可调word_num

config/REPST/PEMS-BAY.yaml

@@ -34,6 +34,7 @@ model:
   d_model: 64
   n_heads: 1
   input_dim: 1
+  word_num: 2000
 
 train:
   batch_size: 16

model/AEPSA/normalizer.py

@@ -3,7 +3,16 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 
-def gumbel_softmax(logits, tau=1, k=1000, hard=True):
+class GumbelSoftmax(nn.Module):
+    def __init__(self, k=1000, hard=True):
+        super(GumbelSoftmax, self).__init__()
+        self.k = k
+        self.hard = hard
+
+    def forward(self, logits):
+        return self.gumbel_softmax(logits, 1, self.k, self.hard)
+
+    def gumbel_softmax(self, logits, tau=1, k=1000, hard=True):
 
         y_soft = F.gumbel_softmax(logits, tau, hard)
 

model/AEPSA/repst.py

@@ -1,8 +1,9 @@
+from tkinter import Y
 import torch
 import torch.nn as nn
 from transformers.models.gpt2.modeling_gpt2 import GPT2Model
 from einops import rearrange
-from model.REPST.normalizer import gumbel_softmax
+from model.REPST.normalizer import GumbelSoftmax
 from model.REPST.reprogramming import PatchEmbedding, ReprogrammingLayer
 
 class repst(nn.Module):
@@ -20,6 +21,8 @@ class repst(nn.Module):
         self.d_ff = configs['d_ff']
         self.gpt_path = configs['gpt_path']
 
+        self.word_choice = GumbelSoftmax(configs['word_num'])
+
         self.d_model = configs['d_model']
         self.n_heads = configs['n_heads']
         self.d_keys = None
@@ -64,7 +67,7 @@ class repst(nn.Module):
         x_enc = rearrange(x, 'b t n c -> b n c t')
         enc_out, n_vars = self.patch_embedding(x_enc)
         self.mapping_layer(self.word_embeddings.permute(1, 0)).permute(1, 0)
-        masks = gumbel_softmax(self.mapping_layer.weight.data.permute(1,0))
+        masks = self.word_choice(self.mapping_layer.weight.data.permute(1,0))
         source_embeddings = self.word_embeddings[masks==1]
    
         enc_out = self.reprogramming_layer(enc_out, source_embeddings, source_embeddings)
@@ -77,4 +80,25 @@ class repst(nn.Module):
 
         return outputs
 
+if __name__ == '__main__':
+    configs = {
+        'device': 'cuda:0',
+        'pred_len': 24,
+        'seq_len': 24,
+        'patch_len': 6,
+        'stride': 7,
+        'dropout': 0.2,
+        'gpt_layers': 9,
+        'd_ff': 128,
+        'gpt_path': './GPT-2',
+        'd_model': 64,
+        'n_heads': 1,
+        'input_dim': 1
+    }
+    model = repst(configs)
+    x = torch.randn(16, 24, 325, 1)
+    y = model(x)
+
+    print(y.shape)
+
 

model/REPST/normalizer.py

@@ -3,7 +3,16 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 
-def gumbel_softmax(logits, tau=1, k=1000, hard=True):
+class GumbelSoftmax(nn.Module):
+    def __init__(self, k=1000, hard=True):
+        super(GumbelSoftmax, self).__init__()
+        self.k = k
+        self.hard = hard
+
+    def forward(self, logits):
+        return self.gumbel_softmax(logits, 1, self.k, self.hard)
+
+    def gumbel_softmax(self, logits, tau=1, k=1000, hard=True):
 
         y_soft = F.gumbel_softmax(logits, tau, hard)
 

model/REPST/repst.py

@@ -3,7 +3,7 @@ import torch
 import torch.nn as nn
 from transformers.models.gpt2.modeling_gpt2 import GPT2Model
 from einops import rearrange
-from model.REPST.normalizer import gumbel_softmax
+from model.REPST.normalizer import GumbelSoftmax
 from model.REPST.reprogramming import PatchEmbedding, ReprogrammingLayer
 
 class repst(nn.Module):
@@ -21,6 +21,8 @@ class repst(nn.Module):
         self.d_ff = configs['d_ff']
         self.gpt_path = configs['gpt_path']
 
+        self.word_choice = GumbelSoftmax(configs['word_num'])
+
         self.d_model = configs['d_model']
         self.n_heads = configs['n_heads']
         self.d_keys = None
@@ -65,7 +67,7 @@ class repst(nn.Module):
         x_enc = rearrange(x, 'b t n c -> b n c t')
         enc_out, n_vars = self.patch_embedding(x_enc)
         self.mapping_layer(self.word_embeddings.permute(1, 0)).permute(1, 0)
-        masks = gumbel_softmax(self.mapping_layer.weight.data.permute(1,0))
+        masks = self.word_choice(self.mapping_layer.weight.data.permute(1,0))
         source_embeddings = self.word_embeddings[masks==1]
    
         enc_out = self.reprogramming_layer(enc_out, source_embeddings, source_embeddings)