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train_LSTM_full_r.py
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train_LSTM_full_r.py
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#!/usr/bin/env python
# coding: utf-8
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import os
from time import time
import shutil
import argparse
import configparser
from model.LSTM_full_r import make_model
from lib.utils import load_fullwhd_graphdata_channel1, get_adjacency_matrix, compute_val_loss_astgcn_full, predict_and_save_results_astgcn_full
from tensorboardX import SummaryWriter
parser = argparse.ArgumentParser()
parser.add_argument("--config", default='configurations/PEMS04_astgcn.conf', type=str,
help="configuration file path")
args = parser.parse_args()
config = configparser.ConfigParser()
print('Read configuration file: %s' % (args.config))
config.read(args.config)
data_config = config['Data']
training_config = config['Training']
adj_filename = data_config['adj_filename']
graph_signal_matrix_filename = data_config['graph_signal_matrix_filename']
if config.has_option('Data', 'id_filename'):
id_filename = data_config['id_filename']
else:
id_filename = None
num_of_vertices = int(data_config['num_of_vertices'])
points_per_hour = int(data_config['points_per_hour'])
num_for_predict = int(data_config['num_for_predict'])
len_input = int(data_config['len_input'])
dataset_name = data_config['dataset_name']
# LSTM
input_size = int(training_config['input_size'])
hidden_size = int(training_config['hidden_size'])
output_size = int(training_config['output_size'])
model_name = training_config['model_name']
ctx = training_config['ctx']
os.environ["CUDA_VISIBLE_DEVICES"] = ctx
USE_CUDA = torch.cuda.is_available()
if (USE_CUDA):
DEVICE = torch.device('cuda:0')
else:
DEVICE = torch.device('cpu')
print("CUDA:", USE_CUDA, DEVICE)
learning_rate = float(training_config['learning_rate'])
epochs = int(training_config['epochs'])
start_epoch = int(training_config['start_epoch'])
batch_size = int(training_config['batch_size'])
num_of_weeks = int(training_config['num_of_weeks'])
num_of_days = int(training_config['num_of_days'])
num_of_hours = int(training_config['num_of_hours'])
folder_dir = '%s_h%dd%dw%d_hidden_size%d_%e' % (model_name, num_of_hours, num_of_days, num_of_weeks, hidden_size, learning_rate)
print('folder_dir:', folder_dir)
params_path = os.path.join('../experiments', dataset_name, folder_dir)
print('params_path:', params_path)
train_loader, train_target_tensor, val_loader, val_target_tensor, test_loader, test_target_tensor, _mean, _std = load_fullwhd_graphdata_channel1(
graph_signal_matrix_filename, num_of_hours,
num_of_days, num_of_weeks, DEVICE, batch_size)
net = make_model(DEVICE, input_size, hidden_size, output_size)
def train_main():
if (start_epoch == 0) and (not os.path.exists(params_path)):
os.makedirs(params_path)
print('create params directory %s' % (params_path))
elif (start_epoch == 0) and (os.path.exists(params_path)):
shutil.rmtree(params_path)
os.makedirs(params_path)
print('delete the old one and create params directory %s' % (params_path))
elif (start_epoch > 0) and (os.path.exists(params_path)):
print('train from params directory %s' % (params_path))
else:
raise SystemExit('Wrong type of model!')
print('param list:')
print('CUDA\t', DEVICE)
print('batch_size\t', batch_size)
print('graph_signal_matrix_filename\t', graph_signal_matrix_filename)
print('start_epoch\t', start_epoch)
print('epochs\t', epochs)
criterion = nn.L1Loss().to(DEVICE)
optimizer = optim.Adam(net.parameters(), lr=learning_rate)
sw = SummaryWriter(logdir=params_path, flush_secs=5)
print(net)
print('Net\'s state_dict:')
total_param = 0
for param_tensor in net.state_dict():
print(param_tensor, '\t', net.state_dict()[param_tensor].size())
total_param += np.prod(net.state_dict()[param_tensor].size())
print('Net\'s total params:', total_param)
print('Optimizer\'s state_dict:')
for var_name in optimizer.state_dict():
print(var_name, '\t', optimizer.state_dict()[var_name])
global_step = 0
best_epoch = 0
best_val_loss = np.inf
start_time = time()
if start_epoch > 0:
params_filename = os.path.join(params_path, 'epoch_%s.params' % start_epoch)
net.load_state_dict(torch.load(params_filename))
print('start epoch:', start_epoch)
print('load weight from: ', params_filename)
# train model
for epoch in range(start_epoch, epochs):
params_filename = os.path.join(params_path, 'epoch_%s.params' % epoch)
# evaluate_on_test_mstgcn(net, test_loader, test_target_tensor, sw, epoch, _mean, _std)
val_loss = compute_val_loss_astgcn_full(net, val_loader, criterion, None, None, sw, epoch)
if val_loss < best_val_loss:
best_val_loss = val_loss
best_epoch = epoch
torch.save(net.state_dict(), params_filename)
print('save parameters to file: %s' % params_filename)
net.train()
for batch_index, batch_data in enumerate(train_loader):
encoder_inputs, labels = batch_data
optimizer.zero_grad()
x1 = encoder_inputs[:, 0, :, :, :].squeeze(dim=1)
x2 = encoder_inputs[:, 1, :, :, :].squeeze(dim=1)
x3 = encoder_inputs[:, 2, :, :, :].squeeze(dim=1)
outputs = net(x1, x2, x3)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
training_loss = loss.item()
global_step += 1
sw.add_scalar('training_loss', training_loss, global_step)
if global_step % 1000 == 0:
print('global step: %s, training loss: %.2f, time: %.2fs' % (global_step, training_loss, time() - start_time))
print('best epoch:', best_epoch)
# apply the best model on the test set
predict_main(best_epoch, test_loader, test_target_tensor, _mean, _std, 'test')
def predict_main(global_step, data_loader, data_target_tensor, _mean, _std, _type):
'''
:param global_step: int
:param data_loader: torch.utils.data.utils.DataLoader
:param data_target_tensor: tensor
:param mean: (1, 1, 3, 1)
:param std: (1, 1, 3, 1)
:param type: string
:return:
'''
params_filename = os.path.join(params_path, 'epoch_%s.params' % global_step)
print('load weight from:', params_filename)
net.load_state_dict(torch.load(params_filename))
predict_and_save_results_astgcn_full(net, data_loader, data_target_tensor, global_step, None, _mean, _std, params_path, _type)
if __name__ == "__main__":
train_main()
# predict_main(224, test_loader, test_target_tensor, _mean, _std, 'test')