Traceback (most recent call last):
File "E:/Con-GAE-main/src/train.py", line 114, in <module>
train_loader = DataLoader(train_dataset, **params)
File "E:\Anaconda3\lib\site-packages\torch_geometric\data\dataloader.py", line 43, in __init__
super(DataLoader,
File "E:\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 266, in __init__
sampler = RandomSampler(dataset, generator=generator) # type: ignore
File "E:\Anaconda3\lib\site-packages\torch\utils\data\sampler.py", line 102, in __init__
import os
import torch
import numpy as np
from torch_geometric.data import Data
from sklearn.metrics import roc_curve, auc
import argparse
import os
import sys
import random
import torch.nn as nn
from torch.utils import data
import pandas as pd
import matplotlib.pyplot as plt
from random import shuffle
import pickle
import torchvision.transforms as transforms
import time
from torch_geometric.data import InMemoryDataset, Dataset, Data, DataLoader
import math
from data_util import ConTrafficGraphDataset as trafficDataset
from model import ConGAE,ConGAE_t, ConGAE_sp, deepConGAE
parser = argparse.ArgumentParser()
# model
parser.add_argument('--model', default = 'ConGAE', help = 'Model type: ConGAE, ConGAE_t, ConGAE_sp, deepConGAE')
# training parameters
parser.add_argument('--randomseed', type=int, default = 1)
parser.add_argument('--train_epoch', type =int, default = 150 , help = 'number of training epochs')
parser.add_argument('--lr', default = 5e-5 , help = 'learning rate')
parser.add_argument('--dropout_p', default = 0.2 , help = 'drop out rate')
parser.add_argument('--adj_drop', default = 0.2 , help = 'edge dropout rate')
parser.add_argument('--verbal', default = False, type = bool , help = 'print loss during training')
# 2-layer ConGAE parameters
parser.add_argument('--input_dim', type=int, default = 4, help = 'input feature dimension')
parser.add_argument('--n_nodes', type=int, default = 50, help = 'total number of nodes in the graph')
parser.add_argument('--node_dim1', type=int, default = 300, help = 'node embedding dimension of the first GCN layer')
parser.add_argument('--node_dim2', type=int, default = 150, help = 'node embedding dimension of the second GCN layer')
parser.add_argument('--encode_dim', type=int, default = 150, help = 'final graph embedding dimension of the Con-GAE encoder')
parser.add_argument('--hour_emb', type=int, default = 100, help = 'hour emnbedding dimension')
parser.add_argument('--week_emb', type=int, default = 100, help = 'week emnbedding dimension')
parser.add_argument('--decoder', type=str, default = 'concatDec', help = 'decoder type:concatDec, bilinearDec')
# deepConGAE parameters
parser.add_argument('--hidden_list', nargs="*", type=int, default = [300, 150], help = 'the node embedding dimension of each layer of GCN')
parser.add_argument('--decode_dim', type=int, default = 150, help = 'the node embedding dimension at decoding')
# files
parser.add_argument('--log_dir', default = '../log/' , help = 'directory to save model')
args = parser.parse_args()
print(args)
#Reproducability
np.random.seed(seed=args.randomseed)
random.seed(args.randomseed)
torch.manual_seed(args.randomseed)
result_dir = args.log_dir + 'results/'
if not os.path.exists(result_dir):
os.makedirs(result_dir)
# ## load data
dirName = "../data/selected_50_orig/"
with open(dirName + 'partition_dict', 'rb') as file:
partition = pickle.load(file)
# item_d: whihc time slice each id correspond to
with open(dirName + 'item_dict', 'rb') as file:
item_d = pickle.load(file)
node_X = np.load(dirName + 'node_X.npy')
node_posx = np.mean(node_X[:, :2], 1)
node_posy = np.mean(node_X[:, 2:], 1)
node_X = torch.from_numpy(node_X).float()
tt_min, tt_max =np.load(dirName + 'tt_minmax.npy' )
start_time = 0+24*23
end_time = 23+24*27
# reset partition
all_data = partition['train'] + partition['val']
partition_test = all_data[350:750] # includes NFL, 400 points, 30% are NFL
partition_val = all_data[:150]
partition_train = all_data[150:350] + all_data[750:] # the rest
source_dir = dirName # full sample size (~2000)
# Parameters
params = {'batch_size': 10,
'shuffle': True,
'num_workers': 0}
params_val = {'batch_size': 10,
'shuffle': False,
'num_workers': 0}
root = '../data/selected_50_pg/root/'
# data loaders
train_dataset = trafficDataset(root, partition_train, node_X, item_d, source_dir )
test_dataset = trafficDataset(root, partition_test, node_X, item_d, source_dir)
val_dataset = trafficDataset(root, partition_val, node_X, item_d, source_dir)
train_loader = DataLoader(train_dataset, **params)
test_loader = DataLoader(test_dataset,**params_val )
val_loader = DataLoader(val_dataset,**params_val )
# ## load model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if args.model == 'ConGAE_sp':
model = ConGAE_sp(args.input_dim, args.node_dim1,args.node_dim2, args.dropout_p,args.adj_drop, decoder = args.decoder, n_nodes = args.n_nodes)
if args.model == 'ConGAE_t':
model = ConGAE_t(args.input_dim, args.node_dim1,args.node_dim2, args.dropout_p,args.adj_drop,decoder = args.decoder, hour_emb = args.hour_emb, week_emb = args.week_emb,n_nodes = args.n_nodes)
if args.model == 'ConGAE':
model = ConGAE(input_feat_dim=args.input_dim, node_dim1 =args.node_dim1, node_dim2=args.node_dim2, encode_dim = args.encode_dim ,hour_emb = args.hour_emb, week_emb = args.week_emb, n_nodes = args.n_nodes)
if args.model == 'deepConGAE':
model = deepConGAE(args.input_dim, hidden_list = args.hidden_list, encode_dim = args.encode_dim,decode_dim = args.decode_dim, dropout = args.dropout_p, adj_drop = args.adj_drop, hour_emb = args.hour_emb, week_emb = args.week_emb,n_nodes = args.n_nodes)
model.float()
# specify optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
criterion = nn.MSELoss()
def calc_rmse(recon_adj, adj, tt_min, tt_max):
adj = adj * (tt_max - tt_min) + tt_min
recon_adj = recon_adj * (tt_max - tt_min) + tt_min
rmse = criterion(recon_adj, adj)
return torch.sqrt(rmse)
def train(epoch, train_loader ,test_loader, best_val):
model.train()
train_loss = 0
loss_train = []
loss_val = []
for graph_data in train_loader:
graph_data = graph_data.to(device)
optimizer.zero_grad()
if args.model == 'ConGAE_sp':
recon = model(graph_data.x, graph_data.edge_index, graph_data.edge_attr)
else:
recon = model(graph_data.x, graph_data.edge_index, graph_data.edge_attr,graph_data.hour, graph_data.week)
loss = criterion(recon, graph_data.edge_attr)
loss.backward()
optimizer.step()
loss_train.append(loss.item())
for graph_val in val_loader:
# evaluation
model.eval()
graph_val = graph_val.to(device)
with torch.no_grad():
if args.model == 'ConGAE_sp':
recon_val = model(graph_val.x, graph_val.edge_index, graph_val.edge_attr)
else:
recon_val = model(graph_val.x, graph_val.edge_index, graph_val.edge_attr, graph_val.hour, graph_val.week)
mse_val = criterion(recon_val, graph_val.edge_attr)
loss_val.append(mse_val.item())
loss_train = sum(loss_train) / len(loss_train)
loss_val = sum(loss_val) / len(loss_val)
# print results
if args.verbal and epoch % 15 == 0:
print('Train Epoch: {} loss: {:e} val_loss: {:e}'.format(
epoch, loss_train, loss_val ))
rmse = math.sqrt(loss_val) * (tt_max - tt_min)
print('validation travel time rmse mean: {:e}'.format(rmse))
# early-stopping
if loss_val < best_val:
torch.save({
'epoch' : epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, model_path)
best_val = loss_val
return loss_train, loss_val, best_val
# ## Train
loss_track = []
val_track = []
model = model.to(device)
n_epochs = args.train_epoch
start = time.time()
best_val = float('inf') # for early stopping
model_path = args.log_dir + args.model + '.pt'
lr_decay_step_size = 100
for epoch in range(1, n_epochs+1):
train_loss, val_loss, best_val = train(epoch, train_loader, val_loader, best_val)
loss_track.append(train_loss)
val_track.append(val_loss)
if epoch % lr_decay_step_size == 0:
for param_group in optimizer.param_groups:
param_group['lr'] = 0.5 * param_group['lr']
print("time for {} epochs: {:.3f} min".format(n_epochs, (time.time() - start)/60))
# plot learning curve
plt.plot(np.array(loss_track), label = 'traiing')
plt.plot(np.array(val_track), label = 'validaiton')
plt.title("loss")
plt.xlabel("# epoch")
plt.ylabel("MSE loss")
plt.legend()
# plt.ylim(0.4, 1)
# plt.show()
plt.savefig(result_dir + args.model +"_training_curve.png")
# save args config
with open(args.log_dir + args.model + '_args.pkl', 'wb') as fp:
pickle.dump(args, fp)
if not isinstance(self.num_samples, int) or self.num_samples <= 0:
File "E:\Anaconda3\lib\site-packages\torch\utils\data\sampler.py", line 110, in num_samples
return len(self.data_source)
TypeError: 'NoneType' object cannot be interpreted as an integer
检查下报错的那句看看data有没有。这个报错是指你的哪里是空的，不能遍历