【图像分割】【深度学习】SAM官方Pytorch代码-Mask decoder模块MaskDeco网络解析

【图像分割】【深度学习】SAM官方Pytorch代码-Mask decoder模块MaskDeco网络解析

Segment Anything：建立了迄今为止最大的分割数据集，在1100万张图像上有超过1亿个掩码，模型的设计和训练是灵活的，其重要的特点是Zero-shot(零样本迁移性)转移到新的图像分布和任务，一个图像分割新的任务、模型和数据集。SAM由三个部分组成：一个强大的图像编码器(Image encoder)计算图像嵌入，一个提示编码器(Prompt encoder)嵌入提示，然后将两个信息源组合在一个轻量级掩码解码器(Mask decoder)中来预测分割掩码。本博客将讲解Mask decoder模块的深度学习网络代码。

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前言

在详细解析SAM代码之前，首要任务是成功运行SAM代码【win10下参考教程】，后续学习才有意义。本博客讲解Mask decoder模块的深度网络代码，不涉及其他功能模块代码。

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MaskDecoder网络简述

SAM模型关于MaskDeco网络的配置

博主以sam_vit_b为例，详细讲解MaskDeco网络的结构。
代码位置：segment_anything/build_sam.py

def build_sam_vit_b(checkpoint=None):return _build_sam(# 图像编码channelencoder_embed_dim=768,# 主体编码器的个数encoder_depth=12,# attention中head的个数encoder_num_heads=12,# 需要将相对位置嵌入添加到注意力图的编码器( Encoder Block)encoder_global_attn_indexes=[2, 5, 8, 11],# 权重checkpoint=checkpoint,)

sam模型中Mask_decoder模块初始化

mask_decoder=MaskDecoder(# 消除掩码歧义预测的掩码数num_multimask_outputs=3,# 用于预测mask的网咯transformertransformer=TwoWayTransformer(# 层数depth=2,# 输入channelembedding_dim=prompt_embed_dim,# MLP内部channelmlp_dim=2048,# attention的head数num_heads=8,),# transformer的channeltransformer_dim=prompt_embed_dim,# MLP的深度,MLP用于预测掩模质量的iou_head_depth=3,# MLP隐藏channeliou_head_hidden_dim=256,
),

MaskDeco网络结构与执行流程

Mask decoder源码位置:segment_anything/modeling/mask_decoder.py
MaskDeco网络(MaskDecoder类)结构参数配置。

def __init__(self,*,# transformer的channeltransformer_dim: int,# 用于预测mask的网咯transformertransformer: nn.Module,# 消除掩码歧义预测的掩码数num_multimask_outputs: int = 3,# 激活层activation: Type[nn.Module] = nn.GELU,# MLP深度,MLP用于预测掩模质量的iou_head_depth: int = 3,# MLP隐藏channeliou_head_hidden_dim: int = 256,
) -> None:super().__init__()self.transformer_dim = transformer_dim              # transformer的channel#----- transformer -----self.transformer = transformer                      # 用于预测mask的网咯transformer# ----- transformer -----self.num_multimask_outputs = num_multimask_outputs  # 消除掩码歧义预测的掩码数self.iou_token = nn.Embedding(1, transformer_dim)   # iou的takenself.num_mask_tokens = num_multimask_outputs + 1    # mask数self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)      # mask的tokens数#----- upscaled -----# 4倍上采样self.output_upscaling = nn.Sequential(nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),     #转置卷积 上采样2倍LayerNorm2d(transformer_dim // 4),activation(),nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),activation(),)# ----- upscaled -----# ----- MLP -----# 对应mask数的MLPself.output_hypernetworks_mlps = nn.ModuleList([MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)for i in range(self.num_mask_tokens)])# ----- MLP -----# ----- MLP -----# 对应iou的MLPself.iou_prediction_head = MLP(transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth)# ----- MLP -----

SAM模型中MaskDeco网络结构如下图所示：

原论文中Mask decoder模块各部分结构示意图：

MaskDeco网络(MaskDecoder类)在特征提取中的几个基本步骤：

1. transformer：融合特征(提示信息特征与图像特征)获得粗略掩膜src
2. upscaled：对粗略掩膜src上采样
3. mask_MLP：全连接层组(计算加权权重，使粗掩膜src转变为掩膜mask)
4. iou_MLP：全连接层组(计算掩膜mask的Score)

def forward(self,# image encoder 图像特征image_embeddings: torch.Tensor,# 位置编码image_pe: torch.Tensor,# 标记点和标记框的嵌入编码sparse_prompt_embeddings: torch.Tensor,# 输入mask的嵌入编码dense_prompt_embeddings: torch.Tensor,# 是否输出多个maskmultimask_output: bool,
) -> Tuple[torch.Tensor, torch.Tensor]:masks, iou_pred = self.predict_masks(image_embeddings=image_embeddings,image_pe=image_pe,sparse_prompt_embeddings=sparse_prompt_embeddings,dense_prompt_embeddings=dense_prompt_embeddings,)# Select the correct mask or masks for outputif multimask_output:mask_slice = slice(1, None)else:mask_slice = slice(0, 1)masks = masks[:, mask_slice, :, :]iou_pred = iou_pred[:, mask_slice]return masks, iou_pred

def predict_masks(self,image_embeddings: torch.Tensor,image_pe: torch.Tensor,sparse_prompt_embeddings: torch.Tensor,dense_prompt_embeddings: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:# Concatenate output tokens# 1,E and 4,E --> 5,Eoutput_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)# 5,E --> B,5,Eoutput_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)# B,5,E and B,N,E -->B,5+N,E       N是点的个数(标记点和标记框的点)tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)# 扩展image_embeddings的B维度,因为boxes标记分割时,n个box时batchsize=batchsize*n# Expand per-image data in batch direction to be per-mask# B,C,H,Wsrc = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)# B,C,H,W + 1,C,H,W ---> B,C,H,Wsrc = src + dense_prompt_embeddings# 1,C,H,W---> B,C,H,Wpos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)b, c, h, w = src.shape# ----- transformer -----# Run the transformer# B,N,Chs, src = self.transformer(src, pos_src, tokens)# ----- transformer -----iou_token_out = hs[:, 0, :]mask_tokens_out = hs[:, 1: (1 + self.num_mask_tokens), :]# Upscale mask embeddings and predict masks using the mask tokens# B,N,C-->B,C,H,Wsrc = src.transpose(1, 2).view(b, c, h, w)# ----- upscaled -----# 4倍上采样upscaled_embedding = self.output_upscaling(src)# ----- upscaled -----hyper_in_list: List[torch.Tensor] = []# ----- mlp -----for i in range(self.num_mask_tokens):# mask_tokens_out[:, i, :]: B,1,C# output_hypernetworks_mlps: B,1,chyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))# B,n,chyper_in = torch.stack(hyper_in_list, dim=1)# ----- mlp -----b, c, h, w = upscaled_embedding.shape# B,n,c × B,c,N-->B,n,h,wmasks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)# ----- mlp -----# Generate mask quality predictions# iou_token_out: B,1,niou_pred = self.iou_prediction_head(iou_token_out)# ----- mlp -----# masks: B,n,h,w# iou_pred: B,1,nreturn masks, iou_pred

MaskDeco网络基本步骤代码详解

transformer

MaskDeco由多个重复堆叠TwoWayAttention Block和1个Multi-Head Attention组成。

class TwoWayTransformer(nn.Module):def __init__(self,# 层数depth: int,# 输入channelembedding_dim: int,# attention的head数num_heads: int,# MLP内部channelmlp_dim: int,activation: Type[nn.Module] = nn.ReLU,attention_downsample_rate: int = 2,) -> None:super().__init__()self.depth = depth      # 层数self.embedding_dim = embedding_dim          # 输入channelself.num_heads = num_heads                  # attention的head数self.mlp_dim = mlp_dim                      # MLP内部隐藏channelself.layers = nn.ModuleList()for i in range(depth):self.layers.append(TwoWayAttentionBlock(embedding_dim=embedding_dim,    # 输入channelnum_heads=num_heads,            # attention的head数mlp_dim=mlp_dim,                # MLP中间channelactivation=activation,          # 激活层attention_downsample_rate=attention_downsample_rate,      # 下采样skip_first_layer_pe=(i == 0),))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,image_pe: Tensor,point_embedding: Tensor,) -> Tuple[Tensor, Tensor]:# BxCxHxW -> BxHWxC == B x N_image_tokens x Cbs, c, h, w = image_embedding.shape# 图像编码(image_encoder的输出)# BxHWxC=>B,N,Cimage_embedding = image_embedding.flatten(2).permute(0, 2, 1)# 图像位置编码# BxHWxC=>B,N,Cimage_pe = image_pe.flatten(2).permute(0, 2, 1)# 标记点编码# B,N,Cqueries = point_embeddingkeys = image_embedding# -----TwoWayAttention-----for layer in self.layers:queries, keys = layer(queries=queries,keys=keys,query_pe=point_embedding,key_pe=image_pe,)# -----TwoWayAttention-----q = queries + point_embeddingk = keys + image_pe# -----Attention-----attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)# -----Attention-----queries = queries + attn_outqueries = self.norm_final_attn(queries)return queries, keys

TwoWayAttention Block

TwoWayAttention Block由LayerNorm 、Multi-Head Attention和MLP构成。

class TwoWayAttentionBlock(nn.Module):def __init__(self,embedding_dim: int,         # 输入channelnum_heads: int,             # attention的head数mlp_dim: int = 2048,        # MLP中间channelactivation: Type[nn.Module] = nn.ReLU,      # 激活层attention_downsample_rate: int = 2,         # 下采样skip_first_layer_pe: bool = False,) -> None:super().__init__()self.self_attn = Attention(embedding_dim, num_heads)self.norm1 = nn.LayerNorm(embedding_dim)self.cross_attn_token_to_image = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)self.norm2 = nn.LayerNorm(embedding_dim)self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)self.norm3 = nn.LayerNorm(embedding_dim)self.norm4 = nn.LayerNorm(embedding_dim)self.cross_attn_image_to_token = Attention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate)self.skip_first_layer_pe = skip_first_layer_pedef forward(self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor) -> Tuple[Tensor, Tensor]:# queries：标记点编码相关(原始标记点编码经过一系列特征提取)# keys：原始图像编码相关(原始图像编码经过一系列特征提取)# query_pe：原始标记点编码# key_pe：原始图像位置编码# 第一轮本身queries==query_pe没比较再"残差"if self.skip_first_layer_pe:queries = self.self_attn(q=queries, k=queries, v=queries)else:q = queries + query_peattn_out = self.self_attn(q=q, k=q, v=queries)queries = queries + attn_outqueries = self.norm1(queries)# Cross attention block, tokens attending to image embeddingq = queries + query_pek = keys + key_peattn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)queries = queries + attn_outqueries = self.norm2(queries)# MLP blockmlp_out = self.mlp(queries)queries = queries + mlp_outqueries = self.norm3(queries)# Cross attention block, image embedding attending to tokensq = queries + query_pek = keys + key_peattn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)keys = keys + attn_outkeys = self.norm4(keys)return queries, keys

TwoWayAttentionBlock的结构对比示意图：

原论文中TwoWayAttention部分示意图：

个人理解：TwoWayAttentionBlock是Prompt encoder的提示信息特征与Image encoder的图像特征的融合过程，而Prompt encoder对提示信息没有过多处理，因此博主认为TwoWayAttentionBlock的目的是边对提示信息特征做进一步处理边与图像特征融合。

Attention

MaskDeco的Attention与ViT的Attention有些细微的不同：MaskDeco的Attention是3个FC层分别接受3个输入获得q、k和v，而ViT的Attention是1个FC层接受1个输入后将结果均拆分获得q、k和v。

class Attention(nn.Module):def __init__(self,embedding_dim: int,         # 输入channelnum_heads: int,             # attention的head数downsample_rate: int = 1,   # 下采样) -> None:super().__init__()self.embedding_dim = embedding_dimself.internal_dim = embedding_dim // downsample_rateself.num_heads = num_headsassert self.internal_dim % num_heads == 0, "num_heads must divide embedding_dim."# qkv获取self.q_proj = nn.Linear(embedding_dim, self.internal_dim)self.k_proj = nn.Linear(embedding_dim, self.internal_dim)self.v_proj = nn.Linear(embedding_dim, self.internal_dim)self.out_proj = nn.Linear(self.internal_dim, embedding_dim)def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:b, n, c = x.shapex = x.reshape(b, n, num_heads, c // num_heads)return x.transpose(1, 2)  # B x N_heads x N_tokens x C_per_headdef _recombine_heads(self, x: Tensor) -> Tensor:b, n_heads, n_tokens, c_per_head = x.shapex = x.transpose(1, 2)return x.reshape(b, n_tokens, n_heads * c_per_head)  # B x N_tokens x Cdef forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:# Input projectionsq = self.q_proj(q)k = self.k_proj(k)v = self.v_proj(v)# Separate into heads# B,N_heads,N_tokens,C_per_headq = self._separate_heads(q, self.num_heads)k = self._separate_heads(k, self.num_heads)v = self._separate_heads(v, self.num_heads)# Attention_, _, _, c_per_head = q.shapeattn = q @ k.permute(0, 1, 3, 2)  # B,N_heads,N_tokens,C_per_head# Scaleattn = attn / math.sqrt(c_per_head)attn = torch.softmax(attn, dim=-1)# Get outputout = attn @ v# # B,N_tokens,Cout = self._recombine_heads(out)out = self.out_proj(out)return out

MaskDeco的Attention和ViT的Attention的结构对比示意图：

原论文中Attention部分示意图：

transformer_MLP

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

transformer中MLP的结构对比示意图：

upscaled

# 在MaskDecoder的__init__定义
self.output_upscaling = nn.Sequential(nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),     #转置卷积 上采样2倍LayerNorm2d(transformer_dim // 4),activation(),nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),activation(),
)
# 在MaskDecoder的predict_masks添加位置编码
upscaled_embedding = self.output_upscaling(src)

upscaled的结构对比示意图：

mask_MLP

此处的MLP基础模块不同于ViT的MLP(transformer_MLP)基础模块。

# 在MaskDecoder的__init__定义
self.output_hypernetworks_mlps = nn.ModuleList([MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)for i in range(self.num_mask_tokens)]
)
# 在MaskDecoder的predict_masks添加位置编码for i in range(self.num_mask_tokens):# mask_tokens_out[:, i, :]: B,1,C# output_hypernetworks_mlps: B,1,chyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))# B,n,chyper_in = torch.stack(hyper_in_list, dim=1)b, c, h, w = upscaled_embedding.shape# B,n,c × B,c,N-->B,n,h,wmasks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)

iou_MLP

此处的MLP基础模块不同于ViT的MLP(transformer_MLP)基础模块。

# 在MaskDecoder的__init__定义
self.iou_prediction_head = MLP(transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
)
# 在MaskDecoder的predict_masks添加位置编码
iou_pred = self.iou_prediction_head(iou_token_out)

MaskDeco_MLP

class MLP(nn.Module):def __init__(self,input_dim: int,         # 输入channelhidden_dim: int,        # 中间channeloutput_dim: int,        # 输出channelnum_layers: int,        # fc的层数sigmoid_output: bool = False,) -> None:super().__init__()self.num_layers = num_layersh = [hidden_dim] * (num_layers - 1)self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))self.sigmoid_output = sigmoid_outputdef forward(self, x):for i, layer in enumerate(self.layers):x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)if self.sigmoid_output:x = F.sigmoid(x)return x

MaskDeco中MLP的结构对比示意图：

总结

尽可能简单、详细的介绍SAM中Mask decoder模块的MaskDeco网络的代码。