Merge pull request #4 from Vicky-51/main

A Modified Version for Batch Training

Batch_Training_Version/GCN_models.py

@@ -0,0 +1,19 @@
+import torch.nn as nn
+import torch.nn.functional as F
+from layers import cheb_conv
+import numpy as np
+
+
+class GCN(nn.Module):
+    def __init__(self, nfeat, nhid, nclass, adj, cheb_K, dropout):
+        super(GCN, self).__init__()
+
+        self.gc1 = cheb_conv(nfeat, nhid, adj, cheb_K)
+        self.gc2 = cheb_conv(nhid, nclass, adj, cheb_K)
+        self.dropout = dropout
+
+    def forward(self, x, adj):
+        x = F.relu(self.gc1(x))
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = self.gc2(x)
+        return F.log_softmax(x, dim=1)

Batch_Training_Version/PEMSD7/PEMSD7数据集介绍.txt

@@ -0,0 +1,10 @@
+V_25文件：取PEMS数据集25个传感器。
+行：表示时间，共12672行。时间间隔5min，一天288个5min，2012年5月和6月工作日共44天。288*44（天）=12672（行）
+列：传感器数量，共25列，代表25个传感器。
+
+
+W_25文件：邻接矩阵，无向图。
+边表示传感器间距离。25行，25列。
+
+
+github限制，完整 PEMSD7 加QQ：935140093

Batch_Training_Version/PEMSD7/W_25.csv

@@ -0,0 +1,25 @@
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Batch_Training_Version/ReadMe.md

@@ -0,0 +1,55 @@
+# Modified Version for STTN Model
+
+**Several modifications have been made based on the code from https://github.com/wubin5/STTN**
+
+A Pytorch version for paper 
+
+[Spatial-Temporal Transformer Networks for Traffic Flow Forecasting]: https://arxiv.org/pdf/2001.02908.pdf
+
+
+
+## Main Modification
+
+- This version allows batch training to improve training efficiency and performance.
+- Fix the bug in multi-head attention.
+- Use *Chebyshev Polynomials* to model *Fixed Graph Convolution Layer*, which is the same as original paper.
+- Provide positional embedding options with sine/cosine functions, which is the same as [Attention is All Your Need]: https://arxiv.org/abs/1706.03762.
+- Add positional embedding to all three components: key, query, value.
+
+
+
+## How to Run?
+
+### prepareData.py
+
+Data preprocessing. Adapted from [ASTGCN]: https://github.com/guoshnBJTU/ASTGCN-r-pytorch. 
+
+- graph_signal_matrix_filename: The path for raw data, shape: [L, N]. Each row represents a  record, N means there are N traffic sensors in total.
+- This code will return and save a processed .npz file in the same path.
+- Other parameters you may refer to the comments in python file.
+
+
+
+### train_batch.py
+
+Training process. Adapted from [ASTGCN]: https://github.com/guoshnBJTU/ASTGCN-r-pytorch. 
+
+- adj_mx: The path for adjacency matrix.
+
+- params_path: The path for saving model parameters and training log.
+
+- filename: The data we got from prepareData.py. You may also use your own preprocess function. The shape of our dataloader should be [Batch_size(B), number_of_sensors(N), input_channels(C), input_length(T)]
+
+- All the training process will be saved.
+
+- You should write down the best epochs for further test.
+
+
+
+### predict_batch.py
+
+Testing process.
+
+- Similar setting as train_batch.py.
+- It will return and save the prediction results for test set.
+