添加部分注释

huaka1 · Nov 12, 2024 · 62c6176 · 62c6176
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diff --git a/.gitignore b/.gitignore
@@ -0,0 +1,216 @@
+# File created using '.gitignore Generator' for Visual Studio Code: https://bit.ly/vscode-gig
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diff --git a/cross_models/attn.py b/cross_models/attn.py
@@ -90,17 +90,20 @@ def __init__(self, seg_num, factor, d_model, n_heads, d_ff = None, dropout=0.1):
                                 nn.GELU(),
                                 nn.Linear(d_ff, d_model))
 
-    def forward(self, x):
+    def forward(self, x): # (B, C, patch_dim, d_model)
         #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')
+        # 对不同的patch进行自注意力
+        time_in = rearrange(x, 'b ts_d seg_num d_model -> (b ts_d) seg_num d_model') # (B * C, patch_dim, d_model)
         time_enc = self.time_attention(
             time_in, time_in, time_in
         )
+
+        # 进行一系列dropout和正则化
         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)
+        dim_in = self.norm2(dim_in) # (B * C, patch_dim, d_model)
 
         #Cross Dimension Stage: use a small set of learnable vectors to aggregate and distribute messages to build the D-to-D connection
         dim_send = rearrange(dim_in, '(b ts_d) seg_num d_model -> (b seg_num) ts_d d_model', b = batch)

diff --git a/cross_models/cross_embed.py b/cross_models/cross_embed.py
@@ -14,9 +14,9 @@ def __init__(self, seg_len, d_model):
 
     def forward(self, x):
         batch, ts_len, ts_dim = x.shape
-
+        # 分段 （32, 168, 7）->（6272, 6）
         x_segment = rearrange(x, 'b (seg_num seg_len) d -> (b d seg_num) seg_len', seg_len = self.seg_len)
-        x_embed = self.linear(x_segment)
-        x_embed = rearrange(x_embed, '(b d seg_num) d_model -> b d seg_num d_model', b = batch, d = ts_dim)
+        x_embed = self.linear(x_segment) #（6272, d_model）
+        x_embed = rearrange(x_embed, '(b d seg_num) d_model -> b d seg_num d_model', b = batch, d = ts_dim) # (32, 7, 28, 256)
 
         return x_embed
diff --git a/cross_models/cross_encoder.py b/cross_models/cross_encoder.py
@@ -60,7 +60,7 @@ def __init__(self, win_size, d_model, n_heads, d_ff, depth, dropout, \
             self.encode_layers.append(TwoStageAttentionLayer(seg_num, factor, d_model, n_heads, \
                                                         d_ff, dropout))
 
-    def forward(self, x):
+    def forward(self, x): # (B, C, patch_dim, d_model)
         _, ts_dim, _, _ = x.shape
 
         if self.merge_layer is not None:
@@ -86,7 +86,7 @@ def __init__(self, e_blocks, win_size, d_model, n_heads, d_ff, block_depth, drop
             self.encode_blocks.append(scale_block(win_size, d_model, n_heads, d_ff, block_depth, dropout,\
                                             ceil(in_seg_num/win_size**i), factor))
 
-    def forward(self, x):
+    def forward(self, x): # (B, C, patch_dim, d_model)
         encode_x = []
         encode_x.append(x)
 

diff --git a/cross_models/cross_former.py b/cross_models/cross_former.py
@@ -44,21 +44,24 @@ def __init__(self, data_dim, in_len, out_len, seg_len, win_size = 4,
         self.decoder = Decoder(seg_len, e_layers + 1, d_model, n_heads, d_ff, dropout, \
                                     out_seg_num = (self.pad_out_len // seg_len), factor = factor)
 
-    def forward(self, x_seq):
+    def forward(self, x_seq): # （B，L，C）
         if (self.baseline):
             base = x_seq.mean(dim = 1, keepdim = True)
         else:
             base = 0
         batch_size = x_seq.shape[0]
-        if (self.in_len_add != 0):
+        # 如果无法平均分段（因为有多余的),则取开头第一个元素复制in_len_add次然后填充，具体看论文附录D1
+        if (self.in_len_add != 0): 
             x_seq = torch.cat((x_seq[:, :1, :].expand(-1, self.in_len_add, -1), x_seq), dim = 1)
 
-        x_seq = self.enc_value_embedding(x_seq)
-        x_seq += self.enc_pos_embedding
+        # embedding和patchtst很像
+        x_seq = self.enc_value_embedding(x_seq) # (B, C, patch_dim, d_model)
+        x_seq += self.enc_pos_embedding # + (1, C, patch_dim, d_model)
         x_seq = self.pre_norm(x_seq)
 
         enc_out = self.encoder(x_seq)
-
+
+        # 在batch_size纬度重复
         dec_in = repeat(self.dec_pos_embedding, 'b ts_d l d -> (repeat b) ts_d l d', repeat = batch_size)
         predict_y = self.decoder(dec_in, enc_out)