Update README.md

.gitignore

@@ -5,6 +5,7 @@ experiments/
 *.csv
 *.npz
 *.pkl
+data/
 
 # ---> Python
 # Byte-compiled / optimized / DLL files

README.md

@@ -1,7 +1,8 @@
 依赖包
+支持python 3.10以上版本。
 
-pip install pyyaml tqdm statsmodels h5py
+conda create -n trafficwheel python=3.10
-pip3 install torch torchvision torchaudio
+pip install pyyaml tqdm statsmodels h5py kagglehub torch torchvision torchaudio
 
 
 

config/args_parser.py

@@ -3,11 +3,11 @@ import yaml
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Model Training and Testing')
-    parser.add_argument('--dataset', default='PEMSD7(L)', type=str)
+    parser.add_argument('--dataset', default='PEMSD8', type=str)
     parser.add_argument('--mode', default='train', type=str)
     parser.add_argument('--device', default='cuda:0', type=str, help='Indices of GPUs')
     parser.add_argument('--debug', default=False, type=eval)
-    parser.add_argument('--model', default='DDGCRN', type=str)
+    parser.add_argument('--model', default='GWN', type=str)
     parser.add_argument('--cuda', default=True, type=bool)
     parser.add_argument('--sample', default=1, type=int)
     parser.add_argument('--emb', default=12, type=int)

dataloader/cde_loader/cdeDataloader.py

@@ -1,4 +1,4 @@
-import controldiffeq
+import model.STGNCDE.controldiffeq
 from lib.normalization import normalize_dataset
 import numpy as np
 import gc

lib/Download_data.py

@@ -0,0 +1,146 @@
+import os
+import requests
+import zipfile
+import shutil
+import kagglehub  # 假设 kagglehub 是一个可用的库
+from tqdm import tqdm
+
+# 定义文件完整性信息的字典
+
+
+def check_and_download_data():
+    """
+    检查 data 文件夹的完整性，并根据缺失文件类型下载相应数据。
+    """
+    current_working_dir = os.getcwd()  # 获取当前工作目录
+    data_dir = os.path.join(current_working_dir, "data")  # 假设 data 文件夹在当前工作目录下
+
+    expected_structure = {
+        "PEMS03": ["PEMS03.csv", "PEMS03.npz", "PEMS03.txt", "PEMS03_dtw_distance.npy", "PEMS03_spatial_distance.npy"],
+        "PEMS04": ["PEMS04.csv", "PEMS04.npz", "PEMS04_dtw_distance.npy", "PEMS04_spatial_distance.npy"],
+        "PEMS07": ["PEMS07.csv", "PEMS07.npz", "PEMS07_dtw_distance.npy", "PEMS07_spatial_distance.npy"],
+        "PEMS08": ["PEMS08.csv", "PEMS08.npz", "PEMS08_dtw_distance.npy", "PEMS08_spatial_distance.npy"]
+    }
+
+    current_dir = os.getcwd()  # 获取当前工作目录
+    missing_adj = False
+    missing_main_files = False
+
+    # 检查 data 文件夹是否存在
+    if not os.path.exists(data_dir) or not os.path.isdir(data_dir):
+        # print(f"目录 {data_dir} 不存在。")
+        print("正在下载所有必要的数据文件...")
+        missing_adj = True
+        missing_main_files = True
+    else:
+        # 检查根目录下的 get_adj.py 文件
+        if "get_adj.py" not in os.listdir(data_dir):
+            # print(f"根目录下缺少文件 get_adj.py。")
+            missing_adj = True
+
+        # 遍历预期的文件结构
+        for subfolder, expected_files in expected_structure.items():
+            subfolder_path = os.path.join(data_dir, subfolder)
+
+            # 检查子文件夹是否存在
+            if not os.path.exists(subfolder_path) or not os.path.isdir(subfolder_path):
+                # print(f"子文件夹 {subfolder} 不存在。")
+                missing_main_files = True
+                continue
+
+            # 获取子文件夹中的实际文件列表
+            actual_files = os.listdir(subfolder_path)
+
+            # 检查是否缺少文件
+            for expected_file in expected_files:
+                if expected_file not in actual_files:
+                    # print(f"子文件夹 {subfolder} 中缺少文件 {expected_file}。")
+                    if "_dtw_distance.npy" in expected_file or "_spatial_distance.npy" in expected_file:
+                        missing_adj = True
+                    else:
+                        missing_main_files = True
+
+    # 根据缺失文件类型调用下载逻辑
+    if missing_adj:
+        download_adj_data(current_dir)
+    if missing_main_files:
+        download_kaggle_data(current_dir)
+
+    return True
+
+
+def download_adj_data(current_dir, max_retries=3):
+    """
+    下载并解压 adj.zip 文件，并显示下载进度条。
+    如果下载失败，最多重试 max_retries 次。
+    """
+    url = "https://code.zhang-heng.com/static/adj.zip"
+    retries = 0
+
+    while retries <= max_retries:
+        try:
+            print(f"正在从 {url} 下载邻接矩阵文件...")
+            response = requests.get(url, stream=True)
+
+            if response.status_code == 200:
+                total_size = int(response.headers.get('content-length', 0))
+                block_size = 1024  # 1KB
+                t = tqdm(total=total_size, unit='B', unit_scale=True, desc="下载进度")
+
+                zip_file_path = os.path.join(current_dir, "adj.zip")
+                with open(zip_file_path, 'wb') as f:
+                    for data in response.iter_content(block_size):
+                        f.write(data)
+                        t.update(len(data))
+                t.close()
+
+                # print("下载完成，文件已保存到:", zip_file_path)
+
+                if os.path.exists(zip_file_path):
+                    with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
+                        zip_ref.extractall(current_dir)
+                    # print("数据集已解压到:", current_dir)
+                    os.remove(zip_file_path)  # 删除zip文件
+                else:
+                    print("未找到下载的zip文件，跳过解压。")
+                break  # 下载成功，退出循环
+            else:
+                print(f"下载失败，状态码: {response.status_code}。请检查链接是否有效。")
+        except Exception as e:
+            print(f"下载或解压数据集时出错: {e}")
+            print("如果链接无效，请检查URL的合法性或稍后重试。")
+
+        retries += 1
+        if retries > max_retries:
+            raise Exception(f"下载失败，已达到最大重试次数（{max_retries}次）。请检查链接或网络连接。")
+
+
+def download_kaggle_data(current_dir):
+    """
+    下载 KaggleHub 数据集，并将 data 文件夹合并到当前工作目录。
+    如果目标文件夹已存在，会覆盖冲突的文件。
+    """
+    try:
+        print("正在下载 KaggleHub 数据集...")
+        path = kagglehub.dataset_download("elmahy/pems-dataset")
+        # print("Path to KaggleHub dataset files:", path)
+
+        if os.path.exists(path):
+            data_folder_path = os.path.join(path, "data")
+            if os.path.exists(data_folder_path):
+                destination_path = os.path.join(current_dir, "data")
+
+                # 使用 shutil.copytree 合并文件夹，覆盖冲突的文件
+                shutil.copytree(data_folder_path, destination_path, dirs_exist_ok=True)
+                # print(f"data 文件夹已合并到: {destination_path}")
+            # else:
+                # print("未找到 data 文件夹，跳过合并操作。")
+        # else:
+            # print("未找到 KaggleHub 数据集路径，跳过处理。")
+    except Exception as e:
+        print(f"下载或处理 KaggleHub 数据集时出错: {e}")
+
+
+# 主程序
+if __name__ == "__main__":
+    check_and_download_data()

run.py

@@ -1,6 +1,11 @@
 import os
 import shutil
 
+# 检查数据集完整性
+from lib.Download_data import check_and_download_data
+data_complete = check_and_download_data()
+assert data_complete is not None, "数据集下载失败，请重试！"
+
 import torch
 from datetime import datetime
 # import time
@@ -8,9 +13,10 @@ from datetime import datetime
 from config.args_parser import parse_args
 from lib.initializer import init_model, init_optimizer
 from lib.loss_function import get_loss_function
+
 from dataloader.loader_selector import get_dataloader
 from trainer.trainer_selector import select_trainer
-import yaml  # 需要安装 PyYAML 库：pip install pyyaml
+import yaml
 
 
 def main():
@@ -24,6 +30,7 @@ def main():
         args['device'] = 'cpu'
         args['model']['device'] = args['device']
 
+
     # Initialize model
     model = init_model(args['model'], device=args['device'])