feat: add mamba and dynamic chunking related code and test code

generate_sine_data.py

@@ -0,0 +1,102 @@
+import numpy as np
+import pandas as pd
+import os
+
+def generate_sine_wave_data(n_samples=10000, seq_len=200, n_channels=2, save_path='./data/sine_wave/'):
+    """
+    生成双通道正弦波时序数据
+    
+    Args:
+        n_samples: 总样本数
+        seq_len: 每个序列长度
+        n_channels: 通道数 (固定为2)
+        save_path: 保存路径
+    """
+    
+    if not os.path.exists(save_path):
+        os.makedirs(save_path)
+    
+    # 生成时间轴
+    t = np.linspace(0, 4*np.pi, seq_len)
+    
+    all_data = []
+    
+    for i in range(n_samples):
+        # 为每个样本生成不同周期和相位的正弦波
+        # 通道1: 随机周期和相位
+        freq1 = np.random.uniform(0.5, 3.0)  # 频率范围
+        phase1 = np.random.uniform(0, 2*np.pi)  # 相位
+        amplitude1 = np.random.uniform(0.5, 2.0)  # 幅度
+        
+        # 通道2: 不同的随机周期和相位
+        freq2 = np.random.uniform(0.3, 2.5)
+        phase2 = np.random.uniform(0, 2*np.pi)
+        amplitude2 = np.random.uniform(0.8, 1.8)
+        
+        # 生成正弦波数据
+        channel1 = amplitude1 * np.sin(freq1 * t + phase1)
+        channel2 = amplitude2 * np.sin(freq2 * t + phase2)
+        
+        # 添加少量噪声
+        # noise1 = np.random.normal(0, 0.1, seq_len)
+        # noise2 = np.random.normal(0, 0.1, seq_len)
+        # 
+        # channel1 += noise1
+        # channel2 += noise2
+        
+        # 组合数据: [timestamp, channel1, channel2]
+        timestamp = np.arange(seq_len)
+        sample_data = np.column_stack([timestamp, channel1, channel2])
+        all_data.append(sample_data)
+    
+    # 转换为连续的时间序列格式
+    continuous_data = []
+    current_time = 0
+    
+    for sample in all_data:
+        sample[:, 0] = current_time + sample[:, 0]  # 调整时间戳
+        continuous_data.append(sample)
+        current_time += seq_len
+    
+    # 合并所有数据
+    full_data = np.vstack(continuous_data)
+    
+    # 创建DataFrame
+    df = pd.DataFrame(full_data, columns=['timestamp', 'channel1', 'channel2'])
+    
+    # 按 8:1:1 比例分割训练、验证、测试集
+    total_len = len(df)
+    train_end = int(0.8 * total_len)
+    val_end = int(0.9 * total_len)
+    
+    train_df = df[:train_end]
+    val_df = df[train_end:val_end]
+    test_df = df[val_end:]
+    
+    # 保存数据
+    train_df.to_csv(os.path.join(save_path, 'train.csv'), index=False)
+    val_df.to_csv(os.path.join(save_path, 'val.csv'), index=False)
+    test_df.to_csv(os.path.join(save_path, 'test.csv'), index=False)
+    
+    # 保存完整数据
+    df.to_csv(os.path.join(save_path, 'sine_wave.csv'), index=False)
+    
+    print(f"数据已生成并保存到 {save_path}")
+    print(f"训练集: {len(train_df)} 条记录")
+    print(f"验证集: {len(val_df)} 条记录") 
+    print(f"测试集: {len(test_df)} 条记录")
+    print(f"总计: {len(df)} 条记录，{n_channels} 个通道")
+    
+    return df
+
+if __name__ == "__main__":
+    # 生成数据
+    data = generate_sine_wave_data(
+        n_samples=200,  # 2000个不同的正弦波样本
+        seq_len=200,     # 每个样本200个时间点
+        n_channels=2,    # 双通道
+        save_path='./data/sine_wave/'
+    )
+    
+    print("\n数据统计信息:")
+    print(data.describe())

layers/DynamicChunking.py

@@ -0,0 +1,440 @@
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from einops import repeat, rearrange
+
+from mamba_ssm.ops.triton.ssd_combined import mamba_chunk_scan_combined
+
+
+
+
+
+@dataclass
+class RoutingModuleOutput:
+    # 路由模块的前向输出：
+    # - boundary_prob: 每个位置为“分隔点/非分隔点”的二分类概率，形状(..., 2)
+    # - boundary_mask: 基于最大概率得到的硬选择（True=分隔点），形状与输入序列相同的前两维
+    # - selected_probs: 对应硬选择类别的概率，形状(..., 1)
+    boundary_prob: torch.Tensor
+    boundary_mask: torch.Tensor
+    selected_probs: torch.Tensor
+
+
+@dataclass
+class RoutingModuleState:
+    """
+    路由模块的推理状态（用于增量/流式step）
+
+    包含：
+      - has_seen_tokens: (batch_size,) 是否已经见过任意token（用于首token强制边界）
+      - last_hidden_state: (batch_size, d_model) 上一次的隐藏状态（用于与当前token做相邻相似度）
+    """
+
+    has_seen_tokens: torch.Tensor  # (batch_size,)
+    last_hidden_state: torch.Tensor  # (batch_size, d_model)
+
+
+@dataclass
+class DeChunkState:
+    """
+    DeChunk 的推理状态（EMA的记忆值）
+
+    包含：
+      - last_value: (batch_size, d_model) EMA反聚合的上一时刻值
+    """
+
+    last_value: torch.Tensor  # (batch_size, d_model)
+
+
+def get_seq_idx(cu_seqlens, device=None):
+    seq_idx = torch.zeros(cu_seqlens[-1], dtype=torch.long, device=device)
+    seq_idx[cu_seqlens[:-1]] = 1
+    seq_idx = (torch.cumsum(seq_idx, dim=0) - 1).unsqueeze(0).int()
+
+    return seq_idx
+
+class RoutingModule(nn.Module):
+    """
+    路由模块：
+    用相邻token的余弦相似度构造“成为分隔点”的概率：
+      p_t = clamp((1 - cos(h_{t-1}, h_t)) / 2, 0, 1)
+    并强制首位置为边界（概率=1）。
+    支持：
+      - 常规batch掩码 mask 模式
+      - packed序列 cu_seqlens 模式（把多序列打包成单条序列的拼接）
+      - 流式推理 step()（维护状态）
+    """
+
+    def __init__(self, d_model, device=None, dtype=None):
+        self.d_model = d_model
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        # 相邻相似度计算前的线性投影（初始化为恒等）
+        self.q_proj_layer = nn.Linear(d_model, d_model, bias=False, **factory_kwargs)
+        self.k_proj_layer = nn.Linear(d_model, d_model, bias=False, **factory_kwargs)
+        with torch.no_grad():
+            self.q_proj_layer.weight.copy_(torch.eye(d_model))
+            self.k_proj_layer.weight.copy_(torch.eye(d_model))
+        # 防止外部权重再初始化
+        self.q_proj_layer.weight._no_reinit = True
+        self.k_proj_layer.weight._no_reinit = True
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, device, dtype=None):
+        # 分配推理cache（用于step）
+        return RoutingModuleState(
+            has_seen_tokens=torch.zeros(batch_size, device=device, dtype=torch.bool),
+            last_hidden_state=torch.zeros(
+                batch_size, self.d_model, device=device, dtype=dtype
+            ),
+        )
+
+    def forward(self, hidden_states, cu_seqlens=None, mask=None, inference_params=None):
+        """
+        hidden_states:
+          - 若 cu_seqlens is None: (B, L, D)
+          - 若 cu_seqlens 非 None: 期望 packed 模式 (T, D)，这里会临时扩维成 (1, T, D)
+        cu_seqlens: packed模式下每条序列的前缀和下标，形如 [0, len1, len1+len2, ...]
+        mask: (B, L) bool，True=有效（非packed）
+        inference_params: RoutingModuleState，用于prefill时的校验与状态维护
+        """
+        assert (mask is not None) or (
+            cu_seqlens is not None
+        ), "Either mask or cu_seqlens must be provided"
+
+        if inference_params is not None:
+            # prefill阶段必须提供mask，且不允许之前已经见过token
+            assert (
+                mask is not None
+            ), "Mask must be provided if inference_params is provided"
+            assert (
+                ~inference_params.has_seen_tokens
+            ).all(), "Cannot have seen tokens when inference_params is not provided"
+
+        if cu_seqlens is not None:
+            # packed 模式：把 (T, D) 临时变为 (1, T, D)
+            hidden_states = hidden_states.unsqueeze(0)
+
+        # 计算相邻余弦相似度 cos(h_{t-1}, h_t)
+        cos_sim = torch.einsum(
+            "b l d, b l d -> b l",
+            F.normalize(self.q_proj_layer(hidden_states[:, :-1]), dim=-1),
+            F.normalize(self.k_proj_layer(hidden_states[:, 1:]), dim=-1),
+        )
+        # p = ((1 - cos) / 2) ∈ [0,1]
+        boundary_prob = torch.clamp(((1 - cos_sim) / 2), min=0.0, max=1.0)
+
+        # 强制首位置为边界：首位概率=1，补充后长度和输入序列长度想等
+        PAD_PROB = 1.0
+        boundary_prob = F.pad(boundary_prob, (1, 0), "constant", PAD_PROB)
+
+        if cu_seqlens is not None:
+            # packed 模式下，每段序列的第一个位置是 cu_seqlens[:-1]
+            boundary_prob = boundary_prob.squeeze(0)
+            boundary_prob[cu_seqlens[:-1]] = PAD_PROB
+
+        # 组装为二分类概率 [非边界, 边界]
+        boundary_prob = torch.stack(((1 - boundary_prob), boundary_prob), dim=-1)
+
+        # 取最大概率类别
+        selected_idx = torch.argmax(boundary_prob, dim=-1)
+
+        # 硬边界掩码
+        boundary_mask = selected_idx == 1  # 形状与 hidden_states 的前两维一致
+        if mask is not None:
+            # 不允许选择到无效token
+            boundary_mask = boundary_mask & mask
+
+        if inference_params is not None:
+            # 维护路由状态：是否见过token、最后一个有效token的隐藏状态
+            has_mask = mask.any(dim=-1)
+            inference_params.has_seen_tokens.copy_(
+                has_mask | inference_params.has_seen_tokens
+            )
+            last_mask = torch.clamp(mask.sum(dim=-1) - 1, min=0)
+            inference_params.last_hidden_state.copy_(
+                torch.where(
+                    has_mask,
+                    hidden_states[
+                        torch.arange(
+                            hidden_states.shape[0], device=hidden_states.device
+                        ),
+                        last_mask,
+                    ],
+                    inference_params.last_hidden_state,
+                )
+            )
+
+        # 取硬选择对应的概率（便于可视化/正则）
+        selected_probs = boundary_prob.gather(
+            dim=-1, index=selected_idx.unsqueeze(-1)
+        )  # (..., 1)
+
+        return RoutingModuleOutput(
+            boundary_prob=boundary_prob,  # (..., 2)
+            boundary_mask=boundary_mask,  # (...)
+            selected_probs=selected_probs,  # (..., 1)
+        )
+
+    def step(self, hidden_states, inference_params):
+        """
+        流式单步：
+          hidden_states: (B, 1, D)
+          使用上一步缓存的 last_hidden_state 与当前token计算相邻相似度，得到当前步的边界概率
+        """
+        # (B, D)
+        hidden_states = hidden_states.squeeze(1)
+        cos_sim = torch.einsum(
+            "b d, b d -> b",
+            F.normalize(self.q_proj_layer(inference_params.last_hidden_state), dim=-1),
+            F.normalize(self.k_proj_layer(hidden_states), dim=-1),
+        )
+        boundary_prob = torch.clamp(((1 - cos_sim) / 2), min=0.0, max=1.0)
+        # 更新最后隐藏状态
+        inference_params.last_hidden_state.copy_(hidden_states)
+        # 首个token前，强制边界
+        boundary_prob = torch.where(
+            inference_params.has_seen_tokens,
+            boundary_prob,
+            torch.ones_like(boundary_prob),
+        )
+        boundary_prob = torch.stack(((1 - boundary_prob), boundary_prob), dim=-1)
+
+        # 标记为已见token
+        inference_params.has_seen_tokens.copy_(
+            torch.ones_like(inference_params.has_seen_tokens)
+        )
+        return RoutingModuleOutput(
+            boundary_prob=boundary_prob,  # (B, 2)
+            boundary_mask=boundary_prob[..., 1] > 0.5,  # (B,)
+            selected_probs=boundary_prob.max(dim=-1).values.unsqueeze(-1),  # (B, 1)
+        )
+
+
+class ChunkLayer(nn.Module):
+    """
+    Chunk层：根据 boundary_mask 将被选中的“边界token”抽取出来，形成下一层序列。
+    支持两种模式：
+      - packed（cu_seqlens 非 None）：直接在拼接后的序列上索引
+      - 非packed（mask 非 None）：通过排序 trick 把True位置排到前面，并生成 next_mask
+    返回：
+      - next_hidden_states: 选中的token序列（packed: shape=(#selected, D)；非packed: (B, M, D)）
+      - next_cu_seqlens:   packed模式下新序列的cu_seqlens；否则None
+      - next_max_seqlen:   packed模式下选中的最大长度；非packed模式返回None
+      - next_mask:         非packed模式下的右侧pad掩码；packed模式下None
+    """
+
+    def forward(self, hidden_states, boundary_mask, cu_seqlens=None, mask=None):
+        assert (mask is not None) or (
+            cu_seqlens is not None
+        ), "Either mask or cu_seqlens must be provided"
+
+        if cu_seqlens is not None:
+            # packed：直接选择True的行，得到拼接后的 selected
+            next_hidden_states = hidden_states[boundary_mask]
+            # 新的cu_seqlens = 对每段最后一个位置(=cu_seqlens[1:]-1)累计True的计数，再前置0
+            next_cu_seqlens = F.pad(
+                boundary_mask.cumsum(dim=0)[cu_seqlens[1:] - 1], (1, 0)
+            )
+            # 新序列的最大段长（仅用于内核/优化）
+            next_max_seqlen = int((next_cu_seqlens[1:] - next_cu_seqlens[:-1]).max())
+            next_mask = None
+        else:
+            # 非packed：对每个batch内，把True位置排到前面（False放到靠后）
+            next_cu_seqlens = None
+            num_tokens = boundary_mask.sum(dim=-1)           # 每个样本被选中的数量
+            next_max_seqlen = int(num_tokens.max())
+
+            device = hidden_states.device
+            L = hidden_states.shape[1]
+            # trick：用 (~boundary_mask)*L 把False加大，从而 argsort 后 True 的下标排在前面
+            token_idx = (
+                torch.arange(L, device=device)[None, :] + (~boundary_mask).long() * L
+            )
+            seq_sorted_indices = torch.argsort(token_idx, dim=1)
+
+            # 收集前 next_max_seqlen 个（不足的样本右侧pad）
+            next_hidden_states = torch.gather(
+                hidden_states,
+                dim=1,
+                index=seq_sorted_indices[:, :next_max_seqlen, None].expand(
+                    -1, -1, hidden_states.shape[-1]
+                ),
+            )
+
+            # 下游的有效mask（右侧pad无效）
+            next_mask = (
+                torch.arange(next_max_seqlen, device=device)[None, :]
+                < num_tokens[:, None]
+            )
+            # 非packed模式下，不再需要 max_seqlen（返回None）
+            next_max_seqlen = None
+
+        return next_hidden_states, next_cu_seqlens, next_max_seqlen, next_mask
+
+    def step(self, hidden_states, boundary_mask):
+        # 流式step：仅返回当前步被选中的token（用于下一层）
+        return hidden_states[boundary_mask]
+
+
+class DeChunkLayer(nn.Module):
+    """
+    DeChunk层：把“被选中的边界token序列”反聚合（EMA）回原始等长序列。
+    实现上复用 Mamba2 的 Triton 扫描核 mamba_chunk_scan_combined：
+      - 将 d_model 切分为 nheads * headdim
+      - 使用参数 A=-1, b=p, c=1 的一阶状态空间/EMA形式进行前向扫描
+      - 最终把扫描输出根据分段索引映射回原位置（plug back）
+    支持：
+      - packed 模式（cu_seqlens）
+      - 非packed（batch+右侧pad）
+      - 流式 step（EMA递推）
+    """
+
+    def __init__(
+        self,
+        d_model,
+        dtype=torch.bfloat16,
+        block_size=256,
+        headdim=32,
+    ):
+        super().__init__()
+        self.d_model = d_model
+
+        # 仅为内核要求：使用 bfloat16，块大小与头维拆分
+        self.dtype = dtype
+        self.block_size = block_size
+        self.headdim = headdim
+        assert d_model % self.headdim == 0
+        self.nheads = d_model // self.headdim
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, device, dtype=None):
+        # 分配EMA的last_value缓存
+        return DeChunkState(
+            last_value=torch.zeros(
+                batch_size, self.d_model, device=device, dtype=dtype
+            ),
+        )
+
+    def forward(
+        self,
+        hidden_states,  # 被选中的token序列（packed: (M, D)；非packed: (B, M, D)）
+        boundary_mask,  # 原序列上的边界掩码（(T,) 或 (B, L)）
+        boundary_prob,  # 原序列上的二分类概率（(..., 2)）
+        cu_seqlens=None,
+        inference_params=None,
+        mask=None,
+    ):
+        """
+        核心思路：
+          1) 从 boundary_prob 得到 p = P(boundary) ∈ (1e-4, 1-1e-4)
+          2) 构造 dt = log(1 / (1-p))，并对输入做缩放 x = h / dt
+          3) 用 mamba_chunk_scan_combined 扫描：A=-1, b=p, c=1，对 (B, M, H, P) 进行块扫描
+          4) 将结果根据 cumulative boundary index 回填到原序列位置
+        """
+        if inference_params is not None:
+            # prefill时必须有mask，且首token必须是边界（保证EMA初始化）
+            assert (
+                mask is not None
+            ), "Mask must be provided if inference_params is provided"
+            assert boundary_mask[
+                :, 0
+            ].all(), "First token must be a boundary if running prefill"
+
+        # 取边界概率的“边界类”概率 p，并限制在(1e-4, 1-1e-4)内，避免数值不稳
+        p = torch.clamp(boundary_prob[..., -1].float(), min=1e-4, max=1 - (1e-4))
+
+        if cu_seqlens is not None:
+            # packed：从原序列p中取出被选中的位置对应的概率，形状(B=1, M)
+            p = p[boundary_mask].unsqueeze(0)
+            # 为triton核准备packed序列的索引映射
+            seq_idx = get_seq_idx(cu_seqlens, device=hidden_states.device)
+        else:
+            B, L = boundary_mask.shape
+            seq_idx = None
+            # 与ChunkLayer一致的排序 trick，得到选中的顺序（True在前）
+            token_idx = (
+                torch.arange(L, device=hidden_states.device)[None, :]
+                + (~boundary_mask).long() * L
+            )
+            seq_sorted_indices = torch.argsort(token_idx, dim=1)
+
+            # 取出与 hidden_states 对应长度的 p（(B, M)）
+            p = torch.gather(
+                p, dim=1, index=seq_sorted_indices[:, : hidden_states.shape[1]]
+            )  # (B, M)
+
+        original_dtype = hidden_states.dtype
+        # 构造 EMA 扫描所需变量
+        dt = torch.log(1 / (1 - p)).to(self.dtype)      # (B, M)
+        x = (hidden_states / dt[..., None]).to(self.dtype)  # (B, M, D) / (B, M, 1)
+
+        # A, b, c 分别对应一阶状态空间/EMA的参数
+        A = -torch.ones(
+            (self.nheads,), device=hidden_states.device, dtype=torch.float32
+        )
+        b = p.to(self.dtype)
+        c = torch.ones_like(b)
+
+        # 调用triton核进行块扫描
+        out = mamba_chunk_scan_combined(
+            rearrange(x, "b l (h p) -> b l h p", p=self.headdim),     # (B, M, H, P)
+            repeat(dt, "b l -> b l h", h=self.nheads),                # (B, M, H)
+            A,                                                       # (H,)
+            rearrange(b, "b l -> b l 1 1"),                          # (B, M, 1, 1)
+            rearrange(c, "b l -> b l 1 1"),                          # (B, M, 1, 1)
+            chunk_size=self.block_size,
+            seq_idx=seq_idx,                                         # packed时提供
+        )
+        out = rearrange(out, "b l h p -> b l (h p)")                 # (B, M, D)
+
+        # 将扫描结果回填（plug back）到原序列位置
+        if cu_seqlens is not None:
+            out = out.squeeze(0)                                     # (M, D)
+            plug_back_idx = boundary_mask.cumsum(dim=0) - 1          # (T,)
+            out = torch.gather(
+                out, dim=0, index=plug_back_idx.unsqueeze(-1).expand(-1, self.d_model)
+            )                                                        # (T, D)
+        else:
+            plug_back_idx = torch.cumsum(boundary_mask, dim=1) - 1   # (B, L)
+            out = torch.gather(
+                out,
+                dim=1,
+                index=plug_back_idx.unsqueeze(-1).expand(-1, -1, self.d_model),
+            )                                                        # (B, L, D)
+
+        # 更新流式缓存
+        if inference_params is not None:
+            inference_params.last_value.copy_(out[:, -1])
+
+        return out.to(original_dtype)
+
+    def step(self, hidden_states, boundary_mask, boundary_prob, inference_params):
+        """
+        流式单步 EMA 反聚合：
+          hidden_states: (B', 1, D)，其中 B' = 当前步被选中的数量（boundary_mask.sum()）
+          boundary_mask: (B,) 当前batch哪些位置被选中为边界
+          boundary_prob: (B, 2) 当前batch各位置的边界概率
+        输出：(B, 1, D)，对应对所有位置做了一步 EMA 更新后的值
+        """
+        B = boundary_mask.shape[0]
+        D = hidden_states.shape[-1]
+
+        # 构造当前步每个位置的 p（未被选中的位置 p=0）
+        p = torch.zeros(B, device=hidden_states.device, dtype=hidden_states.dtype)
+        p[boundary_mask] = boundary_prob[boundary_mask, -1].clamp(
+            min=1e-4, max=1 - (1e-4)
+        )
+
+        # 构造当前被选中的隐藏状态（未选中为0）
+        current_hidden_states = torch.zeros(
+            B, D, device=hidden_states.device, dtype=hidden_states.dtype
+        )
+        current_hidden_states[boundary_mask] = hidden_states.squeeze(1)
+
+        # EMA：result = p * x + (1 - p) * last
+        result = p * current_hidden_states + (1 - p) * inference_params.last_value
+        inference_params.last_value.copy_(result)
+
+        return result.unsqueeze(1)

layers/SelfAttention_Family.py

@@ -17,25 +17,30 @@ class DSAttention(nn.Module):
         self.output_attention = output_attention
         self.dropout = nn.Dropout(attention_dropout)
 
-    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None, key_padding_mask=None):
+        """
+        key_padding_mask: (B, S) bool, True=valid, False=pad（可选，忽略或由上层应用）
+        """
         B, L, H, E = queries.shape
         _, S, _, D = values.shape
         scale = self.scale or 1. / sqrt(E)
 
-        tau = 1.0 if tau is None else tau.unsqueeze(
+        tau = 1.0 if tau is None else tau.unsqueeze(1).unsqueeze(1)   # B x 1 x 1 x 1
-            1).unsqueeze(1)  # B x 1 x 1 x 1
+        delta = 0.0 if delta is None else delta.unsqueeze(1).unsqueeze(1)  # B x 1 x 1 x S
-        delta = 0.0 if delta is None else delta.unsqueeze(
-            1).unsqueeze(1)  # B x 1 x 1 x S
 
-        # De-stationary Attention, rescaling pre-softmax score with learned de-stationary factors
+        scores = torch.einsum("blhe,bshe->bhls", queries, keys) * tau + delta  # (B,H,L,S)
-        scores = torch.einsum("blhe,bshe->bhls", queries, keys) * tau + delta
 
         if self.mask_flag:
             if attn_mask is None:
                 attn_mask = TriangularCausalMask(B, L, device=queries.device)
-
             scores.masked_fill_(attn_mask.mask, -np.inf)
 
+        # 可选：基于key_padding_mask的无效键屏蔽（不改变原行为，默认None）
+        if key_padding_mask is not None:
+            # key_padding_mask: True 表示有效，False为padding
+            invalid_k = (~key_padding_mask).unsqueeze(1).unsqueeze(1)  # (B,1,1,S)
+            scores = scores.masked_fill(invalid_k, -np.inf)
+
         A = self.dropout(torch.softmax(scale * scores, dim=-1))
         V = torch.einsum("bhls,bshd->blhd", A, values)
 
@@ -46,6 +51,12 @@ class DSAttention(nn.Module):
 
 
 class FullAttention(nn.Module):
+    """
+    修正点：
+      - 新增 key_padding_mask 支持，用于屏蔽批内右侧pad的键向量（与DC变长对齐）
+      - key_padding_mask 约定：shape=(B, S)，True=有效，False=padding
+      - 其余行为与原实现保持一致
+    """
     def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
         super(FullAttention, self).__init__()
         self.scale = scale
@@ -53,21 +64,33 @@ class FullAttention(nn.Module):
         self.output_attention = output_attention
         self.dropout = nn.Dropout(attention_dropout)
 
-    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None, key_padding_mask=None):
+        """
+        queries: (B, L, H, E)
+        keys:    (B, S, H, E)
+        values:  (B, S, H, D)
+        attn_mask: TriangularCausalMask 或 None
+        key_padding_mask: (B, S) bool，True=有效，False=padding（可选）
+        """
         B, L, H, E = queries.shape
         _, S, _, D = values.shape
         scale = self.scale or 1. / sqrt(E)
 
-        scores = torch.einsum("blhe,bshe->bhls", queries, keys)
+        scores = torch.einsum("blhe,bshe->bhls", queries, keys)  # (B,H,L,S)
 
         if self.mask_flag:
             if attn_mask is None:
                 attn_mask = TriangularCausalMask(B, L, device=queries.device)
-
             scores.masked_fill_(attn_mask.mask, -np.inf)
 
-        A = self.dropout(torch.softmax(scale * scores, dim=-1))
+        # 基于key_padding_mask屏蔽无效键（padding位置不参与注意力）
-        V = torch.einsum("bhls,bshd->blhd", A, values)
+        if key_padding_mask is not None:
+            # key_padding_mask: True=有效，False=padding
+            invalid_k = (~key_padding_mask).unsqueeze(1).unsqueeze(1)  # (B,1,1,S)
+            scores = scores.masked_fill(invalid_k, -np.inf)
+
+        A = self.dropout(torch.softmax(scale * scores, dim=-1))  # (B,H,L,S)
+        V = torch.einsum("bhls,bshd->blhd", A, values)           # (B,L,H,D)
 
         if self.output_attention:
             return V.contiguous(), A
@@ -85,100 +108,86 @@ class ProbAttention(nn.Module):
         self.dropout = nn.Dropout(attention_dropout)
 
     def _prob_QK(self, Q, K, sample_k, n_top):  # n_top: c*ln(L_q)
-        # Q [B, H, L, D]
+        # Q [B, H, L_q, D], K [B, H, L_k, D]
         B, H, L_K, E = K.shape
         _, _, L_Q, _ = Q.shape
 
-        # calculate the sampled Q_K
         K_expand = K.unsqueeze(-3).expand(B, H, L_Q, L_K, E)
-        # real U = U_part(factor*ln(L_k))*L_q
+        index_sample = torch.randint(L_K, (L_Q, sample_k), device=Q.device)
-        index_sample = torch.randint(L_K, (L_Q, sample_k))
+        K_sample = K_expand[:, :, torch.arange(L_Q, device=Q.device).unsqueeze(1), index_sample, :]
-        K_sample = K_expand[:, :, torch.arange(
+        Q_K_sample = torch.matmul(Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze(-2)  # (B,H,L_Q,sample_k)
-            L_Q).unsqueeze(1), index_sample, :]
-        Q_K_sample = torch.matmul(
-            Q.unsqueeze(-2), K_sample.transpose(-2, -1)).squeeze()
 
-        # find the Top_k query with sparisty measurement
+        M = Q_K_sample.max(-1)[0] - torch.div(Q_K_sample.sum(-1), L_K)  # (B,H,L_Q)
-        M = Q_K_sample.max(-1)[0] - torch.div(Q_K_sample.sum(-1), L_K)
+        M_top = M.topk(n_top, sorted=False)[1]  # indices
-        M_top = M.topk(n_top, sorted=False)[1]
 
-        # use the reduced Q to calculate Q_K
         Q_reduce = Q[torch.arange(B)[:, None, None],
-                   torch.arange(H)[None, :, None],
+                     torch.arange(H)[None, :, None],
-                   M_top, :]  # factor*ln(L_q)
+                     M_top, :]  # (B,H,n_top,D)
-        Q_K = torch.matmul(Q_reduce, K.transpose(-2, -1))  # factor*ln(L_q)*L_k
+        Q_K = torch.matmul(Q_reduce, K.transpose(-2, -1))  # (B,H,n_top,L_K)
-
         return Q_K, M_top
 
     def _get_initial_context(self, V, L_Q):
         B, H, L_V, D = V.shape
         if not self.mask_flag:
-            # V_sum = V.sum(dim=-2)
+            V_mean = V.mean(dim=-2)  # (B,H,D)
-            V_sum = V.mean(dim=-2)
+            context = V_mean.unsqueeze(-2).expand(B, H, L_Q, D).clone()
-            contex = V_sum.unsqueeze(-2).expand(B, H,
+        else:
-                                                L_Q, V_sum.shape[-1]).clone()
+            assert L_Q == L_V
-        else:  # use mask
+            context = V.cumsum(dim=-2)
-            # requires that L_Q == L_V, i.e. for self-attention only
+        return context
-            assert (L_Q == L_V)
-            contex = V.cumsum(dim=-2)
-        return contex
 
     def _update_context(self, context_in, V, scores, index, L_Q, attn_mask):
         B, H, L_V, D = V.shape
-
         if self.mask_flag:
             attn_mask = ProbMask(B, H, L_Q, index, scores, device=V.device)
             scores.masked_fill_(attn_mask.mask, -np.inf)
-
+        attn = torch.softmax(scores, dim=-1)
-        attn = torch.softmax(scores, dim=-1)  # nn.Softmax(dim=-1)(scores)
 
         context_in[torch.arange(B)[:, None, None],
-        torch.arange(H)[None, :, None],
+                   torch.arange(H)[None, :, None],
-        index, :] = torch.matmul(attn, V).type_as(context_in)
+                   index, :] = torch.matmul(attn, V).type_as(context_in)
         if self.output_attention:
-            attns = (torch.ones([B, H, L_V, L_V]) /
+            attns = (torch.ones([B, H, L_V, L_V], device=attn.device, dtype=attn.dtype) / L_V)
-                     L_V).type_as(attn).to(attn.device)
+            attns[torch.arange(B)[:, None, None], torch.arange(H)[None, :, None], index, :] = attn
-            attns[torch.arange(B)[:, None, None], torch.arange(H)[
-                                                  None, :, None], index, :] = attn
             return context_in, attns
         else:
             return context_in, None
 
-    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None, key_padding_mask=None):
+        """
+        key_padding_mask 目前未集成到 ProbAttention（如需，可在scores处对无效键置 -inf）
+        """
         B, L_Q, H, D = queries.shape
         _, L_K, _, _ = keys.shape
 
-        queries = queries.transpose(2, 1)
+        queries = queries.transpose(2, 1)  # (B,H,L_Q,D)
-        keys = keys.transpose(2, 1)
+        keys = keys.transpose(2, 1)        # (B,H,L_K,D)
-        values = values.transpose(2, 1)
+        values = values.transpose(2, 1)    # (B,H,L_K,D)
 
-        U_part = self.factor * \
+        U_part = self.factor * int(np.ceil(np.log(L_K)))
-                 np.ceil(np.log(L_K)).astype('int').item()  # c*ln(L_k)
+        u = self.factor * int(np.ceil(np.log(L_Q)))
-        u = self.factor * \
-            np.ceil(np.log(L_Q)).astype('int').item()  # c*ln(L_q)
 
-        U_part = U_part if U_part < L_K else L_K
+        U_part = min(U_part, L_K)
-        u = u if u < L_Q else L_Q
+        u = min(u, L_Q)
 
-        scores_top, index = self._prob_QK(
+        scores_top, index = self._prob_QK(queries, keys, sample_k=U_part, n_top=u)
-            queries, keys, sample_k=U_part, n_top=u)
 
-        # add scale factor
         scale = self.scale or 1. / sqrt(D)
-        if scale is not None:
+        scores_top = scores_top * scale
-            scores_top = scores_top * scale
+
-        # get the context
         context = self._get_initial_context(values, L_Q)
-        # update the context with selected top_k queries
+        context, attn = self._update_context(context, values, scores_top, index, L_Q, attn_mask)
-        context, attn = self._update_context(
-            context, values, scores_top, index, L_Q, attn_mask)
 
         return context.contiguous(), attn
 
 
 class AttentionLayer(nn.Module):
-    def __init__(self, attention, d_model, n_heads, d_keys=None,
+    """
-                 d_values=None):
+    修正点：
+      - forward 新增 key_padding_mask 参数，并向 inner_attention 透传
+      - 保持与旧调用兼容（不传时默认None）
+    """
+    def __init__(self, attention, d_model, n_heads, d_keys=None, d_values=None):
         super(AttentionLayer, self).__init__()
 
         d_keys = d_keys or (d_model // n_heads)
@@ -191,7 +200,10 @@ class AttentionLayer(nn.Module):
         self.out_projection = nn.Linear(d_values * n_heads, d_model)
         self.n_heads = n_heads
 
-    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
+    def forward(self, queries, keys, values, attn_mask, tau=None, delta=None, key_padding_mask=None):
+        """
+        key_padding_mask: (B, S) bool, True=有效，False=padding
+        """
         B, L, _ = queries.shape
         _, S, _ = keys.shape
         H = self.n_heads
@@ -206,10 +218,10 @@ class AttentionLayer(nn.Module):
             values,
             attn_mask,
             tau=tau,
-            delta=delta
+            delta=delta,
+            key_padding_mask=key_padding_mask,
         )
         out = out.view(B, L, -1)
-
         return self.out_projection(out), attn
 
 
@@ -232,12 +244,11 @@ class ReformerLayer(nn.Module):
         if N % (self.bucket_size * 2) == 0:
             return queries
         else:
-            # fill the time series
             fill_len = (self.bucket_size * 2) - (N % (self.bucket_size * 2))
             return torch.cat([queries, torch.zeros([B, fill_len, C]).to(queries.device)], dim=1)
 
-    def forward(self, queries, keys, values, attn_mask, tau, delta):
+    def forward(self, queries, keys, values, attn_mask, tau, delta, key_padding_mask=None):
-        # in Reformer: defalut queries=keys
+        # queries=keys in Reformer
         B, N, C = queries.shape
         queries = self.attn(self.fit_length(queries))[:, :N, :]
         return queries, None
@@ -275,23 +286,23 @@ class TwoStageAttentionLayer(nn.Module):
                                   nn.GELU(),
                                   nn.Linear(d_ff, d_model))
 
-    def forward(self, x, attn_mask=None, tau=None, delta=None):
+    def forward(self, x, attn_mask=None, tau=None, delta=None, key_padding_mask=None):
         # Cross Time Stage: Directly apply MSA to each dimension
         batch = x.shape[0]
         time_in = rearrange(x, 'b ts_d seg_num d_model -> (b ts_d) seg_num d_model')
         time_enc, attn = self.time_attention(
-            time_in, time_in, time_in, attn_mask=None, tau=None, delta=None
+            time_in, time_in, time_in, attn_mask=None, tau=None, delta=None, key_padding_mask=key_padding_mask
         )
         dim_in = time_in + self.dropout(time_enc)
         dim_in = self.norm1(dim_in)
         dim_in = dim_in + self.dropout(self.MLP1(dim_in))
         dim_in = self.norm2(dim_in)
 
-        # Cross Dimension Stage: use a small set of learnable vectors to aggregate and distribute messages to build the D-to-D connection
+        # Cross Dimension Stage
         dim_send = rearrange(dim_in, '(b ts_d) seg_num d_model -> (b seg_num) ts_d d_model', b=batch)
         batch_router = repeat(self.router, 'seg_num factor d_model -> (repeat seg_num) factor d_model', repeat=batch)
-        dim_buffer, attn = self.dim_sender(batch_router, dim_send, dim_send, attn_mask=None, tau=None, delta=None)
+        dim_buffer, _ = self.dim_sender(batch_router, dim_send, dim_send, attn_mask=None, tau=None, delta=None)
-        dim_receive, attn = self.dim_receiver(dim_send, dim_buffer, dim_buffer, attn_mask=None, tau=None, delta=None)
+        dim_receive, _ = self.dim_receiver(dim_send, dim_buffer, dim_buffer, attn_mask=None, tau=None, delta=None)
         dim_enc = dim_send + self.dropout(dim_receive)
         dim_enc = self.norm3(dim_enc)
         dim_enc = dim_enc + self.dropout(self.MLP2(dim_enc))

models/DC_PatchTST.py

@@ -0,0 +1,528 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+from layers.SelfAttention_Family import FullAttention, AttentionLayer
+
+# 需要 Mamba2 作为外层编码器
+from mamba_ssm.modules.mamba2 import Mamba2
+
+
+
+# -------------------- Routing（余弦路由，和论文一致） --------------------
+class RoutingModule(nn.Module):
+    def __init__(self, d_model):
+        super().__init__()
+        self.q_proj = nn.Linear(d_model, d_model, bias=False)
+        self.k_proj = nn.Linear(d_model, d_model, bias=False)
+        with torch.no_grad():
+            nn.init.eye_(self.q_proj.weight)
+            nn.init.eye_(self.k_proj.weight)
+        self.q_proj.weight._no_reinit = True
+        self.k_proj.weight._no_reinit = True
+
+    def forward(self, x, mask=None):
+        """
+        x: (B, L, D)
+        mask: (B, L) bool, True=有效
+        返回：
+          boundary_prob: (B, L, 2)
+          boundary_mask: (B, L) bool
+          selected_probs: (B, L, 1)
+        """
+        B, L, D = x.shape
+        q = F.normalize(self.q_proj(x[:, :-1]), dim=-1)  # (B, L-1, D)
+        k = F.normalize(self.k_proj(x[:, 1:]), dim=-1)   # (B, L-1, D)
+        cos_sim = (q * k).sum(dim=-1)                    # (B, L-1)
+        p = torch.clamp((1 - cos_sim) / 2, 0.0, 1.0)     # (B, L-1)
+        p = F.pad(p, (1, 0), value=1.0)                  # 强制首位是边界
+
+        if mask is not None:
+            p = p * mask.float()
+            p[:, 0] = torch.where(mask[:, 0], torch.ones_like(p[:, 0]), p[:, 0])
+
+        boundary_prob = torch.stack([1 - p, p], dim=-1)  # (B, L, 2)
+        selected_idx = boundary_prob.argmax(dim=-1)
+        boundary_mask = (selected_idx == 1)
+        if mask is not None:
+            boundary_mask = boundary_mask & mask
+        selected_probs = boundary_prob.gather(-1, selected_idx.unsqueeze(-1))  # (B, L, 1)
+        return boundary_prob, boundary_mask, selected_probs
+
+
+# -------------------- 选择并右侧零pad（不丢弃、不重复填充） --------------------
+def select_and_right_pad(x, boundary_mask):
+    """
+    内存优化版本：减少临时tensor创建
+    x: (B, L, D), boundary_mask: (B, L) bool
+    返回:
+      x_pad: (B, T_max, D)
+      key_padding_mask: (B, T_max) bool, True=有效
+      lengths: (B,)
+    """
+    B, L, D = x.shape
+    device = x.device
+    lengths = boundary_mask.sum(dim=1)  # (B,)
+    T_max = int(lengths.max().item()) if lengths.max() > 0 else 1
+
+    x_pad = x.new_zeros(B, T_max, D)
+    key_padding_mask = torch.zeros(B, T_max, dtype=torch.bool, device=device)
+
+    # 预创建默认索引tensor避免重复创建
+    default_idx = torch.tensor([0], device=device)
+    
+    for b in range(B):
+        mask_b = boundary_mask[b]
+        if mask_b.any():
+            idx = mask_b.nonzero(as_tuple=True)[0]  # 更高效的nonzero
+            t = idx.numel()
+            x_pad[b, :t] = x[b, idx]
+            key_padding_mask[b, :t] = True
+        else:
+            # 使用预创建的tensor
+            x_pad[b, 0] = x[b, default_idx]
+            key_padding_mask[b, 0] = True
+
+    return x_pad, key_padding_mask, lengths
+
+
+# -------------------- Mamba2 堆叠（外层编码器） --------------------
+class Mamba2Encoder(nn.Module):
+    def __init__(self, d_model, depth=4, dropout=0.0):
+        super().__init__()
+        self.layers = nn.ModuleList([Mamba2(d_model=d_model) for _ in range(depth)])
+        self.norm = nn.LayerNorm(d_model)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        for layer in self.layers:
+            x = layer(x)
+        x = self.norm(x)
+        x = self.dropout(x)
+        return x
+
+
+# -------------------- 两层Encoder + DC（变长，不丢信息；以比率约束压缩） --------------------
+class DCEmbedding2StageVarLen(nn.Module):
+    """
+    - Stage 0: (B*nvars, L, 1) -> Linear(D0) -> Mamba2(D0) -> Routing -> 选择 -> 扩宽到 D1
+    - Stage 1: (B*nvars, L0_sel, D1) -> Mamba2(D1) -> Routing -> 选择
+    输出：
+      enc_out: (B*nvars, T_max, D1)
+      key_padding_mask: (B*nvars, T_max)
+      n_vars: int
+      aux: dict（含两层ratio loss与边界信息）
+    """
+    def __init__(self, d_model_out, d_model_stage0, depth_enc0=4, depth_enc1=4, dropout=0.0,
+                 target_ratio0=0.25, target_ratio1=0.5):
+        super().__init__()
+        assert d_model_out >= d_model_stage0, "要求 D0 <= D1"
+        self.d0 = d_model_stage0
+        self.d1 = d_model_out
+
+        # 标量 -> D0
+        self.input_proj = nn.Linear(1, self.d0)
+
+        # Stage 0
+        self.enc0 = Mamba2Encoder(self.d0, depth=depth_enc0, dropout=dropout)
+        self.router0 = RoutingModule(self.d0)
+        delta = self.d1 - self.d0
+        self.pad_vec = nn.Parameter(torch.zeros(delta)) if delta > 0 else None
+        self.target_ratio0 = target_ratio0
+
+        # Stage 1
+        self.enc1 = Mamba2Encoder(self.d1, depth=depth_enc1, dropout=dropout)
+        self.router1 = RoutingModule(self.d1)
+        self.target_ratio1 = target_ratio1
+
+    def _expand_width(self, x):
+        if self.pad_vec is None:
+            return x
+        B, L, _ = x.shape
+        return torch.cat([x, self.pad_vec.view(1, 1, -1).expand(B, L, -1)], dim=-1)
+
+    @staticmethod
+    def _ratio_loss(boundary_mask: torch.Tensor, boundary_prob: torch.Tensor, target_ratio: float) -> torch.Tensor:
+        eps = 1e-6
+        F_act = boundary_mask.float().mean(dim=1)      # (B,)
+        G_prob = boundary_prob[..., 1].mean(dim=1)     # (B,)
+        N = 1.0 / max(target_ratio, eps)
+        loss = N / (N - 1.0 + eps) * (((N - 1.0) * F_act * G_prob) + (1.0 - F_act) * (1.0 - G_prob))
+        return loss.mean()
+
+    def forward(self, x):
+        """
+        x: (B, nvars, L)
+        内存优化版本：及时删除中间tensor
+        """
+        B, nvars, L = x.shape
+        x = x.reshape(B * nvars, L, 1)
+        x = self.input_proj(x)  # (B*nvars, L, D0)
+
+        # Stage 0
+        h0 = self.enc0(x)                    # (B*nvars, L, D0)
+        p0, bm0, _ = self.router0(h0)
+        h0_sel, mask0, len0 = select_and_right_pad(h0, bm0)  # (B*nvars, L0_max, D0)
+        
+        # 及时删除不需要的tensor
+        del h0
+        
+        # h0_sel = self._expand_width(h0_sel)                  # (B*nvars, L0_max, D1)
+
+        # Stage 1
+        #h1 = self.enc1(h0_sel)               # (B*nvars, L0_max, D1)
+        #p1, bm1, _ = self.router1(h1)
+        #bm1 = bm1 & mask0
+        #h1_sel, mask1, len1 = select_and_right_pad(h1, bm1)  # (B*nvars, L1_max, D1)
+        
+        # 及时删除中间tensor
+        #del h1, h0_sel
+
+        # 计算ratio loss时使用detach避免保存计算图
+        ratio_loss0 = self._ratio_loss(bm0, p0, target_ratio=self.target_ratio0)
+        # ratio_loss1 = self._ratio_loss(bm1, p1, target_ratio=self.target_ratio1)
+        
+        # 简化aux字典，只保存必要信息
+        aux = {
+            "stage0": {"boundary_mask": bm0.detach(), "boundary_prob": p0.detach(), "lengths": len0.detach()},
+            # "stage1": {"boundary_mask": bm1.detach(), "boundary_prob": p1.detach(), "lengths": len1.detach()},
+            "ratio_loss0": ratio_loss0,
+            # "ratio_loss1": ratio_loss1,
+        }
+        
+        return h0_sel, mask0, nvars, aux
+
+
+# -------------------- Encoder/EncoderLayer（带 key_padding_mask 透传） --------------------
+class EncoderLayerWithMask(nn.Module):
+    """
+    与原EncoderLayer结构一致，但 forward 增加 key_padding_mask，并传入 AttentionLayer。
+    FFN 用简单的 MLP（与常规Transformer一致）。
+    """
+    def __init__(self, attention: AttentionLayer, d_model, d_ff, dropout=0.1, activation="gelu"):
+        super().__init__()
+        self.attention = attention
+        self.dropout = nn.Dropout(dropout)
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+
+        if activation == "relu":
+            act = nn.ReLU()
+        elif activation == "gelu":
+            act = nn.GELU()
+        else:
+            raise ValueError(f"Unsupported activation: {activation}")
+
+        self.ffn = nn.Sequential(
+            nn.Linear(d_model, d_ff),
+            act,
+            nn.Dropout(dropout),
+            nn.Linear(d_ff, d_model),
+        )
+
+    def forward(self, x, attn_mask=None, tau=None, delta=None, key_padding_mask=None):
+        # Multi-head attention with key padding mask
+        attn_out, attn = self.attention(
+            x, x, x, attn_mask, tau=tau, delta=delta, key_padding_mask=key_padding_mask
+        )
+        x = x + self.dropout(attn_out)
+        x = self.norm1(x)
+
+        # FFN
+        y = self.ffn(x)
+        x = x + self.dropout(y)
+        x = self.norm2(x)
+        return x, attn
+
+
+class EncoderWithMask(nn.Module):
+    """
+    与原Encoder类似，但 forward 支持 key_padding_mask，并传递给每一层的注意力。
+    """
+    def __init__(self, attn_layers, norm_layer=None):
+        super().__init__()
+        self.attn_layers = nn.ModuleList(attn_layers)
+        self.norm = norm_layer
+
+    def forward(self, x, attn_mask=None, key_padding_mask=None):
+        attns = []
+        for attn_layer in self.attn_layers:
+            x, attn = attn_layer(x, attn_mask=attn_mask, key_padding_mask=key_padding_mask)
+            attns.append(attn)
+        if self.norm is not None:
+            x = self.norm(x)
+        return x, attns
+
+
+# -------------------- 门控注意力聚合 + 任务头（不依赖token数；保留信息） --------------------
+def masked_softmax(logits: torch.Tensor, mask: torch.Tensor, dim: int = -1) -> torch.Tensor:
+    """
+    logits: (..., T)
+    mask:   (..., T) bool, True=有效
+    """
+    neg_inf = torch.finfo(logits.dtype).min
+    logits = logits.masked_fill(~mask, neg_inf)
+    return torch.softmax(logits, dim=dim)
+
+class GatedAttnAggregator(nn.Module):
+    """
+    门控注意力聚合器（可学习查询 + mask softmax + 值端门控）
+    输入:  x: (B*, T, D), mask: (B*, T) bool
+    输出:  slots: (B*, R, D) 其中 R 为聚合插槽数（可配置）
+    """
+    def __init__(self, d_model: int, num_slots: int = 4, d_att: int = None, dropout: float = 0.1):
+        super().__init__()
+        self.d_model = d_model
+        self.R = num_slots
+        self.d_att = d_att or d_model
+
+        # 可学习查询（R个）
+        self.query = nn.Parameter(torch.randn(self.R, self.d_att) / (self.d_att ** 0.5))
+
+        # 线性投影
+        self.key_proj = nn.Linear(d_model, self.d_att)
+        self.val_proj = nn.Linear(d_model, d_model)
+
+        # 值端门控（逐token标量门）
+        self.gate = nn.Sequential(
+            nn.Linear(d_model, d_model // 2),
+            nn.GELU(),
+            nn.Linear(d_model // 2, 1),
+            nn.Sigmoid()
+        )
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x_bt_t_d: torch.Tensor, mask_bt: torch.Tensor) -> torch.Tensor:
+        """
+        x_bt_t_d: (B*, T, D)
+        mask_bt:  (B*, T) bool
+        return: slots (B*, R, D)
+        """
+        BStar, T, D = x_bt_t_d.shape
+        K = self.key_proj(x_bt_t_d)                # (B*, T, d_att)
+        V = self.val_proj(x_bt_t_d)                # (B*, T, D)
+        g = self.gate(x_bt_t_d)                    # (B*, T, 1)
+        Vg = V * g                                 # 门控后的值
+
+        Q = self.query.unsqueeze(0).expand(BStar, -1, -1)  # (B*, R, d_att)
+
+        logits = torch.matmul(Q, K.transpose(1, 2)) / (self.d_att ** 0.5)  # (B*, R, T)
+        attn_mask = mask_bt.unsqueeze(1)                                    # (B*, 1, T)
+        attn = masked_softmax(logits, attn_mask, dim=-1)
+        attn = self.dropout(attn)
+
+        slots = torch.matmul(attn, Vg)  # (B*, R, D)
+        return slots
+
+class AttnPoolHeadForecast(nn.Module):
+    """
+    预测任务头：门控注意力聚合到 R 个slots，再映射到 target_window（pred_len）
+    输出：(B, pred_len, nvars)
+    """
+    def __init__(self, d_model: int, target_window: int, num_slots: int = 4, dropout: float = 0.1):
+        super().__init__()
+        self.agg = GatedAttnAggregator(d_model, num_slots=num_slots, dropout=dropout)
+        self.proj = nn.Sequential(
+            nn.LayerNorm(num_slots * d_model),
+            nn.Linear(num_slots * d_model, target_window),
+        )
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, enc_out_bt_t_d: torch.Tensor, key_padding_mask_bt: torch.Tensor, n_vars: int, B: int):
+        slots = self.agg(enc_out_bt_t_d, key_padding_mask_bt)   # (B*, R, D)
+        slots = slots.reshape(B, n_vars, -1)                    # (B, nvars, R*D)
+        out = self.proj(self.dropout(slots))                    # (B, nvars, pred_len)
+        return out.permute(0, 2, 1)                             # (B, pred_len, nvars)
+
+class AttnPoolHeadSeq(nn.Module):
+    """
+    序列重建头：门控注意力聚合后映射到 seq_len
+    输出：(B, seq_len, nvars)
+    """
+    def __init__(self, d_model: int, target_window: int, num_slots: int = 4, dropout: float = 0.1):
+        super().__init__()
+        self.agg = GatedAttnAggregator(d_model, num_slots=num_slots, dropout=dropout)
+        self.proj = nn.Sequential(
+            nn.LayerNorm(num_slots * d_model),
+            nn.Linear(num_slots * d_model, target_window),
+        )
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, enc_out_bt_t_d: torch.Tensor, key_padding_mask_bt: torch.Tensor, n_vars: int, B: int):
+        slots = self.agg(enc_out_bt_t_d, key_padding_mask_bt)   # (B*, R, D)
+        slots = slots.reshape(B, n_vars, -1)                    # (B, nvars, R*D)
+        out = self.proj(self.dropout(slots))                    # (B, nvars, seq_len)
+        return out.permute(0, 2, 1)                             # (B, seq_len, nvars)
+
+class AttnPoolHeadCls(nn.Module):
+    """
+    分类头：每变量先门控注意力聚合到 R 个slots，拼接所有变量后线性分类。
+    输出：(B, num_class)
+    """
+    def __init__(self, d_model: int, n_vars: int, num_class: int, num_slots: int = 4, dropout: float = 0.1):
+        super().__init__()
+        self.agg = GatedAttnAggregator(d_model, num_slots=num_slots, dropout=dropout)
+        self.dropout = nn.Dropout(dropout)
+        self.proj = nn.Sequential(
+            nn.LayerNorm(n_vars * num_slots * d_model),
+            nn.Linear(n_vars * num_slots * d_model, num_class),
+        )
+        self.n_vars = n_vars
+        self.num_slots = num_slots
+        self.d_model = d_model
+
+    def forward(self, enc_out_bt_t_d: torch.Tensor, key_padding_mask_bt: torch.Tensor, n_vars: int, B: int):
+        slots = self.agg(enc_out_bt_t_d, key_padding_mask_bt)              # (B*, R, D)
+        slots = slots.reshape(B, n_vars, self.num_slots * self.d_model)    # (B, nvars, R*D)
+        flat = self.dropout(slots.reshape(B, -1))                          # (B, nvars*R*D)
+        return self.proj(flat)
+
+
+# -------------------- 主模型：两层DC(比率控制) + 带mask的Encoder + 门控聚合头 --------------------
+class Transpose(nn.Module):
+    def __init__(self, *dims, contiguous=False):
+        super().__init__()
+        self.dims, self.contiguous = dims, contiguous
+    def forward(self, x):
+        return x.transpose(*self.dims).contiguous() if self.contiguous else x.transpose(*self.dims)
+
+class Model(nn.Module):
+    """
+    PatchTST with DC and masked attention + gated heads:
+      - 用两层 Mamba2 编码器 + 动态分块 替代 PatchEmbedding
+      - DC 使用 ratio loss（target_ratio0/1）控制压缩强度；随层级加深，序列变短，d_model 变大（D0->D1）
+      - 注意力传入 key_padding_mask 屏蔽pad
+      - 头部使用门控注意力聚合（不依赖token数，信息保留更充分）
+    """
+
+    def __init__(
+        self, configs,
+        d_model_stage0=None,        # D0，默认= d_model // 2
+        depth_enc0=1, depth_enc1=1,
+        target_ratio0=0.25,         # 约等于 1/N0
+        target_ratio1=0.5,          # 约等于 1/N1
+        agg_slots=4,                # 门控聚合的slot数
+    ):
+        super().__init__()
+        self.task_name = configs.task_name
+        self.seq_len = configs.seq_len
+        self.pred_len = configs.pred_len
+        self.enc_in = configs.enc_in
+
+        # DC 嵌入
+        D1 = configs.d_model
+        D0 = d_model_stage0 if d_model_stage0 is not None else max(16, D1 // 2)
+        assert D1 >= D0, "要求 D0 <= D1"
+        self.dc_embedding = DCEmbedding2StageVarLen(
+            d_model_out=D1,
+            d_model_stage0=D0,
+            depth_enc0=depth_enc0,
+            depth_enc1=depth_enc1,
+            dropout=configs.dropout,
+            target_ratio0=target_ratio0,
+            target_ratio1=target_ratio1,
+        )
+
+        # 带mask的Encoder
+        attn_layers = [
+            EncoderLayerWithMask(
+                AttentionLayer(
+                    FullAttention(False, configs.factor, attention_dropout=configs.dropout, output_attention=False),
+                    D1, configs.n_heads
+                ),
+                d_model=D1,
+                d_ff=configs.d_ff,
+                dropout=configs.dropout,
+                activation=configs.activation
+            ) for _ in range(configs.e_layers)
+        ]
+        self.encoder = EncoderWithMask(
+            attn_layers,
+            norm_layer=nn.Sequential(Transpose(1, 2), nn.BatchNorm1d(D1), Transpose(1, 2))
+        )
+
+        # 门控聚合头（与token数无关）
+        if self.task_name in ('long_term_forecast', 'short_term_forecast'):
+            self.head = AttnPoolHeadForecast(D1, self.pred_len, num_slots=agg_slots, dropout=configs.dropout)
+        elif self.task_name in ('imputation', 'anomaly_detection'):
+            self.head = AttnPoolHeadSeq(D1, self.seq_len, num_slots=agg_slots, dropout=configs.dropout)
+        elif self.task_name == 'classification':
+            self.head_cls = AttnPoolHeadCls(D1, n_vars=self.enc_in, num_class=configs.num_class, num_slots=agg_slots, dropout=configs.dropout)
+
+    # --------- 归一化/反归一化 ---------
+    def _pre_norm(self, x):
+        means = x.mean(1, keepdim=True).detach()
+        x = x - means
+        stdev = torch.sqrt(torch.var(x, dim=1, keepdim=True, unbiased=False) + 1e-5)
+        x = x / stdev
+        return x, means, stdev
+
+    def _denorm(self, y, means, stdev, length):
+        return y * (stdev[:, 0, :].unsqueeze(1).repeat(1, length, 1)) + \
+               (means[:, 0, :].unsqueeze(1).repeat(1, length, 1))
+
+    # --------- DC + Transformer Encoder（携带 key_padding_mask） ----------
+    def _embed_and_encode(self, x_enc):
+        """
+        x_enc: (B, L, C)
+        返回：
+          enc_out: (B*nvars, T_max, D1)
+          n_vars: int
+          key_padding_mask: (B*nvars, T_max)
+          aux: dict
+        """
+        B, L, C = x_enc.shape
+        x_vars = x_enc.permute(0, 2, 1)  # (B, nvars, L)
+        enc_out, key_padding_mask, n_vars, aux = self.dc_embedding(x_vars)
+        enc_out, _ = self.encoder(enc_out, attn_mask=None, key_padding_mask=key_padding_mask)
+        return enc_out, n_vars, key_padding_mask, B, aux
+
+    # --------- 各任务前向 ---------
+    def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
+        x_enc, means, stdev = self._pre_norm(x_enc)
+        enc_out, n_vars, key_padding_mask, B, aux = self._embed_and_encode(x_enc)
+        dec_out = self.head(enc_out, key_padding_mask, n_vars, B)  # (B, pred_len, nvars)
+        dec_out = self._denorm(dec_out, means, stdev, self.pred_len)
+        return dec_out, aux
+
+    def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):
+        means = torch.sum(x_enc, dim=1) / torch.sum(mask == 1, dim=1)
+        means = means.unsqueeze(1).detach()
+        x = x_enc - means
+        x = x.masked_fill(mask == 0, 0)
+        stdev = torch.sqrt(torch.sum(x * x, dim=1) / torch.sum(mask == 1, dim=1) + 1e-5)
+        stdev = stdev.unsqueeze(1).detach()
+        x = x / stdev
+
+        enc_out, n_vars, key_padding_mask, B, aux = self._embed_and_encode(x)
+        dec_out = self.head(enc_out, key_padding_mask, n_vars, B)  # (B, seq_len, nvars)
+        dec_out = self._denorm(dec_out, means, stdev, self.seq_len)
+        return dec_out, aux
+
+    def anomaly_detection(self, x_enc):
+        x_enc, means, stdev = self._pre_norm(x_enc)
+        enc_out, n_vars, key_padding_mask, B, aux = self._embed_and_encode(x_enc)
+        dec_out = self.head(enc_out, key_padding_mask, n_vars, B)  # (B, seq_len, nvars)
+        dec_out = self._denorm(dec_out, means, stdev, self.seq_len)
+        return dec_out, aux
+
+    def classification(self, x_enc, x_mark_enc):
+        x_enc, _, _ = self._pre_norm(x_enc)
+        enc_out, n_vars, key_padding_mask, B, aux = self._embed_and_encode(x_enc)
+        logits = self.head_cls(enc_out, key_padding_mask, n_vars, B)  # (B, num_class)
+        return logits, aux
+
+    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
+        if self.task_name in ('long_term_forecast', 'short_term_forecast'):
+            dec_out, aux = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
+            return dec_out[:, -self.pred_len:, :], aux  # [B, L, D], aux含ratio losses
+        if self.task_name == 'imputation':
+            dec_out, aux = self.imputation(x_enc, x_mark_enc, x_dec, x_mark_dec, mask)
+            return dec_out, aux
+        if self.task_name == 'anomaly_detection':
+            dec_out, aux = self.anomaly_detection(x_enc)
+            return dec_out, aux
+        if self.task_name == 'classification':
+            logits, aux = self.classification(x_enc, x_mark_enc)
+            return logits, aux
+        return None, None

models/DC_hnet.py

@@ -0,0 +1,339 @@
+from dataclasses import dataclass
+from typing import Optional, Literal, List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# 来自你的代码库（可直接使用）
+from hnet.modules.dc import RoutingModule, ChunkLayer
+from hnet.modules.isotropic import Isotropic
+from hnet.models.config_hnet import HNetConfig, SSMConfig, AttnConfig
+
+# -------------------- 辅助 --------------------
+def create_isotropic_encoder(d_model, arch="m", height=4, device=None, dtype=None):
+    """创建简化的Isotropic编码器"""
+    factory_kwargs = {"device": device, "dtype": dtype}
+    
+    # 创建HNetConfig，确保list字段有足够的元素
+    config = HNetConfig(
+        arch_layout=[f"{arch}{height}"],
+        d_model=[d_model], 
+        d_intermediate=[d_model * 2],
+        ssm_cfg=SSMConfig(
+            d_conv=4,
+            expand=2,
+            d_state=128,
+            chunk_size=256
+        ),
+        attn_cfg=AttnConfig(
+            num_heads=[8],          # 确保有至少一个元素
+            rotary_emb_dim=[0],     # 确保有至少一个元素  
+            window_size=[-1]        # 确保有至少一个元素
+        )
+    )
+    
+    return Isotropic(
+        config=config,
+        pos_idx=0,
+        stage_idx=0,
+        **factory_kwargs
+    )
+
+def ratio_loss(boundary_mask: torch.Tensor, boundary_prob: torch.Tensor, target_N: int) -> torch.Tensor:
+    F_act = boundary_mask.float().mean(dim=1)      # (B,)
+    G_prob = boundary_prob[..., 1].mean(dim=1)     # (B,)
+    N = float(target_N)
+    loss = N / (N - 1.0) * (((N - 1.0) * F_act) + (1.0 - F_act) * (1.0 - G_prob))
+    return loss.mean()
+
+def masked_mean(x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+    mask_f = mask.float().unsqueeze(-1)     # (B, L, 1)
+    s = (x * mask_f).sum(dim=1)             # (B, D)
+    denom = mask_f.sum(dim=1).clamp_min(1.0)
+    return s / denom
+
+# -------------------- 多层Encoder（金字塔）：每层Mamba2 + 路由下采样，只有最终有主网络 --------------------
+class PyramidEncoders_NoDechunk(nn.Module):
+    """
+    层级结构（仅编码器逐层压缩；主网络只在最终一层）：
+      输入 x0: (B, L0, 1)
+      - 线性升维 -> D0
+      For s = 0..S-1:
+        Es(Mamba2, D_s) -> h_s (B, L_s, D_s)
+        路由 + 下采样 -> x_{s+1} (B, L_{s+1}, D_s), mask_{s+1}
+        维度扩展 D_s -> D_{s+1}（拼接共享向量）
+      最终 x_S: (B, L_S, D_S) 送入单一主网络 M (Transformer/Mamba)
+    跨尺度融合（不去分块）：融合 E^0 的 pooled_enc0 与 主网络 pooled_main
+    """
+    def __init__(
+        self,
+        d_models: List[int],                    # [D0, D1, ..., D_S] 单调非降
+        encoder_cfg_per_stage: List[dict],      # S个编码器配置（必须 arch='m'/'M'）
+        main_cfg: dict,                         # 单一主网络配置（在最压缩序列上工作）
+        fusion_dropout: float = 0.1,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+    ):
+        super().__init__()
+        factory_kwargs = {"device": device, "dtype": dtype}
+
+        assert len(d_models) >= 1
+        S = len(d_models) - 1
+        assert S == len(encoder_cfg_per_stage), "stage数等于encoder配置数"
+        for i in range(S):
+            assert d_models[i+1] >= d_models[i], "需满足 D_s <= D_{s+1}（宽度单调增加）"
+            assert encoder_cfg_per_stage[i].get("arch", "m") in ("m", "M"), "Encoder必须为Mamba2"
+
+        self.S = S
+        self.d_models = d_models
+
+        # 输入升维到 D0
+        self.input_proj = nn.Linear(1, d_models[0], **factory_kwargs)
+
+        # 每层编码器 + 路由 + 下采样 + 扩宽参数
+        self.encoders = nn.ModuleList()
+        self.routers = nn.ModuleList()
+        self.chunks = nn.ModuleList()
+        self.pad_vectors = nn.ParameterList()
+        for s in range(S):
+            self.encoders.append(
+                create_isotropic_encoder(
+                    d_model=d_models[s], 
+                    **{k: v for k, v in encoder_cfg_per_stage[s].items() if k != "d_model"}, 
+                    **factory_kwargs
+                )
+            )
+            self.routers.append(RoutingModule(d_models[s], **factory_kwargs))
+            self.chunks.append(ChunkLayer())
+            delta = d_models[s+1] - d_models[s]
+            self.pad_vectors.append(nn.Parameter(torch.zeros(delta, **factory_kwargs)) if delta > 0 else nn.Parameter(torch.empty(0, **factory_kwargs)))
+
+        # 最终唯一的主网络：在 D_S & L_S 上运行
+        self.main_network = create_isotropic_encoder(
+            d_model=d_models[-1], 
+            **{k: v for k, v in main_cfg.items() if k != "d_model"}, 
+            **factory_kwargs
+        )
+
+        # 跨尺度融合：将 pooled_enc0(D0) 投到 D_S 并与 pooled_main(D_S) 融合 -> D_S
+        self.proj_enc0_to_DS = nn.Linear(d_models[0], d_models[-1], **factory_kwargs)
+        self.fusion_head = nn.Sequential(
+            nn.Linear(d_models[-1] + d_models[-1], d_models[-1], **factory_kwargs),
+            nn.GELU(),
+            nn.Dropout(fusion_dropout),
+            nn.Linear(d_models[-1], d_models[-1], **factory_kwargs),
+        )
+
+    def _expand_width(self, x: torch.Tensor, pad_vec: nn.Parameter) -> torch.Tensor:
+        if pad_vec.numel() == 0:
+            return x
+        early = x.shape[:-1]
+        return torch.cat([x, pad_vec.expand(*early, -1)], dim=-1)
+
+    def forward(self, x_scalar: torch.Tensor, mask: Optional[torch.Tensor] = None, return_seq: bool = False):
+        """
+        x_scalar: (B, L) 或 (B, L, 1)
+        mask: (B, L) bool
+        返回:
+          fused_vec: (B, D_S)
+          debug: 可选
+          aux: 包含各层路由信息（供ratio loss）
+        """
+        if x_scalar.dim() == 2:
+            x_scalar = x_scalar.unsqueeze(-1)  # (B, L, 1)
+        B, L, _ = x_scalar.shape
+        device = x_scalar.device
+        if mask is None:
+            mask = torch.ones(B, L, dtype=torch.bool, device=device)
+
+        # 初始升维到 D0
+        x = self.input_proj(x_scalar)  # (B, L0, D0)
+        cur_mask = mask
+
+        pooled_enc0 = None
+        aux_per_stage = []
+        seq_debug = [] if return_seq else None
+
+        # 逐层：Encoder(Mamba2)->Routing->Chunk->Expand D
+        for s in range(self.S):
+            d_in = self.d_models[s]
+            # 细粒度编码（未压缩序列）
+            h_enc = self.encoders[s](x, mask=cur_mask)  # (B, L_s, D_s)
+
+            if s == 0:
+                pooled_enc0 = masked_mean(h_enc, cur_mask)  # (B, D0)
+
+            # 路由 + 下采样（得到更短序列）
+            bpred = self.routers[s](h_enc, mask=cur_mask)
+            x_next, _, _, mask_next = self.chunks[s](h_enc, bpred.boundary_mask, mask=cur_mask)  # (B, L_{s+1}, D_s)
+
+            # 扩展宽度 D_s -> D_{s+1}
+            x_next = self._expand_width(x_next, self.pad_vectors[s])  # (B, L_{s+1}, D_{s+1})
+
+            # 推进到下一层
+            x, cur_mask = x_next, mask_next
+
+            aux_per_stage.append({
+                "boundary_mask": bpred.boundary_mask,
+                "boundary_prob": bpred.boundary_prob,
+                "selected_probs": bpred.selected_probs,
+            })
+            if return_seq:
+                seq_debug.append({"stage": s, "seq": x, "mask": cur_mask})
+
+        # 现在 x: (B, L_S, D_S), cur_mask: (B, L_S)
+        # 最终单一主网络在最压缩序列上
+        h_main = self.main_network(x, mask=cur_mask)  # (B, L_S, D_S)
+
+        # 主网络池化
+        if cur_mask is None:
+            pooled_main = h_main.mean(dim=1)  # (B, D_S)
+        else:
+            pooled_main = (h_main * cur_mask.float().unsqueeze(-1)).sum(dim=1) / \
+                          cur_mask.float().sum(dim=1, keepdim=True).clamp_min(1.0)
+
+        # 跨尺度融合：E^0 全局池化 与 主网络池化
+        pooled_enc0_in_DS = self.proj_enc0_to_DS(pooled_enc0)  # (B, D_S)
+        fused = torch.cat([pooled_enc0_in_DS, pooled_main], dim=-1)  # (B, 2*D_S)
+        fused = self.fusion_head(fused)  # (B, D_S)
+
+        aux = {"per_stage": aux_per_stage}
+        if return_seq:
+            return fused, {"stages": seq_debug, "main_seq": h_main, "main_mask": cur_mask}, aux
+        else:
+            return fused, None, aux
+
+# -------------------- 顶层：多通道融合 + 分类头（仅一个主网络） --------------------
+@dataclass
+class HierEncodersSingleMainConfig:
+    num_channels: int
+    d_models: List[int]                      # [D0, D1, ..., D_S] 单调非降
+    num_classes: int
+    encoder_cfg_per_stage: List[dict]        # S个编码器配置（均为Mamba2, height≈4）
+    main_cfg: dict                           # 单一主网络配置（Transformer或Mamba2），d_model自动用D_S
+    target_compression_N_per_stage: List[int]
+    share_channel: bool = True
+    fusion_across_channels: Literal["mean", "concat"] = "mean"
+    dropout: float = 0.1
+
+class HierEncodersSingleMainClassifier(nn.Module):
+    def __init__(self, cfg: HierEncodersSingleMainConfig, dtype=None, device=None):
+        super().__init__()
+        self.cfg = cfg
+        factory_kwargs = {"dtype": dtype, "device": device}
+
+        S = len(cfg.d_models) - 1
+        assert S == len(cfg.encoder_cfg_per_stage) == len(cfg.target_compression_N_per_stage), "stage数不一致"
+
+        if cfg.share_channel:
+            self.channel_encoder = PyramidEncoders_NoDechunk(
+                d_models=cfg.d_models,
+                encoder_cfg_per_stage=cfg.encoder_cfg_per_stage,
+                main_cfg=cfg.main_cfg,
+                **factory_kwargs,
+            )
+        else:
+            self.channel_encoder = nn.ModuleList([
+                PyramidEncoders_NoDechunk(
+                    d_models=cfg.d_models,
+                    encoder_cfg_per_stage=cfg.encoder_cfg_per_stage,
+                    main_cfg=cfg.main_cfg,
+                    **factory_kwargs,
+                )
+                for _ in range(cfg.num_channels)
+            ])
+
+        fusion_dim = (cfg.num_channels * cfg.d_models[-1]) if cfg.fusion_across_channels == "concat" \
+                     else cfg.d_models[-1]
+        self.dropout = nn.Dropout(cfg.dropout)
+        self.head = nn.Linear(fusion_dim, cfg.num_classes, **factory_kwargs)
+
+    def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None, return_seq: bool = False):
+        """
+        x: (B, L, N)  多通道输入
+        mask: (B, L)  时序mask
+        """
+        B, L, N = x.shape
+        assert N == self.cfg.num_channels
+
+        channel_vecs: List[torch.Tensor] = []
+        ratio_losses = []
+        seq_dbg_all = [] if return_seq else None
+
+        for c in range(N):
+            x_c = x[..., c]  # (B, L)
+            if self.cfg.share_channel:
+                vec, seq_dbg, aux = self.channel_encoder(x_c, mask=mask, return_seq=return_seq)
+            else:
+                vec, seq_dbg, aux = self.channel_encoder[c](x_c, mask=mask, return_seq=return_seq)
+
+            # ratio loss 累加（每个encoder stage一项）
+            total_rl = 0.0
+            for s, aux_s in enumerate(aux["per_stage"]):
+                rl = ratio_loss(aux_s["boundary_mask"], aux_s["boundary_prob"], self.cfg.target_compression_N_per_stage[s])
+                total_rl = total_rl + rl
+            ratio_losses.append(total_rl)
+
+            channel_vecs.append(vec)
+            if return_seq:
+                seq_dbg_all.append(seq_dbg)
+
+        if self.cfg.fusion_across_channels == "concat":
+            fused = torch.cat(channel_vecs, dim=-1)  # (B, N*D_S)
+        else:
+            fused = torch.stack(channel_vecs, dim=1).mean(dim=1)  # (B, D_S)
+
+        fused = self.dropout(fused)
+        logits = self.head(fused)
+
+        aux_all = {"ratio_loss": torch.stack(ratio_losses).mean()}
+        if return_seq:
+            return logits, seq_dbg_all, aux_all
+        else:
+            return logits, None, aux_all
+
+# -------------------- 使用示例 --------------------
+if __name__ == "__main__":
+    """
+    符合要求：
+      - 多层仅增加编码器数量（每层Mamba2 + 动态分块），主网络只有最终一个
+      - 序列长度逐层缩短（由DC决定），通道维度 d_model 单调增大（SpaceByte式共享向量拼接）
+      - 不使用去分块（dechunk）；跨尺度融合用 E^0 的全局池化 + 最终主网络池化
+    """
+    B, L, N = 8, 1024, 6
+    num_classes = 7
+    d_models = [128, 256, 512]  # D0 <= D1 <= D2
+
+    encoder_cfg_per_stage = [
+        dict(arch="m", height=4, ssm_cfg=dict(), attn_cfg=dict()),  # stage 0 encoder (Mamba2)
+        dict(arch="m", height=4, ssm_cfg=dict(), attn_cfg=dict()),  # stage 1 encoder (Mamba2)
+    ]
+    main_cfg = dict(
+        arch="T", height=12, ssm_cfg=dict(), attn_cfg=dict(num_heads=8)  # 最终主网络（较重）
+    )
+    target_compression_N_per_stage = [4, 4]
+
+    cfg = HierEncodersSingleMainConfig(
+        num_channels=N,
+        d_models=d_models,
+        num_classes=num_classes,
+        encoder_cfg_per_stage=encoder_cfg_per_stage,
+        main_cfg=main_cfg,
+        target_compression_N_per_stage=target_compression_N_per_stage,
+        share_channel=True,
+        fusion_across_channels="mean",
+        dropout=0.1,
+    )
+
+    model = HierEncodersSingleMainClassifier(cfg).cuda().train()
+    x = torch.randn(B, L, N, device="cuda")
+    mask = torch.ones(B, L, dtype=torch.bool, device="cuda")
+
+    logits, _, aux = model(x, mask=mask, return_seq=False)
+    y = torch.randint(0, num_classes, (B,), device="cuda")
+    cls_loss = F.cross_entropy(logits, y)
+    ratio_reg = 0.03 * aux["ratio_loss"]
+    loss = cls_loss + ratio_reg
+    loss.backward()
+    print("logits:", logits.shape, "loss:", float(loss))

models/vanillaMamba-Copy1.py

@@ -0,0 +1,138 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from mamba_ssm import Mamba2
+
+
+class ValueEmbedding(nn.Module):
+    """
+    对每个时间步的单通道标量做线性投影到 d_model，并可选 Dropout。
+    不包含 temporal embedding 和 positional embedding。
+    """
+    def __init__(self, in_dim: int, d_model: int, dropout: float = 0.0, bias: bool = True):
+        super().__init__()
+        self.proj = nn.Linear(in_dim, d_model, bias=bias)
+        self.dropout = nn.Dropout(dropout) if dropout and dropout > 0.0 else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # x: [B, L, 1] -> [B, L, d_model]
+        return self.dropout(self.proj(x))
+
+
+class ChannelMambaBlock(nn.Module):
+    """
+    针对单个通道的两层 Mamba-2 处理块：
+      - 输入: [B, L, 1]，先做投影到 d_model
+      - 两层 Mamba2，且在第一层输出和第二层输出均添加残差连接
+      - 每层后接 LayerNorm
+      - 输出: [B, L, d_model]
+    """
+    def __init__(self, d_model: int, dropout: float, m2_kwargs: dict):
+        super().__init__()
+        self.embed = ValueEmbedding(in_dim=1, d_model=d_model, dropout=dropout, bias=True)
+
+        # 两层 Mamba-2
+        self.mamba1 = Mamba2(d_model=d_model, **m2_kwargs)
+        self.mamba2 = Mamba2(d_model=d_model, **m2_kwargs)
+
+        # 每层后接的归一化
+        self.ln1 = nn.LayerNorm(d_model)
+        self.ln2 = nn.LayerNorm(d_model)
+
+    def forward(self, x_ch: torch.Tensor) -> torch.Tensor:
+        # x_ch: [B, L, 1]
+        x = self.embed(x_ch)  # [B, L, d_model]
+
+        # 第一层 + 残差
+        y1 = self.mamba1(x)           # [B, L, d_model]
+        y1 = self.ln1(x + y1)         # 残差1 + LN
+
+        # 第二层 + 残差
+        y2 = self.mamba2(y1)          # [B, L, d_model]
+        y2 = self.ln2(y1 + y2)        # 残差2 + LN
+
+        return y2                      # [B, L, d_model]
+
+
+class Model(nn.Module):
+    """
+    按通道独立处理的 Mamba-2 分类模型：
+      - 将输入的每个通道拆开，分别使用独立的两层 Mamba2（含两处残差）
+      - 每个通道得到 [B, L, d_model] 输出
+      - 取各通道最后时间步的表示拼接，接分类头
+    输入:
+      - x_enc: [B, L, D] 多变量时间序列
+    输出:
+      - logits: [B, num_class]
+    """
+    def __init__(self, configs):
+        super().__init__()
+        self.task_name = getattr(configs, 'task_name', 'classification')
+        assert self.task_name == 'classification', "当前模型仅实现 classification 任务"
+
+        # 基本配置
+        self.enc_in = configs.enc_in            # 通道数 D
+        self.d_model = configs.d_model          # 每通道的模型维度
+        self.num_class = configs.num_class
+        self.dropout = getattr(configs, 'dropout', 0.1)
+
+        # Mamba-2 超参数（按需从 configs 读取）
+        # 注意：此处不再使用 e_layers 的堆叠，而是固定每通道两层以满足“在第一层和第二层输出处添加残差”的要求
+        m2_kwargs = dict(
+            d_state=getattr(configs, 'd_state', 64),
+            d_conv=getattr(configs, 'd_conv', 4),
+            expand=getattr(configs, 'expand', 2),
+            headdim=getattr(configs, 'headdim', 64),
+            d_ssm=getattr(configs, 'd_ssm', None),
+            ngroups=getattr(configs, 'ngroups', 1),
+            A_init_range=getattr(configs, 'A_init_range', (1, 16)),
+            D_has_hdim=getattr(configs, 'D_has_hdim', False),
+            rmsnorm=getattr(configs, 'rmsnorm', True),
+            norm_before_gate=getattr(configs, 'norm_before_gate', False),
+            dt_min=getattr(configs, 'dt_min', 0.001),
+            dt_max=getattr(configs, 'dt_max', 0.1),
+            dt_init_floor=getattr(configs, 'dt_init_floor', 1e-4),
+            dt_limit=getattr(configs, 'dt_limit', (0.0, float("inf"))),
+            bias=getattr(configs, 'bias', False),
+            conv_bias=getattr(configs, 'conv_bias', True),
+            chunk_size=getattr(configs, 'chunk_size', 256),
+            use_mem_eff_path=getattr(configs, 'use_mem_eff_path', True),
+        )
+
+        # 为每个通道构建独立的两层 Mamba2 处理块
+        self.channel_blocks = nn.ModuleList([
+            ChannelMambaBlock(d_model=self.d_model, dropout=self.dropout, m2_kwargs=m2_kwargs)
+            for _ in range(self.enc_in)
+        ])
+
+        # 分类头：将各通道最后时间步的表示拼接后 -> GELU -> Dropout -> Linear
+        self.act = nn.GELU()
+        self.head = nn.Sequential(
+            nn.Dropout(self.dropout),
+            nn.Linear(self.d_model * self.enc_in, self.num_class)
+        )
+
+    def classification(self, x_enc: torch.Tensor) -> torch.Tensor:
+        # x_enc: [B, L, D]
+        B, L, D = x_enc.shape
+        assert D == self.enc_in, f"输入通道数 {D} 与 enc_in {self.enc_in} 不一致"
+
+        per_channel_last = []
+        for c in range(D):
+            # 取出单通道序列 [B, L] -> [B, L, 1]
+            x_ch = x_enc[:, :, c].unsqueeze(-1)
+            y_ch = self.channel_blocks[c](x_ch)   # [B, L, d_model]
+            per_channel_last.append(y_ch[:, -1, :])  # [B, d_model]
+
+        # 拼接各通道最后时刻的表示 -> [B, D * d_model]
+        h_last = torch.cat(per_channel_last, dim=-1)
+
+        # 分类头
+        h_last = self.act(h_last)
+        logits = self.head(h_last)   # [B, num_class]
+        return logits
+
+    # 与 TimesNet 的 forward 签名保持一致；忽略 x_mark_enc / x_dec / x_mark_dec / mask
+    def forward(self, x_enc, x_mark_enc=None, x_dec=None, x_mark_dec=None, mask=None):
+        return self.classification(x_enc)

models/vanillaMamba.py

@@ -0,0 +1,203 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from mamba_ssm import Mamba2
+
+
+class ValueEmbedding(nn.Module):
+    """
+    对每个时间步的单通道标量做线性投影到 d_model，并可选 Dropout。
+    不包含 temporal embedding 和 positional embedding。
+    """
+    def __init__(self, in_dim: int, d_model: int, dropout: float = 0.0, bias: bool = True):
+        super().__init__()
+        self.proj = nn.Linear(in_dim, d_model, bias=bias)
+        self.dropout = nn.Dropout(dropout) if dropout and dropout > 0.0 else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # x: [B, L, 1] -> [B, L, d_model]
+        return self.dropout(self.proj(x))
+
+
+class ChannelMambaBlock(nn.Module):
+    """
+    针对单个通道的两层 Mamba-2 处理块：
+      - 输入: [B, L, 1]，先做投影到 d_model
+      - 两层 Mamba2，且在第一层输出和第二层输出均添加残差连接
+      - 每层后接 LayerNorm
+      - 输出: [B, L, d_model]
+    """
+    def __init__(self, d_model: int, dropout: float, m2_kwargs: dict):
+        super().__init__()
+        self.embed = ValueEmbedding(in_dim=1, d_model=d_model, dropout=dropout, bias=True)
+
+        # 两层 Mamba-2
+        self.mamba1 = Mamba2(d_model=d_model, **m2_kwargs)
+        self.mamba2 = Mamba2(d_model=d_model, **m2_kwargs)
+
+        # 每层后接的归一化
+        self.ln1 = nn.LayerNorm(d_model)
+        self.ln2 = nn.LayerNorm(d_model)
+
+    def forward(self, x_ch: torch.Tensor) -> torch.Tensor:
+        # x_ch: [B, L, 1]
+        x = self.embed(x_ch)  # [B, L, d_model]
+
+        # 第一层 + 残差
+        y1 = self.mamba1(x)           # [B, L, d_model]
+        y1 = self.ln1(x + y1)         # 残差1 + LN
+
+        # 第二层 + 残差
+        y2 = self.mamba2(y1)          # [B, L, d_model]
+        y2 = self.ln2(y1 + y2)        # 残差2 + LN
+
+        return y2                      # [B, L, d_model]
+
+
+class Model(nn.Module):
+    """
+    按通道独立处理的 Mamba-2 模型，支持：
+      - 分类：各通道独立提取，取最后时刻拼接 -> 分类头
+      - 长/短期预测：各通道独立提取，保留整段序列，经时间维线性映射到目标长度，再投影回标量并拼接
+        注意：预测输出通道数与输入通道数严格相同（逐通道预测）。
+
+    输入:
+      - x_enc: [B, L, D] 多变量时间序列
+      - x_mark_enc, x_dec, x_mark_dec, mask: 兼容接口参数（本模型在分类/预测中未使用这些标注）
+
+    输出:
+      - classification: logits [B, num_class]
+      - forecast: [B, pred_len, D]
+    """
+    def __init__(self, configs):
+        super().__init__()
+        # 任务类型
+        self.task_name = getattr(configs, 'task_name', 'classification')
+        assert self.task_name in ['classification', 'long_term_forecast', 'short_term_forecast'], \
+            "只支持 classification / long_term_forecast / short_term_forecast"
+
+        # 基本配置
+        self.enc_in = configs.enc_in             # 通道数 D
+        self.d_model = configs.d_model           # 每通道的模型维度
+        self.num_class = getattr(configs, 'num_class', None)
+        self.dropout = getattr(configs, 'dropout', 0.1)
+
+        # 预测相关
+        self.seq_len = getattr(configs, 'seq_len', None)
+        self.pred_len = getattr(configs, 'pred_len', None)
+        if self.task_name in ['long_term_forecast', 'short_term_forecast']:
+            assert self.seq_len is not None and self.pred_len is not None, "预测任务需要 seq_len 与 pred_len"
+        # 输出通道必须与输入通道一致
+        self.c_out = getattr(configs, 'c_out', self.enc_in)
+        assert self.c_out == self.enc_in, "预测任务要求输出通道 c_out 与输入通道 enc_in 一致"
+
+        # Mamba-2 超参数
+        m2_kwargs = dict(
+            d_state=getattr(configs, 'd_state', 64),
+            d_conv=getattr(configs, 'd_conv', 4),
+            expand=getattr(configs, 'expand', 2),
+            headdim=getattr(configs, 'headdim', 64),
+            d_ssm=getattr(configs, 'd_ssm', None),
+            ngroups=getattr(configs, 'ngroups', 1),
+            A_init_range=getattr(configs, 'A_init_range', (1, 16)),
+            D_has_hdim=getattr(configs, 'D_has_hdim', False),
+            rmsnorm=getattr(configs, 'rmsnorm', True),
+            norm_before_gate=getattr(configs, 'norm_before_gate', False),
+            dt_min=getattr(configs, 'dt_min', 0.001),
+            dt_max=getattr(configs, 'dt_max', 0.1),
+            dt_init_floor=getattr(configs, 'dt_init_floor', 1e-4),
+            dt_limit=getattr(configs, 'dt_limit', (0.0, float("inf"))),
+            bias=getattr(configs, 'bias', False),
+            conv_bias=getattr(configs, 'conv_bias', True),
+            chunk_size=getattr(configs, 'chunk_size', 256),
+            use_mem_eff_path=getattr(configs, 'use_mem_eff_path', True),
+        )
+
+        # 为每个通道构建独立的两层 Mamba2 处理块
+        self.channel_blocks = nn.ModuleList([
+            ChannelMambaBlock(d_model=self.d_model, dropout=self.dropout, m2_kwargs=m2_kwargs)
+            for _ in range(self.enc_in)
+        ])
+
+        # 分类头：将各通道最后时间步的表示拼接后 -> GELU -> Dropout -> Linear
+        if self.task_name == 'classification':
+            assert self.num_class is not None, "classification 需要提供 num_class"
+            self.act = nn.GELU()
+            self.head = nn.Sequential(
+                nn.Dropout(self.dropout),
+                nn.Linear(self.d_model * self.enc_in, self.num_class)
+            )
+
+        # 预测头：
+        # - 先对时间维做线性映射： [B, L, d_model] -> [B, pred_len, d_model]
+        # - 再将 d_model 投影为单通道标量： [B, pred_len, d_model] -> [B, pred_len, 1]
+        if self.task_name in ['long_term_forecast', 'short_term_forecast']:
+            self.predict_linear = nn.Linear(self.seq_len, self.pred_len)
+            self.projection = nn.Linear(self.d_model, 1, bias=True)
+
+    def classification(self, x_enc: torch.Tensor) -> torch.Tensor:
+        # x_enc: [B, L, D]
+        B, L, D = x_enc.shape
+        assert D == self.enc_in, f"输入通道数 {D} 与 enc_in {self.enc_in} 不一致"
+
+        per_channel_last = []
+        for c in range(D):
+            # 取出单通道序列 [B, L] -> [B, L, 1]
+            x_ch = x_enc[:, :, c].unsqueeze(-1)
+            y_ch = self.channel_blocks[c](x_ch)        # [B, L, d_model]
+            per_channel_last.append(y_ch[:, -1, :])    # [B, d_model]
+
+        # 拼接各通道最后时刻的表示 -> [B, D * d_model]
+        h_last = torch.cat(per_channel_last, dim=-1)
+
+        # 分类头
+        logits = self.head(self.act(h_last))           # [B, num_class]
+        return logits
+
+    def forecast(self, x_enc: torch.Tensor) -> torch.Tensor:
+        """
+        逐通道预测：
+          - 归一化（时间维），按通道独立提取
+          - 使用整段 Mamba 输出序列，经时间维线性映射到目标长度，再投影为标量
+          - 反归一化
+        返回：
+          dec_out: [B, L+pred_len, D]，在 forward 中会取最后 pred_len 段
+        """
+        B, L, D = x_enc.shape
+        assert L == self.seq_len, f"输入长度 {L} 与配置 seq_len {self.seq_len} 不一致"
+        assert D == self.enc_in, f"输入通道数 {D} 与 enc_in {self.enc_in} 不一致"
+
+        # Normalization (per Non-stationary Transformer)
+        means = x_enc.mean(1, keepdim=True).detach()                          # [B, 1, D]
+        x = x_enc - means
+        stdev = torch.sqrt(x.var(dim=1, keepdim=True, unbiased=False) + 1e-5) # [B, 1, D]
+        x = x / stdev
+
+        per_channel_seq = []
+        for c in range(D):
+            x_ch = x[:, :, c].unsqueeze(-1)                    # [B, L, 1]
+            h_ch = self.channel_blocks[c](x_ch)                # [B, L, d_model]
+            # 时间维映射到 L + pred_len
+            h_ch = self.predict_linear(h_ch.permute(0, 2, 1)).permute(0, 2, 1)  # [B, L+pred_len, d_model]
+            # 投影回单通道
+            y_ch = self.projection(h_ch)                       # [B, L+pred_len, 1]
+            per_channel_seq.append(y_ch)
+
+        # 拼接通道
+        dec_out = torch.cat(per_channel_seq, dim=-1)           # [B, pred_len, D]
+
+        # De-normalization
+        dec_out = dec_out * stdev[:, 0, :].unsqueeze(1) + means[:, 0, :].unsqueeze(1)
+
+        return dec_out
+
+    # 与 TimesNet 的 forward 签名保持一致；忽略 x_mark_enc / x_dec / x_mark_dec / mask
+    def forward(self, x_enc, x_mark_enc=None, x_dec=None, x_mark_dec=None, mask=None):
+        if self.task_name in ['long_term_forecast', 'short_term_forecast']:
+            dec_out = self.forecast(x_enc)                       # [B, L+pred_len, D]
+            return dec_out[:, -self.pred_len:, :]                # 仅返回预测部分 [B, pred_len, D]
+        elif self.task_name == 'classification':
+            return self.classification(x_enc)
+        else:
+            raise NotImplementedError(f"Unsupported task: {self.task_name}")

run.py

@@ -7,6 +7,7 @@ from exp.exp_imputation import Exp_Imputation
 from exp.exp_short_term_forecasting import Exp_Short_Term_Forecast
 from exp.exp_anomaly_detection import Exp_Anomaly_Detection
 from exp.exp_classification import Exp_Classification
+from exp.exp_dc_patchtst_classification import Exp_DC_PatchTST_Classification
 from utils.print_args import print_args
 import random
 import numpy as np
@@ -191,7 +192,10 @@ if __name__ == '__main__':
     elif args.task_name == 'anomaly_detection':
         Exp = Exp_Anomaly_Detection
     elif args.task_name == 'classification':
-        Exp = Exp_Classification
+        if args.model == 'DC_PatchTST':
+            Exp = Exp_DC_PatchTST_Classification
+        else:
+            Exp = Exp_Classification
     else:
         Exp = Exp_Long_Term_Forecast
 

scripts/classification/DC_PatchTST.sh

@@ -0,0 +1,142 @@
+export CUDA_VISIBLE_DEVICES=0
+
+model_name=DC_PatchTST
+
+
+
+# DC_PatchTST specific parameters
+d_model_stage0=64        # Stage 0 dimension (D0)
+depth_enc0=1             # Stage 0 Mamba2 encoder depth  
+depth_enc1=1             # Stage 1 Mamba2 encoder depth
+target_ratio0=0.25       # Target compression ratio for stage 0
+target_ratio1=0.25        # Target compression ratio for stage 1
+
+# EthanolConcentration dataset
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/EthanolConcentration/ \
+  --model_id EthanolConcentration \
+  --model $model_name \
+  --data UEA \
+  --e_layers 3 \
+  --batch_size 8 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 8 \
+  --dropout 0.1 \
+  --activation gelu \
+  --des 'DC_PatchTST_Exp' \
+  --itr 1 \
+  --learning_rate 0.0002 \
+  --train_epochs 100 \
+  --patience 10 \
+  --d_model_stage0 $d_model_stage0 \
+  --depth_enc0 $depth_enc0 \
+  --depth_enc1 $depth_enc1 \
+  --target_ratio0 $target_ratio0 \
+  --target_ratio1 $target_ratio1
+
+# FaceDetection dataset  
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/FaceDetection/ \
+  --model_id FaceDetection \
+  --model $model_name \
+  --data UEA \
+  --e_layers 3 \
+  --batch_size 8 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 8 \
+  --dropout 0.1 \
+  --activation gelu \
+  --des 'DC_PatchTST_Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 100 \
+  --patience 10 \
+  --d_model_stage0 $d_model_stage0 \
+  --depth_enc0 $depth_enc0 \
+  --depth_enc1 $depth_enc1 \
+  --target_ratio0 $target_ratio0 \
+  --target_ratio1 $target_ratio1
+
+# Handwriting dataset
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/Handwriting/ \
+  --model_id Handwriting \
+  --model $model_name \
+  --data UEA \
+  --e_layers 3 \
+  --batch_size 8 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 8 \
+  --dropout 0.1 \
+  --activation gelu \
+  --des 'DC_PatchTST_Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 100 \
+  --patience 10 \
+  --d_model_stage0 $d_model_stage0 \
+  --depth_enc0 $depth_enc0 \
+  --depth_enc1 $depth_enc1 \
+  --target_ratio0 $target_ratio0 \
+  --target_ratio1 $target_ratio1
+
+# Heartbeat dataset
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/Heartbeat/ \
+  --model_id Heartbeat \
+  --model $model_name \
+  --data UEA \
+  --e_layers 3 \
+  --batch_size 8 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 8 \
+  --dropout 0.1 \
+  --activation gelu \
+  --des 'DC_PatchTST_Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 100 \
+  --patience 10 \
+  --d_model_stage0 $d_model_stage0 \
+  --depth_enc0 $depth_enc0 \
+  --depth_enc1 $depth_enc1 \
+  --target_ratio0 $target_ratio0 \
+  --target_ratio1 $target_ratio1
+
+# JapaneseVowels dataset
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/JapaneseVowels/ \
+  --model_id JapaneseVowels \
+  --model $model_name \
+  --data UEA \
+  --e_layers 3 \
+  --batch_size 8 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 8 \
+  --dropout 0.1 \
+  --activation gelu \
+  --des 'DC_PatchTST_Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 100 \
+  --patience 10 \
+  --d_model_stage0 $d_model_stage0 \
+  --depth_enc0 $depth_enc0 \
+  --depth_enc1 $depth_enc1 \
+  --target_ratio0 $target_ratio0 \
+  --target_ratio1 $target_ratio1

scripts/classification/vanillaMamba_classification.sh

@@ -0,0 +1,259 @@
+#!/bin/bash
+
+# vanillaMamba Classification Training Script for Multiple Datasets
+export CUDA_VISIBLE_DEVICES=0
+
+model_name=vanillaMamba
+
+# Create results directory if it doesn't exist
+mkdir -p ./results
+
+# UWaveGestureLibrary dataset (seq_len=315, enc_in=3) - use Copy1 config
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/UWaveGestureLibrary/ \
+  --model_id UWaveGestureLibrary \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 64 \
+  --seq_len 315 \
+  --enc_in 3 \
+  --d_model 128 \
+  --d_state 64 \
+  --d_conv 4 \
+  --expand 2 \
+  --headdim 128 \
+  --dropout 0.1 \
+  --des 'vanillaMamba_UWaveGestureLibrary' \
+  --itr 1 \
+  --learning_rate 0.002 \
+  --train_epochs 150 \
+  --patience 30 \
+  --revin 0 | tee ./results/vanillaMamba_UWaveGestureLibrary.log
+
+# EthanolConcentration dataset (seq_len=1751, enc_in=3) - use Copy1 config
+python -u run.py \
+  --task_name classification \
+  --is_training 3 \
+  --root_path ./dataset/EthanolConcentration/ \
+  --model_id EthanolConcentration \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 64 \
+  --seq_len 1751 \
+  --enc_in 4 \
+  --d_model 128 \
+  --d_state 64 \
+  --d_conv 4 \
+  --expand 2 \
+  --headdim 64 \
+  --dropout 0.1 \
+  --des 'vanillaMamba_EthanolConcentration' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 200 \
+  --patience 30 \
+  --revin 0 | tee ./results/vanillaMamba_EthanolConcentration.log
+
+# Handwriting dataset (seq_len=152, enc_in=3) - use Copy1 config
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/Handwriting/ \
+  --model_id Handwriting \
+  --model $model_name \
+  --data UEA \
+  --e_layers 4 \
+  --batch_size 64 \
+  --seq_len 152 \
+  --enc_in 3 \
+  --d_model 128 \
+  --d_state 64 \
+  --d_conv 4 \
+  --expand 2 \
+  --headdim 64 \
+  --dropout 0.1 \
+  --des 'vanillaMamba_Handwriting' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 200 \
+  --patience 30 \
+  --revin 0 | tee ./results/vanillaMamba_Handwriting.log
+
+# JapaneseVowels dataset (seq_len=29, enc_in=12) - use Copy1 config
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/JapaneseVowels/ \
+  --model_id JapaneseVowels \
+  --model $model_name \
+  --data UEA \
+  --e_layers 3 \
+  --batch_size 64 \
+  --seq_len 29 \
+  --enc_in 12 \
+  --d_model 128 \
+  --d_state 64 \
+  --d_conv 4 \
+  --expand 2 \
+  --headdim 64 \
+  --dropout 0.1 \
+  --des 'vanillaMamba_JapaneseVowels' \
+  --itr 1 \
+  --learning_rate 0.0005 \
+  --train_epochs 100 \
+  --patience 30 \
+  --revin 0 | tee ./results/vanillaMamba_JapaneseVowels.log
+
+# PEMS-SF dataset (seq_len=144, enc_in=963) - use Copy1 config
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/PEMS-SF/ \
+  --model_id PEMS-SF \
+  --model $model_name \
+  --data UEA \
+  --e_layers 3 \
+  --batch_size 16 \
+  --seq_len 144 \
+  --enc_in 963 \
+  --d_model 128 \
+  --d_state 64 \
+  --d_conv 4 \
+  --expand 2 \
+  --headdim 64 \
+  --dropout 0.1 \
+  --des 'vanillaMamba_PEMS-SF' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 150 \
+  --patience 30 \
+  --revin 0 | tee ./results/vanillaMamba_PEMS-SF.log
+
+# Heartbeat dataset (seq_len=405, enc_in=61) - use original config
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/Heartbeat/ \
+  --model_id Heartbeat \
+  --model $model_name \
+  --data UEA \
+  --e_layers 3 \
+  --batch_size 64 \
+  --seq_len 405 \
+  --enc_in 61 \
+  --d_model 128 \
+  --d_state 64 \
+  --d_conv 4 \
+  --expand 2 \
+  --headdim 64 \
+  --dropout 0.1 \
+  --des 'vanillaMamba_Heartbeat' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 150 \
+  --patience 10 \
+  --revin 0 | tee ./results/vanillaMamba_Heartbeat.log
+
+# FaceDetection dataset (seq_len=62, enc_in=144) - use original config
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/FaceDetection/ \
+  --model_id FaceDetection \
+  --model $model_name \
+  --data UEA \
+  --e_layers 3 \
+  --batch_size 64 \
+  --seq_len 62 \
+  --enc_in 144 \
+  --d_model 128 \
+  --d_state 64 \
+  --d_conv 4 \
+  --expand 2 \
+  --headdim 64 \
+  --dropout 0.1 \
+  --des 'vanillaMamba_FaceDetection' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 100 \
+  --patience 10 \
+  --revin 0 | tee ./results/vanillaMamba_FaceDetection.log
+
+# SelfRegulationSCP1 dataset (seq_len=896, enc_in=6) - use original config
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/SelfRegulationSCP1/ \
+  --model_id SelfRegulationSCP1 \
+  --model $model_name \
+  --data UEA \
+  --e_layers 3 \
+  --batch_size 64 \
+  --seq_len 896 \
+  --enc_in 6 \
+  --d_model 128 \
+  --d_state 64 \
+  --d_conv 4 \
+  --expand 2 \
+  --headdim 64 \
+  --dropout 0.1 \
+  --des 'vanillaMamba_SelfRegulationSCP1' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 100 \
+  --patience 10 \
+  --revin 0 | tee ./results/vanillaMamba_SelfRegulationSCP1.log
+
+# SelfRegulationSCP2 dataset (seq_len=1152, enc_in=7) - use original config
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/SelfRegulationSCP2/ \
+  --model_id SelfRegulationSCP2 \
+  --model $model_name \
+  --data UEA \
+  --e_layers 3 \
+  --batch_size 64 \
+  --seq_len 1152 \
+  --enc_in 7 \
+  --d_model 128 \
+  --d_state 64 \
+  --d_conv 4 \
+  --expand 2 \
+  --headdim 64 \
+  --dropout 0.1 \
+  --des 'vanillaMamba_SelfRegulationSCP2' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 100 \
+  --patience 10 \
+  --revin 0 | tee ./results/vanillaMamba_SelfRegulationSCP2.log
+
+# SpokenArabicDigits dataset (seq_len=93, enc_in=13) - use original config
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/SpokenArabicDigits/ \
+  --model_id SpokenArabicDigits \
+  --model $model_name \
+  --data UEA \
+  --e_layers 3 \
+  --batch_size 64 \
+  --seq_len 93 \
+  --enc_in 13 \
+  --d_model 128 \
+  --d_state 64 \
+  --d_conv 4 \
+  --expand 2 \
+  --headdim 64 \
+  --dropout 0.1 \
+  --des 'vanillaMamba_SpokenArabicDigits' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 100 \
+  --patience 10 \
+  --revin 0 | tee ./results/vanillaMamba_SpokenArabicDigits.log

scripts/classification/xPatch_SparseChannel-Copy1.sh

@@ -0,0 +1,145 @@
+#!/bin/bash
+
+# xPatch_SparseChannel Classification Training Script for Multiple Datasets
+export CUDA_VISIBLE_DEVICES=0
+
+model_name=xPatch_SparseChannel
+
+# Create results directory if it doesn't exist
+mkdir -p ./results
+
+
+
+# UWaveGestureLibrary dataset (seq_len=315, enc_in=3, k_graph=3)
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/UWaveGestureLibrary/ \
+  --model_id UWaveGestureLibrary \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 64 \
+  --seq_len 315 \
+  --enc_in 3 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --dropout 0.1 \
+  --des 'xPatch_SparseChannel_UWaveGestureLibrary' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 100 \
+  --patience 30 \
+  --revin 0 \
+  --k_graph 3 | tee ./results/xPatch_SparseChannel_UWaveGestureLibrary.log
+
+
+
+# EthanolConcentration dataset (seq_len=1751, enc_in=3, k_graph=3)
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/EthanolConcentration/ \
+  --model_id EthanolConcentration \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 64 \
+  --seq_len 1751 \
+  --enc_in 3 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --dropout 0.1 \
+  --des 'xPatch_SparseChannel_EthanolConcentration' \
+  --itr 1 \
+  --learning_rate 0.0005 \
+  --train_epochs 100 \
+  --patience 30 \
+  --revin 0 \
+  --k_graph 3 | tee ./results/xPatch_SparseChannel_EthanolConcentration.log
+
+# Handwriting dataset (seq_len=152, enc_in=3, k_graph=3)
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/Handwriting/ \
+  --model_id Handwriting \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 64 \
+  --seq_len 152 \
+  --enc_in 3 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --dropout 0.1 \
+  --des 'xPatch_SparseChannel_Handwriting' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 100 \
+  --patience 30 \
+  --revin 0 \
+  --k_graph 3 | tee ./results/xPatch_SparseChannel_Handwriting.log
+
+# JapaneseVowels dataset (seq_len=29, enc_in=12, k_graph=8)
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/JapaneseVowels/ \
+  --model_id JapaneseVowels \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 64 \
+  --seq_len 29 \
+  --enc_in 12 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --dropout 0.1 \
+  --des 'xPatch_SparseChannel_JapaneseVowels' \
+  --itr 1 \
+  --learning_rate 0.0005 \
+  --train_epochs 100 \
+  --patience 30 \
+  --revin 0 \
+  --k_graph 8 | tee ./results/xPatch_SparseChannel_JapaneseVowels.log
+
+
+
+# PEMS-SF dataset (seq_len=144, enc_in=963, k_graph=8)
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/PEMS-SF/ \
+  --model_id PEMS-SF \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 16 \
+  --seq_len 144 \
+  --enc_in 963 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --dropout 0.1 \
+  --des 'xPatch_SparseChannel_PEMS-SF' \
+  --itr 1 \
+  --learning_rate 0.0005 \
+  --train_epochs 100 \
+  --patience 30 \
+  --revin 0 \
+  --k_graph 8 | tee ./results/xPatch_SparseChannel_PEMS-SF.log

scripts/classification/xPatch_SparseChannel.sh

@@ -1,10 +1,66 @@
 #!/bin/bash
 
-# xPatch_SparseChannel Classification Training Script for FaceDetection Dataset
+# xPatch_SparseChannel Classification Training Script for Multiple Datasets
 export CUDA_VISIBLE_DEVICES=0
 
 model_name=xPatch_SparseChannel
 
+# Create results directory if it doesn't exist
+mkdir -p ./results
+
+# Heartbeat dataset (seq_len=405, enc_in=61, k_graph=8)
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/Heartbeat/ \
+  --model_id Heartbeat \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 64 \
+  --seq_len 405 \
+  --enc_in 61 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --dropout 0.1 \
+  --des 'xPatch_SparseChannel_Heartbeat' \
+  --itr 1 \
+  --learning_rate 0.0005 \
+  --train_epochs 100 \
+  --patience 5 \
+  --revin 0 \
+  --k_graph 8 | tee ./results/xPatch_SparseChannel_Heartbeat.log
+
+# UWaveGestureLibrary dataset (seq_len=315, enc_in=3, k_graph=3)
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/UWaveGestureLibrary/ \
+  --model_id UWaveGestureLibrary \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 64 \
+  --seq_len 315 \
+  --enc_in 3 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --dropout 0.1 \
+  --des 'xPatch_SparseChannel_UWaveGestureLibrary' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 100 \
+  --patience 30 \
+  --revin 0 \
+  --k_graph 3 | tee ./results/xPatch_SparseChannel_UWaveGestureLibrary.log
+
+# FaceDetection dataset (seq_len=62, enc_in=144, k_graph=8)
 python -u run.py \
   --task_name classification \
   --is_training 1 \
@@ -12,21 +68,202 @@ python -u run.py \
   --model_id FaceDetection \
   --model $model_name \
   --data UEA \
-  --e_layers 3 \
+  --e_layers 2 \
   --batch_size 64 \
   --seq_len 62 \
   --enc_in 144 \
   --d_model 128 \
   --d_ff 256 \
-  --n_heads 8 \
+  --n_heads 16 \
   --patch_len 16 \
   --stride 8 \
-  --moving_avg 25 \
   --dropout 0.1 \
   --des 'xPatch_SparseChannel_FaceDetection' \
   --itr 1 \
   --learning_rate 0.0005 \
   --train_epochs 100 \
   --patience 5 \
-  --revin 1 \
+  --revin 0 \
-  --k_graph 8
+  --k_graph 8 | tee ./results/xPatch_SparseChannel_FaceDetection.log
+
+# EthanolConcentration dataset (seq_len=1751, enc_in=3, k_graph=3)
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/EthanolConcentration/ \
+  --model_id EthanolConcentration \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 64 \
+  --seq_len 1751 \
+  --enc_in 3 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --dropout 0.1 \
+  --des 'xPatch_SparseChannel_EthanolConcentration' \
+  --itr 1 \
+  --learning_rate 0.0005 \
+  --train_epochs 100 \
+  --patience 30 \
+  --revin 0 \
+  --k_graph 3 | tee ./results/xPatch_SparseChannel_EthanolConcentration.log
+
+# Handwriting dataset (seq_len=152, enc_in=3, k_graph=3)
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/Handwriting/ \
+  --model_id Handwriting \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 64 \
+  --seq_len 152 \
+  --enc_in 3 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --dropout 0.1 \
+  --des 'xPatch_SparseChannel_Handwriting' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --train_epochs 100 \
+  --patience 30 \
+  --revin 0 \
+  --k_graph 3 | tee ./results/xPatch_SparseChannel_Handwriting.log
+
+# JapaneseVowels dataset (seq_len=29, enc_in=12, k_graph=8)
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/JapaneseVowels/ \
+  --model_id JapaneseVowels \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 64 \
+  --seq_len 29 \
+  --enc_in 12 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --dropout 0.1 \
+  --des 'xPatch_SparseChannel_JapaneseVowels' \
+  --itr 1 \
+  --learning_rate 0.0005 \
+  --train_epochs 100 \
+  --patience 30 \
+  --revin 0 \
+  --k_graph 8 | tee ./results/xPatch_SparseChannel_JapaneseVowels.log
+
+# SelfRegulationSCP1 dataset (seq_len=896, enc_in=6, k_graph=6)
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/SelfRegulationSCP1/ \
+  --model_id SelfRegulationSCP1 \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 64 \
+  --seq_len 896 \
+  --enc_in 6 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --dropout 0.1 \
+  --des 'xPatch_SparseChannel_SelfRegulationSCP1' \
+  --itr 1 \
+  --learning_rate 0.0005 \
+  --train_epochs 100 \
+  --patience 5 \
+  --revin 0 \
+  --k_graph 6 | tee ./results/xPatch_SparseChannel_SelfRegulationSCP1.log
+
+# SelfRegulationSCP2 dataset (seq_len=1152, enc_in=7, k_graph=7)
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/SelfRegulationSCP2/ \
+  --model_id SelfRegulationSCP2 \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 64 \
+  --seq_len 1152 \
+  --enc_in 7 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --dropout 0.1 \
+  --des 'xPatch_SparseChannel_SelfRegulationSCP2' \
+  --itr 1 \
+  --learning_rate 0.0005 \
+  --train_epochs 100 \
+  --patience 5 \
+  --revin 0 \
+  --k_graph 7 | tee ./results/xPatch_SparseChannel_SelfRegulationSCP2.log
+
+# SpokenArabicDigits dataset (seq_len=93, enc_in=13, k_graph=8)
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/SpokenArabicDigits/ \
+  --model_id SpokenArabicDigits \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 64 \
+  --seq_len 93 \
+  --enc_in 13 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --dropout 0.1 \
+  --des 'xPatch_SparseChannel_SpokenArabicDigits' \
+  --itr 1 \
+  --learning_rate 0.0005 \
+  --train_epochs 100 \
+  --patience 5 \
+  --revin 0 \
+  --k_graph 8 | tee ./results/xPatch_SparseChannel_SpokenArabicDigits.log
+
+# PEMS-SF dataset (seq_len=144, enc_in=963, k_graph=8)
+python -u run.py \
+  --task_name classification \
+  --is_training 1 \
+  --root_path ./dataset/PEMS-SF/ \
+  --model_id PEMS-SF \
+  --model $model_name \
+  --data UEA \
+  --e_layers 2 \
+  --batch_size 16 \
+  --seq_len 144 \
+  --enc_in 963 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --dropout 0.1 \
+  --des 'xPatch_SparseChannel_PEMS-SF' \
+  --itr 1 \
+  --learning_rate 0.0005 \
+  --train_epochs 100 \
+  --patience 30 \
+  --revin 0 \
+  --k_graph 8 | tee ./results/xPatch_SparseChannel_PEMS-SF.log

scripts/long_term_forecast/vanillaMamba_all.sh

@@ -0,0 +1,251 @@
+#!/bin/bash
+
+model_name=vanillaMamba
+
+# ETTm1 dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/ETT-small/ \
+  --data_path ETTm1.csv \
+  --model_id ETTm1_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data ETTm1 \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 7 \
+  --c_out 7 \
+  --d_model 128 \
+  --expand 2 \
+  --d_conv 4 \
+  --d_state 64 \
+  --headdim 64 \
+  --ngroups 1 \
+  --chunk_size 256 \
+  --dropout 0.1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# ETTm2 dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/ETT-small/ \
+  --data_path ETTm2.csv \
+  --model_id ETTm2_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data ETTm2 \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 7 \
+  --c_out 7 \
+  --d_model 128 \
+  --expand 2 \
+  --d_conv 4 \
+  --d_state 64 \
+  --headdim 64 \
+  --ngroups 1 \
+  --chunk_size 256 \
+  --dropout 0.1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# ETTh1 dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/ETT-small/ \
+  --data_path ETTh1.csv \
+  --model_id ETTh1_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data ETTh1 \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 7 \
+  --c_out 7 \
+  --d_model 128 \
+  --expand 2 \
+  --d_conv 4 \
+  --d_state 64 \
+  --headdim 64 \
+  --ngroups 1 \
+  --chunk_size 256 \
+  --dropout 0.1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# ETTh2 dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/ETT-small/ \
+  --data_path ETTh2.csv \
+  --model_id ETTh2_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data ETTh2 \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 7 \
+  --c_out 7 \
+  --d_model 128 \
+  --expand 2 \
+  --d_conv 4 \
+  --d_state 64 \
+  --headdim 64 \
+  --ngroups 1 \
+  --chunk_size 256 \
+  --dropout 0.1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# Weather dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/weather/ \
+  --data_path weather.csv \
+  --model_id weather_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data custom \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 21 \
+  --c_out 21 \
+  --d_model 128 \
+  --expand 2 \
+  --d_conv 4 \
+  --d_state 64 \
+  --headdim 64 \
+  --ngroups 1 \
+  --chunk_size 256 \
+  --dropout 0.1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# ECL dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/electricity/ \
+  --data_path electricity.csv \
+  --model_id ECL_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data custom \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 321 \
+  --c_out 321 \
+  --d_model 128 \
+  --expand 2 \
+  --d_conv 4 \
+  --d_state 64 \
+  --headdim 64 \
+  --ngroups 1 \
+  --chunk_size 256 \
+  --dropout 0.1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# Traffic dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/traffic/ \
+  --data_path traffic.csv \
+  --model_id traffic_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data custom \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 862 \
+  --c_out 862 \
+  --d_model 128 \
+  --expand 2 \
+  --d_conv 4 \
+  --d_state 64 \
+  --headdim 64 \
+  --ngroups 1 \
+  --chunk_size 256 \
+  --dropout 0.1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# Exchange dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/exchange_rate/ \
+  --data_path exchange_rate.csv \
+  --model_id Exchange_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data custom \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 8 \
+  --c_out 8 \
+  --d_model 128 \
+  --expand 2 \
+  --d_conv 4 \
+  --d_state 64 \
+  --headdim 64 \
+  --ngroups 1 \
+  --chunk_size 256 \
+  --dropout 0.1 \
+  --des 'Exp' \
+  --itr 1
+done

scripts/long_term_forecast/xPatch_SparseChannel_PEMS.sh

@@ -0,0 +1,150 @@
+#!/bin/bash
+
+#export CUDA_VISIBLE_DEVICES=0
+
+model_name=xPatch_SparseChannel
+
+seq_len=96
+pred_len=12
+learning_rate=0.003
+d_model=128
+d_ff=256
+batch_size=128
+train_epochs=10
+patience=10
+
+# PEMS03 dataset
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/PEMS/ \
+  --data_path PEMS03.npz \
+  --model_id PEMS03 \
+  --model $model_name \
+  --data PEMS \
+  --features M \
+  --seq_len $seq_len \
+  --label_len 0 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 358 \
+  --dec_in 358 \
+  --c_out 358 \
+  --lradj 'sigmoid' \
+  --d_model $d_model \
+  --d_ff $d_ff \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 8 \
+  --dropout 0.1 \
+  --revin 1 \
+  --batch_size $batch_size \
+  --learning_rate $learning_rate \
+  --train_epochs $train_epochs \
+  --patience $patience \
+  --des 'Exp' \
+  --itr 1
+
+# PEMS04 dataset
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/PEMS/ \
+  --data_path PEMS04.npz \
+  --model_id PEMS04 \
+  --model $model_name \
+  --data PEMS \
+  --features M \
+  --seq_len $seq_len \
+  --label_len 0 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 307 \
+  --dec_in 307 \
+  --c_out 307 \
+  --lradj 'sigmoid' \
+  --d_model $d_model \
+  --d_ff $d_ff \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 8 \
+  --dropout 0.1 \
+  --revin 1 \
+  --batch_size $batch_size \
+  --learning_rate $learning_rate \
+  --train_epochs $train_epochs \
+  --patience $patience \
+  --des 'Exp' \
+  --itr 1
+
+# PEMS07 dataset
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/PEMS/ \
+  --data_path PEMS07.npz \
+  --model_id PEMS07 \
+  --model $model_name \
+  --data PEMS \
+  --features M \
+  --seq_len $seq_len \
+  --label_len 0 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 883 \
+  --dec_in 883 \
+  --c_out 883 \
+  --lradj 'sigmoid' \
+  --d_model $d_model \
+  --d_ff $d_ff \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 8 \
+  --dropout 0.1 \
+  --revin 1 \
+  --batch_size $batch_size \
+  --learning_rate $learning_rate \
+  --train_epochs $train_epochs \
+  --patience $patience \
+  --des 'Exp' \
+  --itr 1
+
+# PEMS08 dataset
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/PEMS/ \
+  --data_path PEMS08.npz \
+  --model_id PEMS08 \
+  --model $model_name \
+  --data PEMS \
+  --features M \
+  --seq_len $seq_len \
+  --label_len 0 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 170 \
+  --dec_in 170 \
+  --c_out 170 \
+  --lradj 'sigmoid' \
+  --d_model $d_model \
+  --d_ff $d_ff \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 8 \
+  --dropout 0.1 \
+  --revin 1 \
+  --batch_size $batch_size \
+  --learning_rate $learning_rate \
+  --train_epochs $train_epochs \
+  --patience $patience \
+  --des 'Exp' \
+  --itr 1

scripts/long_term_forecast/xPatch_SparseChannel_all-Copy1.sh

@@ -0,0 +1,251 @@
+#!/bin/bash
+
+model_name=xPatch_SparseChannel
+
+# ETTm1 dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/ETT-small/ \
+  --data_path ETTm1.csv \
+  --model_id ETTm1_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data ETTm1 \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 7 \
+  --c_out 7 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 7 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# ETTm2 dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/ETT-small/ \
+  --data_path ETTm2.csv \
+  --model_id ETTm2_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data ETTm2 \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 7 \
+  --c_out 7 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 7 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# ETTh1 dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/ETT-small/ \
+  --data_path ETTh1.csv \
+  --model_id ETTh1_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data ETTh1 \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 7 \
+  --c_out 7 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 7 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# ETTh2 dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/ETT-small/ \
+  --data_path ETTh2.csv \
+  --model_id ETTh2_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data ETTh2 \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 7 \
+  --c_out 7 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 7 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# Weather dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/weather/ \
+  --data_path weather.csv \
+  --model_id weather_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data custom \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 21 \
+  --c_out 21 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 8 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# ECL dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/electricity/ \
+  --data_path electricity.csv \
+  --model_id ECL_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data custom \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 321 \
+  --c_out 321 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 8 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# Traffic dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/traffic/ \
+  --data_path traffic.csv \
+  --model_id traffic_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data custom \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 862 \
+  --c_out 862 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 8 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# Exchange dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/exchange_rate/ \
+  --data_path exchange_rate.csv \
+  --model_id Exchange_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data custom \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 8 \
+  --c_out 8 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 8 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1
+done

scripts/long_term_forecast/xPatch_SparseChannel_all.sh

@@ -0,0 +1,77 @@
+#!/bin/bash
+
+model_name=xPatch_SparseChannel
+
+# ECL dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/electricity/ \
+  --data_path electricity.csv \
+  --model_id ECL_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data custom \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 321 \
+  --batch_size 512 \
+  --learning_rate 0.001 \
+  --use_multi_gpu True \
+  --lradj 'sigmoid' \
+  --train_epochs 50 \
+  --patience 5 \
+  --c_out 321 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 8 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1
+done
+
+# Traffic dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/traffic/ \
+  --data_path traffic.csv \
+  --model_id traffic_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data custom \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 862 \
+  --batch_size 256 \
+  --learning_rate 0.0005 \
+  --use_multi_gpu True \
+  --lradj 'sigmoid' \
+  --train_epochs 50 \
+  --patience 5 \
+  --c_out 862 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 8 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1
+done

scripts/short_term_forecast/vanillaMamba_M4.sh

@@ -0,0 +1,165 @@
+#!/bin/bash
+
+model_name=vanillaMamba
+
+# M4 Monthly
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Monthly' \
+  --model_id m4_Monthly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers 2 \
+  --enc_in 1 \
+  --c_out 1 \
+  --batch_size 16 \
+  --d_model 128 \
+  --expand 2 \
+  --d_conv 4 \
+  --d_state 64 \
+  --headdim 64 \
+  --ngroups 1 \
+  --chunk_size 256 \
+  --dropout 0.1 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --loss 'SMAPE'
+
+# M4 Yearly
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Yearly' \
+  --model_id m4_Yearly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers 2 \
+  --enc_in 1 \
+  --c_out 1 \
+  --batch_size 16 \
+  --d_model 128 \
+  --expand 2 \
+  --d_conv 4 \
+  --d_state 64 \
+  --headdim 64 \
+  --ngroups 1 \
+  --chunk_size 256 \
+  --dropout 0.1 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --loss 'SMAPE'
+
+# M4 Quarterly
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Quarterly' \
+  --model_id m4_Quarterly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers 2 \
+  --enc_in 1 \
+  --c_out 1 \
+  --batch_size 16 \
+  --d_model 128 \
+  --expand 2 \
+  --d_conv 4 \
+  --d_state 64 \
+  --headdim 64 \
+  --ngroups 1 \
+  --chunk_size 256 \
+  --dropout 0.1 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --loss 'SMAPE'
+
+# M4 Weekly
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Weekly' \
+  --model_id m4_Weekly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers 2 \
+  --enc_in 1 \
+  --c_out 1 \
+  --batch_size 16 \
+  --d_model 128 \
+  --expand 2 \
+  --d_conv 4 \
+  --d_state 64 \
+  --headdim 64 \
+  --ngroups 1 \
+  --chunk_size 256 \
+  --dropout 0.1 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --loss 'SMAPE'
+
+# M4 Daily
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Daily' \
+  --model_id m4_Daily \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers 2 \
+  --enc_in 1 \
+  --c_out 1 \
+  --batch_size 16 \
+  --d_model 128 \
+  --expand 2 \
+  --d_conv 4 \
+  --d_state 64 \
+  --headdim 64 \
+  --ngroups 1 \
+  --chunk_size 256 \
+  --dropout 0.1 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --loss 'SMAPE'
+
+# M4 Hourly
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Hourly' \
+  --model_id m4_Hourly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers 2 \
+  --enc_in 1 \
+  --c_out 1 \
+  --batch_size 16 \
+  --d_model 128 \
+  --expand 2 \
+  --d_conv 4 \
+  --d_state 64 \
+  --headdim 64 \
+  --ngroups 1 \
+  --chunk_size 256 \
+  --dropout 0.1 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --loss 'SMAPE'

scripts/short_term_forecast/xPatch_SparseChannel_M4.sh

@@ -0,0 +1,165 @@
+#!/bin/bash
+
+model_name=xPatch_SparseChannel
+
+# M4 Monthly
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Monthly' \
+  --model_id m4_Monthly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers 2 \
+  --enc_in 1 \
+  --c_out 1 \
+  --batch_size 16 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 1 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --loss 'SMAPE'
+
+# M4 Yearly
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Yearly' \
+  --model_id m4_Yearly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers 2 \
+  --enc_in 1 \
+  --c_out 1 \
+  --batch_size 16 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 1 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --loss 'SMAPE'
+
+# M4 Quarterly
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Quarterly' \
+  --model_id m4_Quarterly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers 2 \
+  --enc_in 1 \
+  --c_out 1 \
+  --batch_size 16 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 1 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --loss 'SMAPE'
+
+# M4 Weekly
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Weekly' \
+  --model_id m4_Weekly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers 2 \
+  --enc_in 1 \
+  --c_out 1 \
+  --batch_size 16 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 1 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --loss 'SMAPE'
+
+# M4 Daily
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Daily' \
+  --model_id m4_Daily \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers 2 \
+  --enc_in 1 \
+  --c_out 1 \
+  --batch_size 16 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 1 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --loss 'SMAPE'
+
+# M4 Hourly
+python -u run.py \
+  --task_name short_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/m4 \
+  --seasonal_patterns 'Hourly' \
+  --model_id m4_Hourly \
+  --model $model_name \
+  --data m4 \
+  --features M \
+  --e_layers 2 \
+  --enc_in 1 \
+  --c_out 1 \
+  --batch_size 16 \
+  --d_model 128 \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 1 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1 \
+  --learning_rate 0.001 \
+  --loss 'SMAPE'

test_DC_hnet.py

@@ -0,0 +1,209 @@
+#!/usr/bin/env python3
+"""
+测试DC_hnet模型的脚本
+用于验证时间序列分类模型能否正常运行并得到期望的输出形状
+"""
+
+import torch
+import torch.nn.functional as F
+import sys
+import os
+
+# 添加当前目录到Python路径
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from models.DC_hnet import HierEncodersSingleMainConfig, HierEncodersSingleMainClassifier
+
+def test_dc_hnet_model():
+    """测试DC_hnet时间序列分类模型"""
+    print("=" * 60)
+    print("测试DC_hnet时间序列分类模型")
+    print("=" * 60)
+    
+    # 设置设备
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"使用设备: {device}")
+    
+    # 模型参数配置
+    B, L, N = 8, 512, 6        # batch_size, seq_length, num_channels
+    num_classes = 10            # 分类数
+    d_models = [64, 128, 256]   # 各层维度，单调递增
+    
+    print(f"输入形状: (B={B}, L={L}, N={N})")
+    print(f"分类数: {num_classes}")
+    print(f"模型维度: {d_models}")
+    
+    # 编码器配置（每层都是Mamba）
+    encoder_cfg_per_stage = [
+        dict(arch="m", height=2),  # stage 0: Mamba2, 2层
+        dict(arch="m", height=3),  # stage 1: Mamba2, 3层  
+    ]
+    
+    # 主网络配置（使用Transformer）
+    main_cfg = dict(
+        arch="T", height=4  # Transformer, 4层
+    )
+    
+    # 压缩目标
+    target_compression_N_per_stage = [2, 3]  # 每层压缩比例
+    
+    # 创建配置
+    cfg = HierEncodersSingleMainConfig(
+        num_channels=N,
+        d_models=d_models,
+        num_classes=num_classes,
+        encoder_cfg_per_stage=encoder_cfg_per_stage,
+        main_cfg=main_cfg,
+        target_compression_N_per_stage=target_compression_N_per_stage,
+        share_channel=True,                  # 通道间共享编码器
+        fusion_across_channels="mean",       # 通道融合方式
+        dropout=0.1,
+    )
+    
+    print("配置创建完成")
+    
+    try:
+        # 创建模型 - 设置正确的dtype以兼容flash attention
+        dtype = torch.bfloat16 if device.type == "cuda" else torch.float32
+        model = HierEncodersSingleMainClassifier(cfg, device=device, dtype=dtype)
+        model = model.to(device)
+        print(f"模型创建成功，参数量: {sum(p.numel() for p in model.parameters()):,}")
+        
+        # 创建随机输入数据 - 使用bfloat16以兼容flash attention
+        dtype = torch.bfloat16 if device.type == "cuda" else torch.float32
+        x = torch.randn(B, L, N, device=device, dtype=dtype)
+        mask = torch.ones(B, L, dtype=torch.bool, device=device)
+        
+        print(f"输入数据形状: {x.shape}, 数据类型: {x.dtype}")
+        
+        # 前向传播测试
+        print("\n开始前向传播测试...")
+        model.eval()
+        with torch.no_grad():
+            logits, seq_debug, aux = model(x, mask=mask, return_seq=False)
+        
+        print(f"✅ 前向传播成功!")
+        print(f"输出logits形状: {logits.shape}")  # 应该是 (B, num_classes)
+        print(f"ratio_loss: {aux['ratio_loss']:.4f}")
+        
+        # 验证输出形状
+        expected_shape = (B, num_classes)
+        if logits.shape == expected_shape:
+            print(f"✅ 输出形状正确: {logits.shape}")
+        else:
+            print(f"❌ 输出形状错误: 期望 {expected_shape}, 实际 {logits.shape}")
+            return False
+        
+        # 测试训练模式
+        print("\n开始训练模式测试...")
+        model.train()
+        optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
+        
+        # 创建目标标签
+        y = torch.randint(0, num_classes, (B,), device=device)
+        
+        # 前向传播
+        logits, _, aux = model(x, mask=mask, return_seq=False)
+        
+        # 计算损失
+        cls_loss = F.cross_entropy(logits, y)
+        ratio_reg = 0.01 * aux["ratio_loss"]  # ratio loss正则化
+        total_loss = cls_loss + ratio_reg
+        
+        print(f"分类损失: {cls_loss:.4f}")
+        print(f"ratio损失: {ratio_reg:.4f}")  
+        print(f"总损失: {total_loss:.4f}")
+        
+        # 反向传播
+        optimizer.zero_grad()
+        total_loss.backward()
+        optimizer.step()
+        
+        print("✅ 训练步骤成功!")
+        
+        # 测试序列返回功能
+        print("\n测试序列调试信息返回...")
+        with torch.no_grad():
+            logits, seq_debug, aux = model(x, mask=mask, return_seq=True)
+        
+        if seq_debug is not None:
+            print(f"✅ 序列调试信息获取成功，包含 {len(seq_debug)} 个通道的信息")
+        else:
+            print("❌ 序列调试信息获取失败")
+        
+        print("\n" + "=" * 60)
+        print("🎉 DC_hnet模型测试全部通过!")
+        print("模型可以正常进行时间序列分类任务")
+        print("=" * 60)
+        
+        return True
+        
+    except Exception as e:
+        print(f"❌ 测试失败: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_different_configurations():
+    """测试不同的模型配置"""
+    print("\n测试不同配置...")
+    
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    
+    # 测试配置1: 不共享通道
+    cfg1 = HierEncodersSingleMainConfig(
+        num_channels=3,
+        d_models=[32, 64],
+        num_classes=5,
+        encoder_cfg_per_stage=[dict(arch="m", height=2)],
+        main_cfg=dict(arch="m", height=3),
+        target_compression_N_per_stage=[2],
+        share_channel=False,  # 不共享通道
+        fusion_across_channels="concat",  # 连接融合
+        dropout=0.1,
+    )
+    
+    try:
+        dtype1 = torch.bfloat16 if device.type == "cuda" else torch.float32
+        model1 = HierEncodersSingleMainClassifier(cfg1, device=device, dtype=dtype1)
+        x1 = torch.randn(4, 256, 3, device=device, dtype=dtype1)
+        logits1, _, _ = model1(x1)
+        print(f"✅ 配置1 (不共享通道, concat融合): 输出形状 {logits1.shape}")
+    except Exception as e:
+        print(f"❌ 配置1测试失败: {str(e)}")
+        
+    # 测试配置2: 单层模型
+    cfg2 = HierEncodersSingleMainConfig(
+        num_channels=2, 
+        d_models=[128],  # 只有一层，没有编码器阶段
+        num_classes=3,
+        encoder_cfg_per_stage=[],  # 空的编码器阶段
+        main_cfg=dict(arch="T", height=2),
+        target_compression_N_per_stage=[],
+        share_channel=True,
+        fusion_across_channels="mean",
+        dropout=0.1,
+    )
+    
+    try:
+        dtype2 = torch.bfloat16 if device.type == "cuda" else torch.float32
+        model2 = HierEncodersSingleMainClassifier(cfg2, device=device, dtype=dtype2)
+        x2 = torch.randn(2, 128, 2, device=device, dtype=dtype2)
+        logits2, _, _ = model2(x2)
+        print(f"✅ 配置2 (单层模型): 输出形状 {logits2.shape}")
+    except Exception as e:
+        print(f"❌ 配置2测试失败: {str(e)}")
+
+if __name__ == "__main__":
+    # 主测试
+    success = test_dc_hnet_model()
+    
+    # 额外配置测试
+    test_different_configurations()
+    
+    if success:
+        print("\n🎊 所有测试完成!")
+        sys.exit(0)
+    else:
+        print("\n💥 测试失败!")
+        sys.exit(1)

test_dc_patchtst.py

@@ -0,0 +1,313 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import torch
+import torch.nn as nn
+import pandas as pd
+import numpy as np
+from torch.utils.data import Dataset, DataLoader
+import os
+import matplotlib.pyplot as plt
+from models.DC_PatchTST import Model
+
+class Config:
+    """Configuration class"""
+    def __init__(self):
+        # Basic configuration
+        self.task_name = 'long_term_forecast'
+        self.model = 'DC_PatchTST'
+        
+        # Data configuration
+        self.seq_len = 96     # Input sequence length
+        self.pred_len = 24    # Prediction sequence length
+        self.label_len = 48   # Label length
+        self.enc_in = 2       # Input feature dimension (dual channel)
+        self.dec_in = 2       # Decoder input dimension
+        self.c_out = 2        # Output dimension
+        
+        # Model configuration
+        self.d_model = 128    # Model dimension
+        self.n_heads = 8      # Number of attention heads
+        self.e_layers = 2     # Number of encoder layers
+        self.d_layers = 1     # Number of decoder layers
+        self.d_ff = 256       # Feed forward dimension
+        self.factor = 1       # Attention factor
+        self.dropout = 0.1    # Dropout rate
+        self.activation = 'gelu'
+        
+        # Training configuration
+        self.batch_size = 32
+        self.learning_rate = 0.001
+        self.train_epochs = 50
+        self.patience = 5
+        
+        # Other configuration
+        self.use_amp = False
+        self.num_class = 0
+        
+        # GPU configuration
+        self.use_gpu = torch.cuda.is_available()
+        self.device = torch.device('cuda' if self.use_gpu else 'cpu')
+
+class SineWaveDataset(Dataset):
+    """Sine wave dataset"""
+    def __init__(self, data_path, seq_len=96, pred_len=24, mode='test'):
+        self.seq_len = seq_len
+        self.pred_len = pred_len
+        self.mode = mode
+        
+        # Load data
+        if mode == 'train':
+            df = pd.read_csv(os.path.join(data_path, 'train.csv'))
+        elif mode == 'val':
+            df = pd.read_csv(os.path.join(data_path, 'val.csv'))
+        else:  # test
+            df = pd.read_csv(os.path.join(data_path, 'test.csv'))
+        
+        # Extract feature columns (except timestamp)
+        self.data = df[['channel1', 'channel2']].values.astype(np.float32)
+        
+        # Calculate available sample count
+        self.total_len = len(self.data)
+        self.samples_num = max(0, self.total_len - seq_len - pred_len + 1)
+        
+        print(f"{mode} dataset: {self.total_len} records, {self.samples_num} samples")
+    
+    def __len__(self):
+        return self.samples_num
+    
+    def __getitem__(self, idx):
+        # Input sequence
+        s_begin = idx
+        s_end = s_begin + self.seq_len
+        
+        # Prediction target
+        r_begin = s_end
+        r_end = r_begin + self.pred_len
+        
+        seq_x = self.data[s_begin:s_end]      # (seq_len, n_vars)
+        seq_y = self.data[r_begin:r_end]      # (pred_len, n_vars)
+        
+        # Time marks (simple positional encoding)
+        seq_x_mark = np.arange(self.seq_len).reshape(-1, 1).astype(np.float32)
+        seq_y_mark = np.arange(self.pred_len).reshape(-1, 1).astype(np.float32)
+        
+        return seq_x, seq_y, seq_x_mark, seq_y_mark, idx
+
+def load_model(model_path):
+    """Load saved model"""
+    checkpoint = torch.load(model_path, map_location='cpu', weights_only=False)
+    config = checkpoint['config']
+    
+    # Create model
+    model = Model(config)
+    model.load_state_dict(checkpoint['model_state_dict'])
+    
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    model = model.to(device)
+    model.eval()
+    
+    print(f"Model loaded successfully - Epoch: {checkpoint['epoch']}, Val Loss: {checkpoint['val_loss']:.6f}")
+    print(f"Using device: {device}")
+    
+    return model, config, device
+
+def visualize_predictions_with_chunks(model, test_loader, device, save_path, num_samples=5):
+    """Predict and visualize results, marking chunk points"""
+    model.eval()
+    
+    # Create save directory
+    vis_dir = os.path.join(save_path, 'visualizations')
+    os.makedirs(vis_dir, exist_ok=True)
+    
+    sample_count = 0
+    
+    with torch.no_grad():
+        for batch_x, batch_y, batch_x_mark, batch_y_mark, batch_idx in test_loader:
+            if sample_count >= num_samples:
+                break
+                
+            batch_x = batch_x.to(device)
+            batch_y = batch_y.to(device) 
+            batch_x_mark = batch_x_mark.to(device)
+            batch_y_mark = batch_y_mark.to(device)
+            
+            # Construct decoder input
+            dec_inp = torch.zeros_like(batch_y).to(device)
+            
+            # Predict
+            outputs, aux = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+            
+            # Convert to CPU
+            batch_x_cpu = batch_x.cpu().numpy()
+            batch_y_cpu = batch_y.cpu().numpy()
+            outputs_cpu = outputs.cpu().numpy()
+            
+            # Process each sample in batch
+            for i in range(min(batch_x.size(0), num_samples - sample_count)):
+                input_seq = batch_x_cpu[i]      # (seq_len, 2)
+                true_pred = batch_y_cpu[i]      # (pred_len, 2)  
+                pred_seq = outputs_cpu[i]       # (pred_len, 2)
+                
+                # Get first layer chunk information
+                boundary_mask_stage0 = None
+                if aux is not None and 'stage0' in aux:
+                    # aux['stage0']['boundary_mask'] shape: (B*nvars, L)
+                    # We need to reshape to (B, nvars, L) then take i-th sample
+                    stage0_info = aux['stage0']
+                    boundary_mask = stage0_info['boundary_mask'].cpu().numpy()  # (B*nvars, L)
+                    
+                    B = batch_x.size(0)
+                    nvars = batch_x.size(2)  # should be 2
+                    L = boundary_mask.shape[1]
+                    
+                    # Reshape boundary_mask: (B*nvars, L) -> (B, nvars, L)
+                    boundary_mask = boundary_mask.reshape(B, nvars, L)
+                    boundary_mask_stage0 = boundary_mask[i]  # (nvars, L)
+                
+                # Create visualization for each channel
+                fig, axes = plt.subplots(2, 1, figsize=(15, 10))
+                
+                for ch in range(2):  # dual channel
+                    ax = axes[ch]
+                    
+                    # Time axis
+                    input_time = np.arange(len(input_seq))
+                    pred_time = np.arange(len(input_seq), len(input_seq) + len(true_pred))
+                    
+                    # Plot input sequence
+                    ax.plot(input_time, input_seq[:, ch], 'b-', label='Input Sequence', linewidth=1.5)
+                    
+                    # Plot ground truth prediction and model prediction
+                    ax.plot(pred_time, true_pred[:, ch], 'g-', label='Ground Truth', linewidth=2)
+                    ax.plot(pred_time, pred_seq[:, ch], 'r--', label='Prediction', linewidth=2)
+                    
+                    # Mark first layer chunk points
+                    if boundary_mask_stage0 is not None:
+                        chunk_points = np.where(boundary_mask_stage0[ch])[0]  # Get chunk points for this channel
+                        for point in chunk_points:
+                            if point < len(input_seq):  # Only mark points within input sequence range
+                                ax.axvline(x=point, color='orange', linestyle=':', alpha=0.7, linewidth=1)
+                        
+                        # Add chunk points explanation in legend
+                        if len(chunk_points) > 0:
+                            ax.axvline(x=-1, color='orange', linestyle=':', alpha=0.7, 
+                                     linewidth=1, label=f'Chunk Points (Stage 0)')
+                    
+                    ax.set_title(f'Sample {sample_count + 1} - Channel {ch + 1}')
+                    ax.set_xlabel('Time Steps')
+                    ax.set_ylabel('Value')
+                    ax.legend()
+                    ax.grid(True, alpha=0.3)
+                    
+                    # Add boundary line marking input and prediction boundary
+                    ax.axvline(x=len(input_seq)-0.5, color='black', linestyle='-', alpha=0.5, linewidth=1)
+                    ax.text(len(input_seq)-0.5, ax.get_ylim()[1]*0.9, 'Prediction Start', 
+                           rotation=90, verticalalignment='top', fontsize=8)
+                
+                plt.tight_layout()
+                
+                # Save figure
+                sample_filename = f'sample_{sample_count + 1}_with_chunks.png'
+                sample_path = os.path.join(vis_dir, sample_filename)
+                plt.savefig(sample_path, dpi=300, bbox_inches='tight')
+                plt.close()
+                
+                print(f"Sample {sample_count + 1} visualization saved to: {sample_path}")
+                
+                # Print chunk statistics
+                if boundary_mask_stage0 is not None:
+                    for ch in range(2):
+                        chunk_count = np.sum(boundary_mask_stage0[ch])
+                        chunk_ratio = chunk_count / len(boundary_mask_stage0[ch])
+                        print(f"  Channel {ch + 1}: {chunk_count} chunk points ({chunk_ratio:.2%} of sequence)")
+                
+                sample_count += 1
+                if sample_count >= num_samples:
+                    break
+            
+            if sample_count >= num_samples:
+                break
+
+def evaluate_model(model, test_loader, device):
+    """Evaluate model performance"""
+    model.eval()
+    total_mse = 0.0
+    total_mae = 0.0
+    batch_count = 0
+    
+    all_predictions = []
+    all_ground_truths = []
+    
+    with torch.no_grad():
+        for batch_x, batch_y, batch_x_mark, batch_y_mark, _ in test_loader:
+            batch_x = batch_x.to(device)
+            batch_y = batch_y.to(device)
+            batch_x_mark = batch_x_mark.to(device) 
+            batch_y_mark = batch_y_mark.to(device)
+            
+            dec_inp = torch.zeros_like(batch_y).to(device)
+            
+            outputs, _ = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+            
+            # Calculate loss
+            mse = torch.mean((outputs - batch_y) ** 2)
+            mae = torch.mean(torch.abs(outputs - batch_y))
+            
+            total_mse += mse.item()
+            total_mae += mae.item()
+            batch_count += 1
+            
+            # Collect for overall statistics
+            all_predictions.append(outputs.cpu().numpy())
+            all_ground_truths.append(batch_y.cpu().numpy())
+    
+    avg_mse = total_mse / batch_count
+    avg_mae = total_mae / batch_count
+    
+    # Calculate overall RMSE
+    all_predictions = np.concatenate(all_predictions, axis=0)
+    all_ground_truths = np.concatenate(all_ground_truths, axis=0)
+    rmse = np.sqrt(np.mean((all_predictions - all_ground_truths) ** 2))
+    
+    print(f"\nTest set evaluation results:")
+    print(f"MSE: {avg_mse:.6f}")
+    print(f"MAE: {avg_mae:.6f}")
+    print(f"RMSE: {rmse:.6f}")
+    
+    return avg_mse, avg_mae, rmse
+
+def main():
+    # Configuration parameters
+    model_path = './results/dc_patchtst_sine_wave/best_model.pth'
+    data_path = './data/sine_wave/'
+    save_path = './results/dc_patchtst_sine_wave/'
+    
+    if not os.path.exists(model_path):
+        print(f"Error: Model file not found {model_path}")
+        print("Please run the training script train_dc_patchtst.py first")
+        return
+    
+    # Load model
+    model, config, device = load_model(model_path)
+    
+    # Load test data
+    test_dataset = SineWaveDataset(data_path, config.seq_len, config.pred_len, 'test')
+    test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=0)  # batch_size=1 for easier visualization
+    
+    print(f"\nConfiguration info:")
+    print(f"Sequence length: {config.seq_len}")
+    print(f"Prediction length: {config.pred_len}")
+    print(f"Feature dimension: {config.enc_in}")
+    
+    # Evaluate model
+    mse, mae, rmse = evaluate_model(model, test_loader, device)
+    
+    # Visualize prediction results and mark chunk points
+    print(f"\nGenerating visualization results...")
+    visualize_predictions_with_chunks(model, test_loader, device, save_path, num_samples=5)
+    
+    print(f"\nAll results saved to: {save_path}")
+    print(f"Visualization files located at: {save_path}/visualizations/")
+
+if __name__ == "__main__":
+    main()

train_dc_patchtst.py

@@ -0,0 +1,335 @@
+#!/usr/bin/env python3
+import torch
+import torch.nn as nn
+import pandas as pd
+import numpy as np
+from torch.utils.data import Dataset, DataLoader
+import os
+import argparse
+from models.DC_PatchTST import Model
+import time
+import matplotlib.pyplot as plt
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+
+# 检查并创建结果文件夹
+def ensure_dir(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+class SineWaveDataset(Dataset):
+    """正弦波数据集"""
+    def __init__(self, data_path, seq_len=96, pred_len=24, mode='train'):
+        self.seq_len = seq_len
+        self.pred_len = pred_len
+        self.mode = mode
+        
+        # 加载数据
+        if mode == 'train':
+            df = pd.read_csv(os.path.join(data_path, 'train.csv'))
+        elif mode == 'val':
+            df = pd.read_csv(os.path.join(data_path, 'val.csv'))
+        else:  # test
+            df = pd.read_csv(os.path.join(data_path, 'test.csv'))
+        
+        # 提取特征列（除timestamp外）
+        self.data = df[['channel1', 'channel2']].values.astype(np.float32)
+        
+        # 计算可用样本数量
+        self.total_len = len(self.data)
+        self.samples_num = max(0, self.total_len - seq_len - pred_len + 1)
+        
+        print(f"{mode} 数据集: {self.total_len} 条记录, {self.samples_num} 个样本")
+    
+    def __len__(self):
+        return self.samples_num
+    
+    def __getitem__(self, idx):
+        # 输入序列
+        s_begin = idx
+        s_end = s_begin + self.seq_len
+        
+        # 预测目标
+        r_begin = s_end
+        r_end = r_begin + self.pred_len
+        
+        seq_x = self.data[s_begin:s_end]      # (seq_len, n_vars)
+        seq_y = self.data[r_begin:r_end]      # (pred_len, n_vars)
+        
+        # 时间标记（简单的位置编码）
+        seq_x_mark = np.arange(self.seq_len).reshape(-1, 1).astype(np.float32)
+        seq_y_mark = np.arange(self.pred_len).reshape(-1, 1).astype(np.float32)
+        
+        return seq_x, seq_y, seq_x_mark, seq_y_mark
+
+class Config:
+    """配置类"""
+    def __init__(self):
+        # 基础配置
+        self.task_name = 'long_term_forecast'
+        self.model = 'DC_PatchTST'
+        
+        # 数据配置
+        self.seq_len = 96     # 输入序列长度
+        self.pred_len = 24    # 预测序列长度
+        self.label_len = 48   # 标签长度
+        self.enc_in = 2       # 输入特征维度（双通道）
+        self.dec_in = 2       # 解码器输入维度
+        self.c_out = 2        # 输出维度
+        
+        # 模型配置
+        self.d_model = 128    # 模型维度
+        self.n_heads = 8      # 注意力头数
+        self.e_layers = 2     # 编码器层数
+        self.d_layers = 1     # 解码器层数
+        self.d_ff = 256       # 前向网络维度
+        self.factor = 1       # 注意力因子
+        self.dropout = 0    # Dropout率
+        self.activation = 'gelu'
+        
+        # 训练配置
+        self.batch_size = 1024
+        self.learning_rate = 0.001
+        self.train_epochs = 50
+        self.patience = 5
+        
+        # 其他配置
+        self.use_amp = False
+        self.num_class = 0
+        
+        # GPU配置
+        self.use_gpu = torch.cuda.is_available()
+        self.device = torch.device('cuda' if self.use_gpu else 'cpu')
+
+def train_epoch(model, train_loader, criterion, optimizer, device, use_amp=False):
+    """训练一个epoch"""
+    model.train()
+    total_loss = 0.0
+    batch_count = 0
+    
+    for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(train_loader):
+        batch_x = batch_x.to(device)
+        batch_y = batch_y.to(device)
+        batch_x_mark = batch_x_mark.to(device)
+        batch_y_mark = batch_y_mark.to(device)
+        
+        # 构造解码器输入
+        dec_inp = torch.zeros_like(batch_y).to(device)
+        
+        optimizer.zero_grad()
+        
+        if use_amp:
+            with torch.cuda.amp.autocast():
+                outputs, aux = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+                loss = criterion(outputs, batch_y)
+                
+                # 添加DC的ratio loss
+                if aux is not None and 'ratio_loss0' in aux and 'ratio_loss1' in aux:
+                    ratio_loss = aux['ratio_loss0'] + aux['ratio_loss1']
+                    loss = loss + 0.0 * ratio_loss  # ratio loss权重
+        else:
+            outputs, aux = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+            loss = criterion(outputs, batch_y)
+            
+            # 添加DC的ratio loss
+            if aux is not None and 'ratio_loss0' in aux and 'ratio_loss1' in aux:
+                ratio_loss = aux['ratio_loss0'] + aux['ratio_loss1']
+                loss = loss + 0.0 * ratio_loss  # ratio loss权重
+        
+        loss.backward()
+        optimizer.step()
+        
+        total_loss += loss.item()
+        batch_count += 1
+        
+        if i % 100 == 0:
+            print(f'Batch {i}, Loss: {loss.item():.6f}')
+    
+    return total_loss / batch_count
+
+def validate(model, val_loader, criterion, device):
+    """验证模型"""
+    model.eval()
+    total_loss = 0.0
+    batch_count = 0
+    
+    with torch.no_grad():
+        for batch_x, batch_y, batch_x_mark, batch_y_mark in val_loader:
+            batch_x = batch_x.to(device)
+            batch_y = batch_y.to(device)
+            batch_x_mark = batch_x_mark.to(device)
+            batch_y_mark = batch_y_mark.to(device)
+            
+            dec_inp = torch.zeros_like(batch_y).to(device)
+            
+            outputs, _ = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+            loss = criterion(outputs, batch_y)
+            
+            total_loss += loss.item()
+            batch_count += 1
+    
+    return total_loss / batch_count
+
+def test_model(model, test_loader, device, save_path):
+    """测试模型并可视化结果"""
+    model.eval()
+    predictions = []
+    ground_truths = []
+    
+    with torch.no_grad():
+        for batch_x, batch_y, batch_x_mark, batch_y_mark in test_loader:
+            batch_x = batch_x.to(device)
+            batch_y = batch_y.to(device)
+            batch_x_mark = batch_x_mark.to(device)
+            batch_y_mark = batch_y_mark.to(device)
+            
+            dec_inp = torch.zeros_like(batch_y).to(device)
+            
+            outputs, _ = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+            
+            predictions.append(outputs.cpu().numpy())
+            ground_truths.append(batch_y.cpu().numpy())
+    
+    predictions = np.concatenate(predictions, axis=0)
+    ground_truths = np.concatenate(ground_truths, axis=0)
+    
+    # 计算指标
+    mse = mean_squared_error(ground_truths.reshape(-1), predictions.reshape(-1))
+    mae = mean_absolute_error(ground_truths.reshape(-1), predictions.reshape(-1))
+    
+    print(f"测试结果 - MSE: {mse:.6f}, MAE: {mae:.6f}")
+    
+    # 可视化前几个样本
+    plt.figure(figsize=(15, 10))
+    for i in range(min(4, len(predictions))):
+        for ch in range(2):  # 双通道
+            plt.subplot(4, 2, i*2 + ch + 1)
+            plt.plot(ground_truths[i, :, ch], label='Ground Truth', color='blue')
+            plt.plot(predictions[i, :, ch], label='Prediction', color='red')
+            plt.title(f'Sample {i+1}, Channel {ch+1}')
+            plt.legend()
+    
+    plt.tight_layout()
+    plt.savefig(os.path.join(save_path, 'predictions.png'))
+    print(f"预测结果可视化保存到: {save_path}/predictions.png")
+    
+    return mse, mae
+
+def main():
+    # 配置参数
+    config = Config()
+    
+    # 创建结果目录
+    results_dir = './results/dc_patchtst_sine_wave'
+    ensure_dir(results_dir)
+    
+    print(f"使用设备: {config.device}")
+    print(f"模型配置: seq_len={config.seq_len}, pred_len={config.pred_len}")
+    
+    # 加载数据
+    data_path = './data/sine_wave/'
+    train_dataset = SineWaveDataset(data_path, config.seq_len, config.pred_len, 'train')
+    val_dataset = SineWaveDataset(data_path, config.seq_len, config.pred_len, 'val')
+    test_dataset = SineWaveDataset(data_path, config.seq_len, config.pred_len, 'test')
+    
+    train_loader = DataLoader(train_dataset, batch_size=config.batch_size, shuffle=True, num_workers=4)
+    val_loader = DataLoader(val_dataset, batch_size=config.batch_size, shuffle=False, num_workers=4)
+    test_loader = DataLoader(test_dataset, batch_size=config.batch_size, shuffle=False, num_workers=4)
+    
+    # 创建模型
+    model = Model(config).to(config.device)
+    print(f"模型参数数量: {sum(p.numel() for p in model.parameters())}")
+    
+    # 优化器和损失函数
+    optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate, weight_decay=1e-4)
+    criterion = nn.MSELoss()
+    
+    # 训练循环
+    best_val_loss = float('inf')
+    patience_counter = 0
+    train_losses = []
+    val_losses = []
+    
+    print("开始训练...")
+    start_time = time.time()
+    
+    for epoch in range(config.train_epochs):
+        epoch_start = time.time()
+        
+        # 训练
+        train_loss = train_epoch(model, train_loader, criterion, optimizer, config.device, config.use_amp)
+        
+        # 验证
+        val_loss = validate(model, val_loader, criterion, config.device)
+        
+        train_losses.append(train_loss)
+        val_losses.append(val_loss)
+        
+        epoch_time = time.time() - epoch_start
+        
+        print(f'Epoch {epoch+1:2d}/{config.train_epochs} | '
+              f'Train Loss: {train_loss:.6f} | '
+              f'Val Loss: {val_loss:.6f} | '
+              f'Time: {epoch_time:.2f}s')
+        
+        # 早停检查
+        if val_loss < best_val_loss:
+            best_val_loss = val_loss
+            patience_counter = 0
+            # 保存最佳模型
+            torch.save({
+                'model_state_dict': model.state_dict(),
+                'optimizer_state_dict': optimizer.state_dict(),
+                'config': config,
+                'epoch': epoch,
+                'val_loss': val_loss
+            }, os.path.join(results_dir, 'best_model.pth'))
+            print(f'  -> 保存最佳模型 (val_loss: {val_loss:.6f})')
+        else:
+            patience_counter += 1
+            if patience_counter >= config.patience:
+                print(f'早停触发! 最佳验证损失: {best_val_loss:.6f}')
+                break
+    
+    total_time = time.time() - start_time
+    print(f'\n训练完成! 总时间: {total_time/60:.2f} 分钟')
+    
+    # 加载最佳模型进行测试
+    checkpoint = torch.load(os.path.join(results_dir, 'best_model.pth'))
+    model.load_state_dict(checkpoint['model_state_dict'])
+    
+    print("\n测试最佳模型...")
+    test_mse, test_mae = test_model(model, test_loader, config.device, results_dir)
+    
+    # 保存训练历史
+    history = {
+        'train_losses': train_losses,
+        'val_losses': val_losses,
+        'test_mse': test_mse,
+        'test_mae': test_mae
+    }
+    
+    # 绘制训练曲线
+    plt.figure(figsize=(12, 4))
+    plt.subplot(1, 2, 1)
+    plt.plot(train_losses, label='Train Loss')
+    plt.plot(val_losses, label='Validation Loss')
+    plt.xlabel('Epoch')
+    plt.ylabel('Loss')
+    plt.legend()
+    plt.title('Training History')
+    
+    plt.subplot(1, 2, 2)
+    plt.bar(['MSE', 'MAE'], [test_mse, test_mae])
+    plt.title('Test Metrics')
+    plt.ylabel('Error')
+    
+    plt.tight_layout()
+    plt.savefig(os.path.join(results_dir, 'training_history.png'))
+    
+    print(f"\n结果保存在: {results_dir}")
+    print(f"最佳模型: {results_dir}/best_model.pth")
+    print(f"训练历史: {results_dir}/training_history.png")
+    print(f"预测可视化: {results_dir}/predictions.png")
+
+if __name__ == "__main__":
+    main()