iou_token_out = hs[:, 0, :]  # 第1个为iou的token输出[1,256]
    mask_tokens_out = hs[:, 1: (1 + self.num_mask_tokens), :]  # 随后4个为mask的token输出[4,256]

    # Upscale mask embeddings and predict masks using the mask tokens
    src = src.transpose(1, 2).view(b, c, h, w)   # reshape: [1,4096,256]-> [1,256,64,64]
    upscaled_embedding = self.output_upscaling(src)  # [1,32,256,256]
    hyper_in_list: List[torch.Tensor] = []
    for i in range(self.num_mask_tokens):
        hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
    hyper_in = torch.stack(hyper_in_list, dim=1)  # [1,4,32]
    b, c, h, w = upscaled_embedding.shape  # 1,32,256,256
    
    masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)  # [1,4,256,256]

    # Generate mask quality predictions
    iou_pred = self.iou_prediction_head(iou_token_out)  # [1,4]

    return masks, iou_pred
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由此可见，经TwoWayTransformer获得了iou_token_out和mask_tokens_out，iou_token_out由iou_prediction_head（1个MLP）实现iou预测，4个mask_tokens_out分别经过1个MLP所获得的映射拼接后，与经过output_upscaling上采样后的图像嵌入（包含image encoder嵌入和prompt mask嵌入）进行矩阵相乘，得到mask预测。

2. Mask Decoder结构绘制

（1）结构打印

MaskDecoder(
  (transformer): TwoWayTransformer(
    (layers): ModuleList(
      (0-1): 2 x TwoWayAttentionBlock(
        (self_attn): Attention(
          (q_proj): Linear(in_features=256, out_features=256, bias=True)
          (k_proj): Linear(in_features=256, out_features=256, bias=True)
          (v_proj): Linear(in_features=256, out_features=256, bias=True)
          (out_proj): Linear(in_features=256, out_features=256, bias=True)
        )
        (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
        (cross_attn_token_to_image): Attention(
          (q_proj): Linear(in_features=256, out_features=128, bias=True)
          (k_proj): Linear(in_features=256, out_features=128, bias=True)
          (v_proj): Linear(in_features=256, out_features=128, bias=True)
          (out_proj): Linear(in_features=128, out_features=256, bias=True)
        )
        (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
        (mlp): MLPBlock(
          (lin1): Linear(in_features=256, out_features=2048, bias=True)
          (lin2): Linear(in_features=2048, out_features=256, bias=True)
          (act): ReLU()
        )
        (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
        (norm4): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
        (cross_attn_image_to_token): Attention(
          (q_proj): Linear(in_features=256, out_features=128, bias=True)
          (k_proj): Linear(in_features=256, out_features=128, bias=True)
          (v_proj): Linear(in_features=256, out_features=128, bias=True)
          (out_proj): Linear(in_features=128, out_features=256, bias=True)
        )
      )
    )
    (final_attn_token_to_image): Attention(
      (q_proj): Linear(in_features=256, out_features=128, bias=True)
      (k_proj): Linear(in_features=256, out_features=128, bias=True)
      (v_proj): Linear(in_features=256, out_features=128, bias=True)
      (out_proj): Linear(in_features=128, out_features=256, bias=True)
    )
    (norm_final_attn): LayerNorm((256,), eps=1e-05, elementwise_affine=True)
  )
  (iou_token): Embedding(1, 256)
  (mask_tokens): Embedding(4, 256)
  (output_upscaling): Sequential(
    (0): ConvTranspose2d(256, 64, kernel_size=(2, 2), stride=(2, 2))
    (1): LayerNorm2d()
    (2): GELU(approximate='none')
    (3): ConvTranspose2d(64, 32, kernel_size=(2, 2), stride=(2, 2))
    (4): GELU(approximate='none')
  )
  (output_hypernetworks_mlps): ModuleList(
    (0-3): 4 x MLP(
      (layers): ModuleList(
        (0-1): 2 x Linear(in_features=256, out_features=256, bias=True)
        (2): Linear(in_features=256, out_features=32, bias=True)
      )
    )
  )
  (iou_prediction_head): MLP(
    (layers): ModuleList(
      (0-1): 2 x Linear(in_features=256, out_features=256, bias=True)
      (2): Linear(in_features=256, out_features=4, bias=True)
    )
  )
)
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（2）结构绘制

整体结构就是这样的啦，完结，撒花~

在这里插入图片描述

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原文地址：https://blog.csdn.net/qq_43426908/article/details/133877154