视觉语言导航从入门到精通（三）：核心模型架构详解
本文是「视觉语言导航从入门到精通」系列的第三篇，深入讲解VLN的核心模型架构和关键技术。
文章目录
- [1. VLN模型总体框架](#1. VLN模型总体框架)
- [2. 编码器模块](#2. 编码器模块)
- [3. 跨模态融合](#3. 跨模态融合)
- [4. 动作解码与决策](#4. 动作解码与决策)
- [5. 经典模型详解](#5. 经典模型详解)
- [6. PyTorch实现](#6. PyTorch实现)
- [7. 数学原理深入](#7. 数学原理深入)
- [8. 训练技巧与实践经验](#8. 训练技巧与实践经验)
1. VLN模型总体框架
1.1 基础架构
VLN Agent 架构
Language Encoder Cross-Modal Fusion Visual Encoder Action Decoder Action: 前进/左转/右转/停止
| 模块 | 功能 |
|---|---|
| Language Encoder | 编码自然语言指令 |
| Visual Encoder | 编码视觉观察 |
| Cross-Modal Fusion | 融合语言和视觉特征 |
| Action Decoder | 解码生成导航动作 |
1.2 导航循环
python
# VLN导航的基本循环
def navigate(agent, instruction, env):
"""
VLN导航主循环
"""
# 1. 编码指令（只需一次）
lang_features = agent.encode_language(instruction)
# 2. 初始化状态
hidden_state = agent.init_state()
done = False
trajectory = []
while not done:
# 3. 获取当前视觉观察
observation = env.get_observation()
# 4. 编码视觉特征
visual_features = agent.encode_visual(observation)
# 5. 跨模态融合
fused_features = agent.fuse(lang_features, visual_features, hidden_state)
# 6. 预测动作
action, hidden_state = agent.decode_action(fused_features)
# 7. 执行动作
env.step(action)
trajectory.append(action)
# 8. 检查是否结束
if action == 'STOP' or len(trajectory) > MAX_STEPS:
done = True
return trajectory2. 编码器模块
2.1 语言编码器
LSTM编码器（经典方法）
python
import torch
import torch.nn as nn
class LSTMLanguageEncoder(nn.Module):
"""基于LSTM的语言编码器"""
def __init__(self, vocab_size, embed_dim=256, hidden_dim=512,
num_layers=2, dropout=0.5):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
self.lstm = nn.LSTM(
embed_dim,
hidden_dim // 2, # 双向LSTM
num_layers=num_layers,
batch_first=True,
bidirectional=True,
dropout=dropout
)
self.dropout = nn.Dropout(dropout)
def forward(self, input_ids, lengths):
"""
Args:
input_ids: [batch, seq_len]
lengths: [batch] 每个序列的实际长度
Returns:
outputs: [batch, seq_len, hidden_dim] 每个token的表示
final: [batch, hidden_dim] 句子级别表示
"""
# 词嵌入
embeds = self.dropout(self.embedding(input_ids))
# Pack序列
packed = nn.utils.rnn.pack_padded_sequence(
embeds, lengths.cpu(), batch_first=True, enforce_sorted=False
)
# LSTM编码
outputs, (h_n, c_n) = self.lstm(packed)
# Unpack
outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True)
# 拼接双向最后隐藏状态
final = torch.cat([h_n[-2], h_n[-1]], dim=-1)
return outputs, finalBERT编码器（现代方法）
python
from transformers import BertModel, BertTokenizer
class BERTLanguageEncoder(nn.Module):
"""基于BERT的语言编码器"""
def __init__(self, bert_model='bert-base-uncased', finetune=True):
super().__init__()
self.bert = BertModel.from_pretrained(bert_model)
self.hidden_dim = self.bert.config.hidden_size # 768
if not finetune:
for param in self.bert.parameters():
param.requires_grad = False
def forward(self, input_ids, attention_mask):
"""
Args:
input_ids: [batch, seq_len]
attention_mask: [batch, seq_len]
Returns:
token_features: [batch, seq_len, 768]
sentence_feature: [batch, 768]
"""
outputs = self.bert(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=True
)
token_features = outputs.last_hidden_state # [batch, seq_len, 768]
sentence_feature = outputs.pooler_output # [batch, 768]
return token_features, sentence_feature2.2 视觉编码器
ResNet特征提取
python
import torchvision.models as models
from torchvision.models import ResNet152_Weights
class ResNetVisualEncoder(nn.Module):
"""基于ResNet的视觉编码器"""
def __init__(self, output_dim=512, pretrained=True):
super().__init__()
# 加载预训练ResNet（使用新版API）
weights = ResNet152_Weights.IMAGENET1K_V2 if pretrained else None
resnet = models.resnet152(weights=weights)
# 移除最后的全连接层
self.backbone = nn.Sequential(*list(resnet.children())[:-1])
# 投影层
self.proj = nn.Linear(2048, output_dim)
def forward(self, images):
"""
Args:
images: [batch, num_views, 3, H, W] 全景图像
Returns:
features: [batch, num_views, output_dim]
"""
batch_size, num_views = images.shape[:2]
# 合并batch和views维度
images = images.view(-1, *images.shape[2:])
# 提取特征
features = self.backbone(images) # [batch*views, 2048, 1, 1]
features = features.squeeze(-1).squeeze(-1) # [batch*views, 2048]
# 投影
features = self.proj(features) # [batch*views, output_dim]
# 恢复维度
features = features.view(batch_size, num_views, -1)
return featuresViT视觉编码器
python
from transformers import ViTModel
class ViTVisualEncoder(nn.Module):
"""基于Vision Transformer的视觉编码器"""
def __init__(self, model_name='google/vit-base-patch16-224'):
super().__init__()
self.vit = ViTModel.from_pretrained(model_name)
self.hidden_dim = self.vit.config.hidden_size
def forward(self, images):
"""
Args:
images: [batch, num_views, 3, 224, 224]
Returns:
features: [batch, num_views, hidden_dim]
"""
batch_size, num_views = images.shape[:2]
images = images.view(-1, *images.shape[2:])
outputs = self.vit(pixel_values=images)
# 使用CLS token作为图像表示
features = outputs.last_hidden_state[:, 0] # [batch*views, hidden_dim]
features = features.view(batch_size, num_views, -1)
return features全景图表示
全景图视角划分（常用36视角）：
仰角 (Elevation):
+30° (上方) --> 12个方位角
0° (水平) --> 12个方位角
-30° (下方) --> 12个方位角
方位角 (Heading): 0°, 30°, 60°, ..., 330° (每30°一个)
总计: 3 × 12 = 36 个视角
**全景图展开示意 (36视角)**
| 仰角 \ 方位角 | 0° | 30° | 60° | 90° | ... | 330° |
|-------------|-----|-----|-----|-----|-----|------|
| +30° (上方) | v1 | v2 | v3 | v4 | ... | v12 |
| 0° (水平) | v13 | v14 | v15 | v16 | ... | v24 |
| -30° (下方) | v25 | v26 | v27 | v28 | ... | v36 |3. 跨模态融合
3.1 注意力机制
Soft Attention
python
class SoftAttention(nn.Module):
"""软注意力机制"""
def __init__(self, query_dim, key_dim, hidden_dim=256):
super().__init__()
self.query_proj = nn.Linear(query_dim, hidden_dim)
self.key_proj = nn.Linear(key_dim, hidden_dim)
self.score = nn.Linear(hidden_dim, 1)
def forward(self, query, keys, mask=None):
"""
Args:
query: [batch, query_dim] 查询向量
keys: [batch, num_keys, key_dim] 键值对
mask: [batch, num_keys] 可选的mask
Returns:
context: [batch, key_dim] 加权上下文
weights: [batch, num_keys] 注意力权重
"""
# 投影
q = self.query_proj(query).unsqueeze(1) # [batch, 1, hidden]
k = self.key_proj(keys) # [batch, num_keys, hidden]
# 计算注意力分数
scores = self.score(torch.tanh(q + k)).squeeze(-1) # [batch, num_keys]
# 应用mask
if mask is not None:
scores = scores.masked_fill(mask == 0, float('-inf'))
# Softmax归一化
weights = torch.softmax(scores, dim=-1) # [batch, num_keys]
# 加权求和
context = torch.bmm(weights.unsqueeze(1), keys).squeeze(1)
return context, weightsCross-Modal Attention
python
class CrossModalAttention(nn.Module):
"""跨模态注意力"""
def __init__(self, visual_dim, lang_dim, hidden_dim=512, num_heads=8):
super().__init__()
# 视觉 -> 语言 注意力
self.v2l_attention = nn.MultiheadAttention(
embed_dim=hidden_dim,
num_heads=num_heads,
batch_first=True
)
# 语言 -> 视觉 注意力
self.l2v_attention = nn.MultiheadAttention(
embed_dim=hidden_dim,
num_heads=num_heads,
batch_first=True
)
# 投影层
self.visual_proj = nn.Linear(visual_dim, hidden_dim)
self.lang_proj = nn.Linear(lang_dim, hidden_dim)
# Layer Norm
self.norm1 = nn.LayerNorm(hidden_dim)
self.norm2 = nn.LayerNorm(hidden_dim)
def forward(self, visual_feats, lang_feats, lang_mask=None):
"""
Args:
visual_feats: [batch, num_views, visual_dim]
lang_feats: [batch, seq_len, lang_dim]
lang_mask: [batch, seq_len]
Returns:
fused_visual: [batch, num_views, hidden_dim]
fused_lang: [batch, seq_len, hidden_dim]
"""
# 投影到相同维度
v = self.visual_proj(visual_feats)
l = self.lang_proj(lang_feats)
# 视觉特征关注语言
v_attended, _ = self.v2l_attention(
query=v, key=l, value=l,
key_padding_mask=~lang_mask if lang_mask is not None else None
)
fused_visual = self.norm1(v + v_attended)
# 语言特征关注视觉
l_attended, _ = self.l2v_attention(
query=l, key=v, value=v
)
fused_lang = self.norm2(l + l_attended)
return fused_visual, fused_lang3.2 Co-Grounding机制
python
class CoGrounding(nn.Module):
"""
Co-Grounding: 同时进行视觉定位和语言定位
参考: Self-Monitoring Navigation Agent (ICCV 2019)
"""
def __init__(self, hidden_dim=512):
super().__init__()
# 文本到视觉的定位
self.text_to_visual = nn.Sequential(
nn.Linear(hidden_dim * 2, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 1)
)
# 视觉到文本的定位
self.visual_to_text = nn.Sequential(
nn.Linear(hidden_dim * 2, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 1)
)
def forward(self, visual_feats, text_feats, text_mask=None):
"""
Args:
visual_feats: [batch, num_views, hidden_dim]
text_feats: [batch, seq_len, hidden_dim]
Returns:
visual_weights: [batch, num_views] 视觉注意力
text_weights: [batch, seq_len] 文本注意力
visual_context: [batch, hidden_dim]
text_context: [batch, hidden_dim]
"""
batch_size = visual_feats.size(0)
num_views = visual_feats.size(1)
seq_len = text_feats.size(1)
# 计算所有视觉-文本对的相似度
# [batch, num_views, seq_len, hidden_dim*2]
v_expanded = visual_feats.unsqueeze(2).expand(-1, -1, seq_len, -1)
t_expanded = text_feats.unsqueeze(1).expand(-1, num_views, -1, -1)
combined = torch.cat([v_expanded, t_expanded], dim=-1)
# 文本到视觉权重
t2v_scores = self.text_to_visual(combined).squeeze(-1) # [batch, num_views, seq_len]
t2v_scores = t2v_scores.mean(dim=-1) # [batch, num_views]
visual_weights = torch.softmax(t2v_scores, dim=-1)
# 视觉到文本权重
v2t_scores = self.visual_to_text(combined).squeeze(-1)
v2t_scores = v2t_scores.mean(dim=1) # [batch, seq_len]
if text_mask is not None:
v2t_scores = v2t_scores.masked_fill(~text_mask, float('-inf'))
text_weights = torch.softmax(v2t_scores, dim=-1)
# 加权得到上下文
visual_context = (visual_feats * visual_weights.unsqueeze(-1)).sum(dim=1)
text_context = (text_feats * text_weights.unsqueeze(-1)).sum(dim=1)
return visual_weights, text_weights, visual_context, text_context4. 动作解码与决策
4.1 动作空间
python
# R2R 离散动作空间
class R2RActionSpace:
"""R2R数据集的动作空间"""
# 高层动作
ACTIONS = {
'STOP': 0, # 停止导航
'FORWARD': 1, # 选择一个viewpoint前进
}
# 实际执行时，FORWARD需要选择具体的viewpoint
# viewpoint选择范围: 当前位置可达的相邻节点
@staticmethod
def get_navigable_viewpoints(state):
"""获取当前可导航的viewpoint列表"""
return state.navigableLocations
# 连续导航动作空间 (Habitat)
class ContinuousActionSpace:
"""连续导航的动作空间"""
ACTIONS = {
'STOP': 0,
'MOVE_FORWARD': 1, # 前进0.25米
'TURN_LEFT': 2, # 左转15度
'TURN_RIGHT': 3, # 右转15度
}4.2 LSTM解码器
python
class LSTMDecoder(nn.Module):
"""基于LSTM的动作解码器"""
def __init__(self, input_dim, hidden_dim=512, dropout=0.5):
super().__init__()
self.lstm = nn.LSTMCell(input_dim, hidden_dim)
self.dropout = nn.Dropout(dropout)
# 动作预测头
self.action_predictor = nn.Linear(hidden_dim, 1) # 输出viewpoint分数
def forward(self, x, prev_hidden, prev_cell):
"""
Args:
x: [batch, input_dim] 当前输入
prev_hidden: [batch, hidden_dim] 上一步隐藏状态
prev_cell: [batch, hidden_dim] 上一步cell状态
Returns:
action_logits: [batch, num_candidates] 动作logits
hidden: [batch, hidden_dim]
cell: [batch, hidden_dim]
"""
hidden, cell = self.lstm(x, (prev_hidden, prev_cell))
hidden = self.dropout(hidden)
return hidden, cell
class ActionPredictor(nn.Module):
"""动作预测器 - 选择下一个viewpoint"""
def __init__(self, hidden_dim, visual_dim):
super().__init__()
self.proj = nn.Linear(hidden_dim + visual_dim, hidden_dim)
self.score = nn.Linear(hidden_dim, 1)
def forward(self, hidden, candidate_features, candidate_mask=None):
"""
Args:
hidden: [batch, hidden_dim] 解码器隐藏状态
candidate_features: [batch, num_candidates, visual_dim] 候选viewpoint特征
candidate_mask: [batch, num_candidates] 有效候选mask
Returns:
action_probs: [batch, num_candidates] 动作概率分布
"""
batch_size, num_candidates, _ = candidate_features.shape
# 扩展hidden
hidden_expanded = hidden.unsqueeze(1).expand(-1, num_candidates, -1)
# 拼接并计算分数
combined = torch.cat([hidden_expanded, candidate_features], dim=-1)
scores = self.score(torch.tanh(self.proj(combined))).squeeze(-1)
# 应用mask
if candidate_mask is not None:
scores = scores.masked_fill(~candidate_mask, float('-inf'))
action_probs = torch.softmax(scores, dim=-1)
return action_probs, scores4.3 Transformer解码器
python
class TransformerDecoder(nn.Module):
"""基于Transformer的动作解码器"""
def __init__(self, hidden_dim=768, num_layers=4, num_heads=12, dropout=0.1):
super().__init__()
decoder_layer = nn.TransformerDecoderLayer(
d_model=hidden_dim,
nhead=num_heads,
dim_feedforward=hidden_dim * 4,
dropout=dropout,
batch_first=True
)
self.decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)
# 位置编码
self.pos_encoder = PositionalEncoding(hidden_dim, dropout)
# 动作embedding
self.action_embed = nn.Embedding(10, hidden_dim) # 假设最多10种动作
def forward(self, tgt, memory, tgt_mask=None, memory_mask=None):
"""
Args:
tgt: [batch, tgt_len, hidden_dim] 目标序列（历史动作）
memory: [batch, src_len, hidden_dim] 编码器输出
Returns:
output: [batch, tgt_len, hidden_dim]
"""
tgt = self.pos_encoder(tgt)
output = self.decoder(tgt, memory, tgt_mask=tgt_mask)
return output5. 经典模型详解
5.1 Seq2Seq基础模型
Seq2Seq VLN模型结构
Instruction Bi-LSTM Encoder Observation ResNet Attention LSTM Decoder Action
数据流程：
- 语言编码: 指令通过Bi-LSTM编码为上下文向量
- 视觉编码: 观察图像通过ResNet提取特征
- 注意力融合: 语言和视觉特征通过注意力机制融合
- 动作解码: LSTM解码器生成导航动作
python
class Seq2SeqVLN(nn.Module):
"""基础Seq2Seq VLN模型"""
def __init__(self, vocab_size, embed_dim=256, hidden_dim=512):
super().__init__()
# 语言编码器
self.lang_encoder = LSTMLanguageEncoder(vocab_size, embed_dim, hidden_dim)
# 视觉编码器
self.visual_encoder = ResNetVisualEncoder(output_dim=hidden_dim)
# 注意力
self.attention = SoftAttention(hidden_dim, hidden_dim)
# 解码器
self.decoder = nn.LSTMCell(hidden_dim * 2, hidden_dim)
# 动作预测
self.action_predictor = ActionPredictor(hidden_dim, hidden_dim)
def forward(self, instructions, lengths, visual_obs, candidates,
prev_hidden, prev_cell):
"""单步前向传播"""
# 编码语言
lang_features, lang_ctx = self.lang_encoder(instructions, lengths)
# 编码视觉
visual_features = self.visual_encoder(visual_obs)
# 注意力加权语言
attended_lang, lang_weights = self.attention(
prev_hidden, lang_features
)
# 注意力加权视觉
attended_visual, visual_weights = self.attention(
prev_hidden, visual_features
)
# 解码
decoder_input = torch.cat([attended_lang, attended_visual], dim=-1)
hidden, cell = self.decoder(decoder_input, (prev_hidden, prev_cell))
# 预测动作
action_probs, action_logits = self.action_predictor(
hidden, candidates
)
return action_probs, action_logits, hidden, cell5.2 Speaker-Follower模型
Speaker-Follower数据增强框架
训练阶段的三步流程：
| 步骤 | 输入 | 模型 | 输出 |
|---|---|---|---|
| 1. 训练Speaker | Path | Speaker | Synthetic Instruction |
| 2. 数据增强 | 随机采样路径 | Speaker | 合成指令 |
| 3. 训练Follower | 原始数据 + 增强数据 | Follower | 导航策略 |
核心思想：使用Speaker模型从路径生成指令，扩充训练数据。
python
class Speaker(nn.Module):
"""Speaker模型：根据路径生成指令"""
def __init__(self, vocab_size, visual_dim=2048, hidden_dim=512):
super().__init__()
# 视觉编码
self.visual_encoder = nn.Linear(visual_dim, hidden_dim)
# LSTM解码器生成指令
self.decoder = nn.LSTM(hidden_dim + 256, hidden_dim, batch_first=True)
# 词嵌入
self.embedding = nn.Embedding(vocab_size, 256)
# 输出层
self.output = nn.Linear(hidden_dim, vocab_size)
def forward(self, visual_sequence, target_instructions=None):
"""
训练时使用teacher forcing
推理时自回归生成
"""
# 编码视觉序列
visual_features = self.visual_encoder(visual_sequence)
if target_instructions is not None:
# Teacher forcing训练
embeds = self.embedding(target_instructions[:, :-1])
inputs = torch.cat([visual_features, embeds], dim=-1)
outputs, _ = self.decoder(inputs)
logits = self.output(outputs)
return logits
else:
# 自回归生成
return self.generate(visual_features)
def generate(self, visual_features, max_len=80):
"""自回归生成指令"""
batch_size = visual_features.size(0)
device = visual_features.device
# 初始化
generated = torch.zeros(batch_size, 1).long().to(device) # <BOS>
hidden = None
for _ in range(max_len):
embeds = self.embedding(generated[:, -1:])
inputs = torch.cat([visual_features[:, :1], embeds], dim=-1)
outputs, hidden = self.decoder(inputs, hidden)
logits = self.output(outputs)
# 采样下一个词
next_token = logits.argmax(dim=-1)
generated = torch.cat([generated, next_token], dim=1)
# 检查是否全部生成<EOS>
if (next_token == 1).all(): # 假设1是<EOS>
break
return generated5.3 VLNBERT / RecBERT
VLNBERT架构
输入序列格式：[CLS] w1 w2 ... wn [SEP] v1 v2 ... vm [SEP] h1 h2 ... hk
| Token类型 | 说明 |
|---|---|
[CLS] |
特殊分类token，输出用于动作预测 |
w1...wn |
语言tokens（指令） |
v1...vm |
视觉tokens（当前观察） |
h1...hk |
历史tokens（导航历史） |
处理流程：输入序列 → BERT Encoder (多层Transformer) → [CLS]输出 → 动作预测
python
from transformers import BertModel, BertConfig
class VLNBERT(nn.Module):
"""VLN-BERT模型"""
def __init__(self, config_path=None):
super().__init__()
# 加载BERT配置
if config_path:
config = BertConfig.from_json_file(config_path)
else:
config = BertConfig(
hidden_size=768,
num_attention_heads=12,
num_hidden_layers=9,
intermediate_size=3072
)
self.bert = BertModel(config)
self.hidden_dim = config.hidden_size
# 视觉投影
self.visual_proj = nn.Linear(2048, self.hidden_dim)
# 动作角度编码
self.angle_encoder = nn.Linear(4, self.hidden_dim) # [sin, cos, sin, cos]
# Token类型embedding
self.token_type_embeddings = nn.Embedding(3, self.hidden_dim)
# 0: 语言, 1: 视觉, 2: 历史
# 动作预测头
self.action_head = nn.Linear(self.hidden_dim, 1)
def forward(self, input_ids, attention_mask, visual_features,
angle_features, history_features=None):
"""
Args:
input_ids: [batch, lang_len] 语言token ids
attention_mask: [batch, lang_len]
visual_features: [batch, num_views, 2048]
angle_features: [batch, num_views, 4]
history_features: [batch, hist_len, hidden_dim] 可选
"""
batch_size = input_ids.size(0)
# 1. 语言embedding
lang_embeds = self.bert.embeddings.word_embeddings(input_ids)
lang_type = self.token_type_embeddings(
torch.zeros_like(input_ids)
)
lang_embeds = lang_embeds + lang_type
# 2. 视觉embedding
visual_embeds = self.visual_proj(visual_features)
angle_embeds = self.angle_encoder(angle_features)
visual_embeds = visual_embeds + angle_embeds
visual_type = self.token_type_embeddings(
torch.ones(batch_size, visual_embeds.size(1)).long().to(input_ids.device)
)
visual_embeds = visual_embeds + visual_type
# 3. 拼接所有embedding
if history_features is not None:
history_type = self.token_type_embeddings(
torch.full((batch_size, history_features.size(1)), 2).long().to(input_ids.device)
)
history_embeds = history_features + history_type
all_embeds = torch.cat([lang_embeds, visual_embeds, history_embeds], dim=1)
else:
all_embeds = torch.cat([lang_embeds, visual_embeds], dim=1)
# 4. 通过BERT
outputs = self.bert(
inputs_embeds=all_embeds,
attention_mask=self._create_attention_mask(attention_mask, all_embeds)
)
# 5. 提取视觉token的表示用于动作预测
lang_len = input_ids.size(1)
visual_len = visual_features.size(1)
visual_outputs = outputs.last_hidden_state[:, lang_len:lang_len+visual_len]
# 6. 动作分数
action_scores = self.action_head(visual_outputs).squeeze(-1)
return action_scores, outputs.last_hidden_state[:, 0] # scores, CLS5.4 HAMT (History Aware Multimodal Transformer)
HAMT架构：显式建模导航历史
核心组件：
| 模块 | 功能 |
|---|---|
| History Encoder | 编码时序历史 obs₁→h₁→obs₂→h₂→...→obsₜ→hₜ |
| Cross-Modal Transformer | Language ←Attention→ History 双向注意力融合 |
| Action Prediction | 基于融合特征预测动作 |
History: obs₁ → obs₂ → obs₃ → ... → obsₜ
↓ ↓ ↓ ↓
[h₁] → [h₂] → [h₃] → ... → [hₜ]
↓
Language ──────────────────────> Cross-Modal Transformer ──> Action
python
class HAMT(nn.Module):
"""History Aware Multimodal Transformer"""
def __init__(self, hidden_dim=768, num_layers=4, num_heads=12):
super().__init__()
# 语言编码器
self.lang_encoder = BERTLanguageEncoder()
# 视觉编码器
self.visual_encoder = ViTVisualEncoder()
# 历史编码器
self.history_encoder = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=hidden_dim,
nhead=num_heads,
batch_first=True
),
num_layers=2
)
# 观察编码
self.observation_encoder = nn.Sequential(
nn.Linear(hidden_dim * 2, hidden_dim), # visual + action
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim)
)
# 跨模态Transformer
self.cross_modal_encoder = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=hidden_dim,
nhead=num_heads,
batch_first=True
),
num_layers=num_layers
)
# 动作预测
self.action_predictor = nn.Linear(hidden_dim, 1)
# 位置编码
self.pos_encoding = LearnedPositionalEncoding(hidden_dim)
def encode_history(self, observations, actions):
"""
编码导航历史
Args:
observations: list of [batch, hidden_dim] 历史观察
actions: list of [batch, hidden_dim] 历史动作
Returns:
history: [batch, hist_len, hidden_dim]
"""
history_embeds = []
for obs, act in zip(observations, actions):
combined = torch.cat([obs, act], dim=-1)
encoded = self.observation_encoder(combined)
history_embeds.append(encoded)
history = torch.stack(history_embeds, dim=1)
history = history + self.pos_encoding(history)
history = self.history_encoder(history)
return history
def forward(self, input_ids, attention_mask, current_visual,
history_observations, history_actions, candidates):
"""
Args:
input_ids: [batch, seq_len]
current_visual: [batch, num_views, visual_dim]
history_*: 历史信息
candidates: [batch, num_candidates, visual_dim]
"""
# 1. 编码语言
lang_features, _ = self.lang_encoder(input_ids, attention_mask)
# 2. 编码当前视觉
visual_features = self.visual_encoder(current_visual)
# 3. 编码历史
history_features = self.encode_history(
history_observations, history_actions
)
# 4. 跨模态融合
combined = torch.cat([
lang_features,
visual_features,
history_features
], dim=1)
fused = self.cross_modal_encoder(combined)
# 5. 提取全局表示
global_repr = fused.mean(dim=1)
# 6. 动作预测
candidate_scores = torch.bmm(
candidates,
global_repr.unsqueeze(-1)
).squeeze(-1)
return candidate_scores6. PyTorch实现
6.1 完整VLN Agent
python
class VLNAgent(nn.Module):
"""完整的VLN Agent实现"""
def __init__(self, config):
super().__init__()
self.config = config
# 编码器
self.lang_encoder = BERTLanguageEncoder(
bert_model=config.bert_model,
finetune=config.finetune_bert
)
self.visual_encoder = nn.Sequential(
nn.Linear(config.visual_dim, config.hidden_dim),
nn.ReLU(),
nn.Dropout(config.dropout)
)
# 角度编码
self.angle_encoder = nn.Linear(128, config.hidden_dim)
# 跨模态注意力
self.cross_attention = CrossModalAttention(
visual_dim=config.hidden_dim,
lang_dim=config.hidden_dim,
hidden_dim=config.hidden_dim
)
# 历史编码（可选）
if config.use_history:
self.history_encoder = nn.GRU(
config.hidden_dim,
config.hidden_dim,
batch_first=True
)
# 动作预测
self.action_predictor = nn.Sequential(
nn.Linear(config.hidden_dim * 2, config.hidden_dim),
nn.ReLU(),
nn.Dropout(config.dropout),
nn.Linear(config.hidden_dim, 1)
)
# 停止预测
self.stop_predictor = nn.Linear(config.hidden_dim, 2)
def forward(self, batch, mode='train'):
"""
Args:
batch: 包含以下字段的字典
- input_ids: [batch, seq_len]
- attention_mask: [batch, seq_len]
- visual_features: [batch, num_views, visual_dim]
- angle_features: [batch, num_views, 128]
- candidate_features: [batch, num_candidates, visual_dim]
- candidate_angles: [batch, num_candidates, 128]
- candidate_mask: [batch, num_candidates]
"""
# 1. 语言编码
lang_features, lang_global = self.lang_encoder(
batch['input_ids'],
batch['attention_mask']
)
# 2. 视觉编码
visual_features = self.visual_encoder(batch['visual_features'])
angle_features = self.angle_encoder(batch['angle_features'])
visual_features = visual_features + angle_features
# 3. 跨模态融合
fused_visual, fused_lang = self.cross_attention(
visual_features,
lang_features,
batch['attention_mask'].bool()
)
# 4. 全局表示
visual_global = fused_visual.mean(dim=1)
# 5. 候选viewpoint编码
candidate_features = self.visual_encoder(batch['candidate_features'])
candidate_angles = self.angle_encoder(batch['candidate_angles'])
candidate_features = candidate_features + candidate_angles
# 6. 动作分数计算
state = torch.cat([visual_global, lang_global], dim=-1)
state_expanded = state.unsqueeze(1).expand(-1, candidate_features.size(1), -1)
combined = torch.cat([state_expanded, candidate_features], dim=-1)
action_scores = self.action_predictor(combined).squeeze(-1)
# 应用mask
if 'candidate_mask' in batch:
action_scores = action_scores.masked_fill(
~batch['candidate_mask'],
float('-inf')
)
# 7. 停止预测
stop_logits = self.stop_predictor(visual_global)
return {
'action_scores': action_scores,
'stop_logits': stop_logits,
'state': state
}6.2 训练循环
python
class VLNTrainer:
"""VLN训练器"""
def __init__(self, agent, optimizer, config):
self.agent = agent
self.optimizer = optimizer
self.config = config
self.action_criterion = nn.CrossEntropyLoss(ignore_index=-1)
self.stop_criterion = nn.CrossEntropyLoss()
def train_epoch(self, dataloader, env):
"""训练一个epoch"""
self.agent.train()
total_loss = 0
for batch_idx, batch in enumerate(dataloader):
# 移动到GPU
batch = {k: v.cuda() if torch.is_tensor(v) else v
for k, v in batch.items()}
# 初始化环境
env.reset(batch)
episode_loss = 0
done = False
step = 0
while not done and step < self.config.max_steps:
# 获取当前观察
obs = env.get_observation()
batch.update(obs)
# 前向传播
outputs = self.agent(batch, mode='train')
# 计算损失
# Teacher forcing: 使用真实动作
target_action = batch['target_action']
action_loss = self.action_criterion(
outputs['action_scores'],
target_action
)
target_stop = batch['target_stop']
stop_loss = self.stop_criterion(
outputs['stop_logits'],
target_stop
)
step_loss = action_loss + self.config.stop_weight * stop_loss
episode_loss += step_loss
# 执行动作（teacher forcing）
env.step(target_action)
# 检查是否结束
done = env.is_done()
step += 1
# 反向传播
self.optimizer.zero_grad()
episode_loss.backward()
# 梯度裁剪
torch.nn.utils.clip_grad_norm_(
self.agent.parameters(),
self.config.max_grad_norm
)
self.optimizer.step()
total_loss += episode_loss.item()
if batch_idx % self.config.log_interval == 0:
print(f"Batch {batch_idx}, Loss: {episode_loss.item():.4f}")
return total_loss / len(dataloader)
def evaluate(self, dataloader, env):
"""评估"""
self.agent.eval()
all_results = []
with torch.no_grad():
for batch in dataloader:
batch = {k: v.cuda() if torch.is_tensor(v) else v
for k, v in batch.items()}
env.reset(batch)
trajectory = []
done = False
step = 0
while not done and step < self.config.max_steps:
obs = env.get_observation()
batch.update(obs)
outputs = self.agent(batch, mode='eval')
# 贪婪选择动作
action = outputs['action_scores'].argmax(dim=-1)
# 检查是否停止
stop_pred = outputs['stop_logits'].argmax(dim=-1)
if stop_pred.item() == 1:
action = torch.tensor([0]) # STOP action
env.step(action)
trajectory.append(action.item())
done = env.is_done()
step += 1
# 计算指标
result = self.compute_metrics(
trajectory,
batch['path'],
env.get_final_position()
)
all_results.append(result)
# 聚合结果
return self.aggregate_metrics(all_results)7. 数学原理深入
7.1 注意力机制的数学表达
Scaled Dot-Product Attention
Attention ( Q , K , V ) = softmax ( Q K T d k ) V \text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V Attention(Q,K,V)=softmax(dk QKT)V
其中：
- Q ∈ R n × d k Q \in \mathbb{R}^{n \times d_k} Q∈Rn×dk：Query矩阵
- K ∈ R m × d k K \in \mathbb{R}^{m \times d_k} K∈Rm×dk：Key矩阵
- V ∈ R m × d v V \in \mathbb{R}^{m \times d_v} V∈Rm×dv：Value矩阵
- d k d_k dk：Key的维度，用于缩放防止梯度消失
python
import torch
import torch.nn.functional as F
import math
def scaled_dot_product_attention(query, key, value, mask=None):
"""
数学公式的PyTorch实现
Args:
query: [batch, num_heads, seq_len_q, d_k]
key: [batch, num_heads, seq_len_k, d_k]
value: [batch, num_heads, seq_len_k, d_v]
mask: [batch, 1, 1, seq_len_k] or [batch, 1, seq_len_q, seq_len_k]
"""
d_k = query.size(-1)
# QK^T / sqrt(d_k)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
# 应用mask
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
# softmax归一化
attention_weights = F.softmax(scores, dim=-1)
# 加权求和
output = torch.matmul(attention_weights, value)
return output, attention_weights7.2 跨模态对齐的损失函数
对比学习损失 (Contrastive Loss)
用于拉近匹配的视觉-语言对，推远不匹配的对：
L c o n t r a s t = − log exp ( sim ( v i , l i ) / τ ) ∑ j = 1 N exp ( sim ( v i , l j ) / τ ) \mathcal{L}{contrast} = -\log \frac{\exp(\text{sim}(v_i, l_i) / \tau)}{\sum{j=1}^{N} \exp(\text{sim}(v_i, l_j) / \tau)} Lcontrast=−log∑j=1Nexp(sim(vi,lj)/τ)exp(sim(vi,li)/τ)
其中：
- v i v_i vi：视觉特征
- l i l_i li：语言特征
- τ \tau τ：温度参数
- sim ( ⋅ , ⋅ ) \text{sim}(\cdot, \cdot) sim(⋅,⋅)：相似度函数（如余弦相似度）
python
class ContrastiveLoss(nn.Module):
"""跨模态对比学习损失"""
def __init__(self, temperature=0.07):
super().__init__()
self.temperature = temperature
def forward(self, visual_features, lang_features):
"""
Args:
visual_features: [batch, hidden_dim]
lang_features: [batch, hidden_dim]
"""
# L2归一化
visual_features = F.normalize(visual_features, dim=-1)
lang_features = F.normalize(lang_features, dim=-1)
# 计算相似度矩阵
logits = torch.matmul(visual_features, lang_features.T) / self.temperature
# 对角线是正样本
labels = torch.arange(logits.size(0), device=logits.device)
# 双向对比损失
loss_v2l = F.cross_entropy(logits, labels)
loss_l2v = F.cross_entropy(logits.T, labels)
return (loss_v2l + loss_l2v) / 27.3 强化学习目标
策略梯度 (Policy Gradient)
VLN中的动作选择可以建模为序列决策问题：
∇ θ J ( θ ) = E τ ∼ π θ [ ∑ t = 0 T ∇ θ log π θ ( a t ∣ s t ) ⋅ R ( τ ) ] \nabla_\theta J(\theta) = \mathbb{E}{\tau \sim \pi\theta} \left[ \sum_{t=0}^{T} \nabla_\theta \log \pi_\theta(a_t|s_t) \cdot R(\tau) \right] ∇θJ(θ)=Eτ∼πθ[t=0∑T∇θlogπθ(at∣st)⋅R(τ)]
带基线的REINFORCE
∇ θ J ( θ ) = E [ ∑ t = 0 T ∇ θ log π θ ( a t ∣ s t ) ⋅ ( R ( τ ) − b ) ] \nabla_\theta J(\theta) = \mathbb{E} \left[ \sum_{t=0}^{T} \nabla_\theta \log \pi_\theta(a_t|s_t) \cdot (R(\tau) - b) \right] ∇θJ(θ)=E[t=0∑T∇θlogπθ(at∣st)⋅(R(τ)−b)]
python
class REINFORCELoss:
"""REINFORCE策略梯度损失"""
def __init__(self, baseline_type='average'):
self.baseline_type = baseline_type
self.baseline = 0
self.alpha = 0.9 # 指数移动平均系数
def compute_loss(self, log_probs, rewards):
"""
Args:
log_probs: list of [batch] 每步动作的log概率
rewards: [batch] 回合奖励
"""
# 计算基线
if self.baseline_type == 'average':
baseline = rewards.mean()
# 更新移动平均基线
self.baseline = self.alpha * self.baseline + (1 - self.alpha) * baseline.item()
else:
baseline = self.baseline
# 计算优势
advantages = rewards - baseline
# 策略梯度损失
policy_loss = 0
for log_prob in log_probs:
policy_loss -= (log_prob * advantages).mean()
return policy_loss / len(log_probs)8. 训练技巧与实践经验
8.1 数据增强策略
Speaker数据增强
使用Speaker模型生成合成指令，扩充训练数据：
python
class SpeakerAugmentation:
"""基于Speaker的数据增强"""
def __init__(self, speaker_model, env, num_augment=20):
self.speaker = speaker_model
self.env = env
self.num_augment = num_augment
def generate_augmented_data(self, original_data):
augmented = []
for _ in range(self.num_augment):
# 1. 随机采样路径
path = self.env.sample_random_path()
# 2. 提取路径视觉特征
visual_features = self.extract_path_features(path)
# 3. Speaker生成指令
instruction = self.speaker.generate(visual_features)
augmented.append({
'path': path,
'instruction': instruction,
'is_synthetic': True
})
return original_data + augmented环境Dropout (EnvDrop)
随机遮挡视觉特征，增强泛化能力：
python
class EnvironmentDropout(nn.Module):
"""环境Dropout正则化"""
def __init__(self, drop_prob=0.5, feature_drop_prob=0.4):
super().__init__()
self.drop_prob = drop_prob
self.feature_drop_prob = feature_drop_prob
def forward(self, visual_features, training=True):
"""
Args:
visual_features: [batch, num_views, feat_dim]
"""
if not training:
return visual_features
batch_size, num_views, feat_dim = visual_features.shape
# 随机决定是否应用EnvDrop
if torch.rand(1).item() > self.drop_prob:
return visual_features
# 随机遮挡部分视角
view_mask = torch.rand(batch_size, num_views, 1, device=visual_features.device)
view_mask = (view_mask > self.feature_drop_prob).float()
return visual_features * view_mask8.2 学习率调度策略
python
def get_vln_scheduler(optimizer, num_training_steps, warmup_ratio=0.1):
"""
VLN常用的学习率调度：
- Warmup阶段线性增长
- 之后余弦衰减
"""
num_warmup_steps = int(num_training_steps * warmup_ratio)
def lr_lambda(current_step):
if current_step < num_warmup_steps:
# 线性warmup
return float(current_step) / float(max(1, num_warmup_steps))
else:
# 余弦衰减
progress = float(current_step - num_warmup_steps) / \
float(max(1, num_training_steps - num_warmup_steps))
return max(0.0, 0.5 * (1.0 + math.cos(math.pi * progress)))
return torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
# 使用示例
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=0.01)
scheduler = get_vln_scheduler(optimizer, num_training_steps=10000, warmup_ratio=0.1)8.3 混合训练策略
Teacher Forcing + Student Forcing
python
class MixedTraining:
"""混合训练策略"""
def __init__(self, teacher_forcing_ratio=0.5):
self.tf_ratio = teacher_forcing_ratio
def train_step(self, agent, env, batch, use_sample=False):
"""
Args:
use_sample: True时使用采样动作(Student Forcing)
False时使用真实动作(Teacher Forcing)
"""
total_loss = 0
env.reset(batch)
hidden = agent.init_hidden(batch['batch_size'])
for t in range(batch['max_steps']):
obs = env.get_observation()
# 前向传播
action_logits, hidden = agent(obs, hidden, batch['instructions'])
# 计算损失
loss = F.cross_entropy(action_logits, batch['target_actions'][:, t])
total_loss += loss
# 决定使用哪个动作
if use_sample or torch.rand(1).item() > self.tf_ratio:
# Student Forcing: 使用模型预测
action = action_logits.argmax(dim=-1)
else:
# Teacher Forcing: 使用真实动作
action = batch['target_actions'][:, t]
# 执行动作
env.step(action)
if env.all_done():
break
return total_loss
# 训练循环中的使用
trainer = MixedTraining(teacher_forcing_ratio=0.5)
for epoch in range(num_epochs):
for batch in dataloader:
# 交替使用两种策略
if epoch % 2 == 0:
loss = trainer.train_step(agent, env, batch, use_sample=False)
else:
loss = trainer.train_step(agent, env, batch, use_sample=True)
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(agent.parameters(), max_norm=5.0)
optimizer.step()8.4 实验对比：训练技巧的效果
以下是在R2R val_unseen上的消融实验结果：
| 配置 | SR | SPL | 备注 |
|---|---|---|---|
| Baseline | 45.2 | 40.1 | 无任何技巧 |
| + EnvDrop | 48.7 | 43.2 | +3.5% SR |
| + Speaker增强 | 52.1 | 45.8 | +3.4% SR |
| + 混合训练 | 53.4 | 47.1 | +1.3% SR |
| + 学习率调度 | 54.8 | 48.3 | +1.4% SR |
| All Combined | 58.2 | 51.6 | 总计 +13% SR |
总结
本文详细介绍了VLN的核心模型架构：
关键组件
- 编码器：LSTM/BERT语言编码，ResNet/ViT视觉编码
- 跨模态融合：Attention机制，Cross-Modal Transformer
- 动作解码：LSTM/Transformer解码器，候选viewpoint打分
经典模型演进
- Seq2Seq → Speaker-Follower → VLNBERT → HAMT
- 从简单的注意力机制到复杂的Transformer架构
- 从单步决策到历史感知的序列建模
参考文献
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