更名ASTRA
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@ -153,7 +153,7 @@
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"args": "--config ./config/REPST/AirQuality.yaml"
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},
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// AEPSA 模型组
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// ASTRA 模型组
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{
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"name": "AEPSA: PEMS-BAY",
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"type": "debugpy",
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@ -2,7 +2,7 @@ basic:
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dataset: AirQuality
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device: cuda:0
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mode: train
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model: AEPSA
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model: ASTRA
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seed: 2023
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data:
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@ -2,7 +2,7 @@ basic:
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dataset: BJTaxi-InFlow
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device: cuda:0
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mode: train
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model: AEPSA
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model: ASTRA
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seed: 2023
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data:
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@ -2,7 +2,7 @@ basic:
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dataset: BJTaxi-InFlow
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device: cuda:0
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mode: train
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model: AEPSA
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model: ASTRA
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seed: 2023
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data:
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@ -2,7 +2,7 @@ basic:
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dataset: BJTaxi-OutFlow
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device: cuda:0
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mode: train
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model: AEPSA
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model: ASTRA
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seed: 2023
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data:
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@ -2,7 +2,7 @@ basic:
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dataset: BJTaxi-OutFlow
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device: cuda:0
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mode: train
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model: AEPSA
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model: ASTRA
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seed: 2023
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data:
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@ -2,7 +2,7 @@ basic:
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dataset: METR-LA
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device: cuda:0
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mode: train
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model: AEPSA
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model: ASTRA
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seed: 2023
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data:
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@ -2,7 +2,7 @@ basic:
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dataset: NYCBike-InFlow
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device: cuda:0
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mode: train
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model: AEPSA
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model: ASTRA
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seed: 2023
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data:
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@ -2,7 +2,7 @@ basic:
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dataset: NYCBike-OutFlow
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device: cuda:0
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mode: train
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model: AEPSA
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model: ASTRA
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seed: 2023
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data:
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@ -2,7 +2,7 @@ basic:
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dataset: NYCBike-InFlow
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device: cuda:0
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mode: train
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model: AEPSA
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model: ASTRA
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seed: 2023
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data:
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@ -2,7 +2,7 @@ basic:
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dataset: NYCBike-OutFlow
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device: cuda:0
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mode: train
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model: AEPSA
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model: ASTRA
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seed: 2023
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data:
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@ -2,7 +2,7 @@ basic:
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dataset: PEMS-BAY
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device: cuda:0
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mode: train
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model: AEPSA
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model: ASTRA
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seed: 2023
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data:
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@ -2,7 +2,7 @@ basic:
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dataset: SolarEnergy
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device: cuda:0
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mode: train
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model: AEPSA
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model: ASTRA
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seed: 2023
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data:
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@ -2,8 +2,8 @@ import torch
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import torch.nn as nn
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from transformers.models.gpt2.modeling_gpt2 import GPT2Model
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from einops import rearrange
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from model.AEPSA.normalizer import GumbelSoftmax
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from model.AEPSA.reprogramming import PatchEmbedding, ReprogrammingLayer
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from model.ASTRA.normalizer import GumbelSoftmax
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from model.ASTRA.reprogramming import PatchEmbedding, ReprogrammingLayer
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import torch.nn.functional as F
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class DynamicGraphEnhancer(nn.Module):
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@ -147,10 +147,10 @@ class GraphEnhancedEncoder(nn.Module):
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return torch.stack(enhanced_features, dim=0)
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class AEPSA(nn.Module):
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class ASTRA(nn.Module):
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def __init__(self, configs):
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super(AEPSA, self).__init__()
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super(ASTRA, self).__init__()
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self.device = configs['device']
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self.pred_len = configs['pred_len']
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self.seq_len = configs['seq_len']
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@ -2,8 +2,8 @@ import torch
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import torch.nn as nn
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from transformers.models.gpt2.modeling_gpt2 import GPT2Model
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from einops import rearrange
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from model.AEPSA.normalizer import GumbelSoftmax
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from model.AEPSA.reprogramming import ReprogrammingLayer
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from model.ASTRA.normalizer import GumbelSoftmax
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from model.ASTRA.reprogramming import ReprogrammingLayer
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import torch.nn.functional as F
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# 基于动态图增强的时空序列预测模型实现
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@ -113,10 +113,10 @@ class GraphEnhancedEncoder(nn.Module):
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return torch.stack(enhanced_features, dim=0) # 堆叠返回[B,N,hidden_dim*(K+1)]，每个节点在每个k阶下的切比雪夫特征
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class AEPSA(nn.Module):
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class ASTRA(nn.Module):
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"""自适应特征投影时空自注意力模型"""
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def __init__(self, configs):
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super(AEPSA, self).__init__()
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super(ASTRA, self).__init__()
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self.device = configs['device'] # 运行设备
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self.pred_len = configs['pred_len'] # 预测序列长度
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self.seq_len = configs['seq_len'] # 输入序列长度
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@ -2,8 +2,8 @@ import torch
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import torch.nn as nn
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from transformers.models.gpt2.modeling_gpt2 import GPT2Model
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from einops import rearrange
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from model.AEPSA.normalizer import GumbelSoftmax
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from model.AEPSA.reprogramming import ReprogrammingLayer
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from model.ASTRA.normalizer import GumbelSoftmax
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from model.ASTRA.reprogramming import ReprogrammingLayer
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import torch.nn.functional as F
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# 基于动态图增强的时空序列预测模型实现
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@ -113,10 +113,10 @@ class GraphEnhancedEncoder(nn.Module):
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return torch.stack(enhanced_features, dim=0) # 堆叠返回[B,N,hidden_dim*(K+1)]，每个节点在每个k阶下的切比雪夫特征
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class AEPSA(nn.Module):
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class ASTRA(nn.Module):
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"""自适应特征投影时空自注意力模型"""
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def __init__(self, configs):
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super(AEPSA, self).__init__()
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super(ASTRA, self).__init__()
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self.device = configs['device'] # 运行设备
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self.pred_len = configs['pred_len'] # 预测序列长度
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self.seq_len = configs['seq_len'] # 输入序列长度
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@ -23,9 +23,9 @@ from model.ST_SSL.ST_SSL import STSSLModel
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from model.STGNRDE.Make_model import make_model as make_nrde_model
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from model.STAWnet.STAWnet import STAWnet
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from model.REPST.repst import repst as REPST
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from model.AEPSA.aepsa import AEPSA as AEPSA
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from model.AEPSA.aepsav2 import AEPSA as AEPSAv2
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from model.AEPSA.aepsav3 import AEPSA as AEPSAv3
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from model.ASTRA.astra import ASTRA as ASTRA
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from model.ASTRA.astrav2 import ASTRA as ASTRAv2
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from model.ASTRA.astrav3 import ASTRA as ASTRAv3
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@ -83,9 +83,9 @@ def model_selector(config):
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return STAWnet(model_config)
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case "REPST":
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return REPST(model_config)
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case "AEPSA":
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return AEPSA(model_config)
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case "AEPSA_v2":
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return AEPSAv2(model_config)
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case "AEPSA_v3":
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return AEPSAv3(model_config)
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case "ASTRA":
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return ASTRA(model_config)
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case "ASTRA_v2":
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return ASTRAv2(model_config)
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case "ASTRA_v3":
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return ASTRAv3(model_config)
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@ -104,14 +104,14 @@ class Trainer:
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loss = self.loss(output, label)
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# 检查output和label的shape是否一致
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if output.shape == label.shape:
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print(f"✓ Test passed: output shape {output.shape} matches label shape {label.shape}")
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import sys
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sys.exit(0)
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else:
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print(f"✗ Test failed: output shape {output.shape} does not match label shape {label.shape}")
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import sys
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sys.exit(1)
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# if output.shape == label.shape:
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# print(f"✓ Test passed: output shape {output.shape} matches label shape {label.shape}")
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# import sys
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# sys.exit(0)
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# else:
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# print(f"✗ Test failed: output shape {output.shape} does not match label shape {label.shape}")
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# import sys
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# sys.exit(1)
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# 反归一化
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d_output = self.scaler.inverse_transform(output)
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