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main.py
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main.py
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import argparse
import json
import os
import shutil
import h5py
import torch
import yaml
import torch.nn as nn
import torch
from models import create_model
from utils.train import train_model, train_baseline, test_model, test_baseline
from utils.util import get_optimizer, get_loss, get_scheduler
from utils.data_container import get_data_loader, get_data_loader_base
from utils.preprocess import preprocessing, preprocessing_for_metric
# 250, 266
def train(conf, data_category):
print(json.dumps(conf, indent=4))
os.environ["CUDA_VISIBLE_DEVICES"] = str(conf['device'])
device = torch.device(0)
model_name = conf['model']['name']
optimizer_name = conf['optimizer']['name']
data_set = conf['data']['dataset']
graph = h5py.File(os.path.join('data', data_set, 'all_graph.h5'), 'r')
scheduler_name = conf['scheduler']['name']
loss = get_loss(**conf['loss'])
# data_category = conf['data']['data_category']
loss.to(device)
encoder, decoder, support = None, None, None
if model_name == 'Costnet':
base_model_name = conf['Base']['name']
encoder, decoder = preprocessing(base_model_name, conf, loss, graph, data_category, device, data_set,
optimizer_name, scheduler_name)
if model_name == 'Metricnet' or model_name == 'GWNET' or model_name == 'Evonet' or model_name == 'STGCN' or model_name == 'DCRNN' or model_name == 'STG2Seq' or model_name == 'Evonet2':
support = preprocessing_for_metric(data_category=data_category, dataset=conf['data']['dataset'],
Normal_Method=conf['data']['Normal_Method'], _len=conf['data']['_len'], **conf['preprocess'])
model, trainer = create_model(model_name,
loss,
conf['model'][model_name],
data_category,
device,
graph,
encoder,
decoder,
support)
optimizer = get_optimizer(optimizer_name, model.parameters(), conf['optimizer'][optimizer_name]['lr'])
scheduler = get_scheduler(scheduler_name, optimizer, **conf['scheduler'][scheduler_name])
if torch.cuda.device_count() > 1:
print("use ", torch.cuda.device_count(), "GPUS")
model = nn.DataParallel(model)
else:
model.to(device)
save_folder = os.path.join('save', conf['name'], f'{data_set}_{"".join(data_category)}', conf['tag'])
run_folder = os.path.join('run', conf['name'], f'{data_set}_{"".join(data_category)}', conf['tag'])
shutil.rmtree(save_folder, ignore_errors=True)
os.makedirs(save_folder)
shutil.rmtree(run_folder, ignore_errors=True)
os.makedirs(run_folder)
with open(os.path.join(save_folder, 'config.yaml'), 'w+') as _f:
yaml.safe_dump(conf, _f)
data_loader, normal = get_data_loader(**conf['data'], data_category=data_category, device=device,
model_name=model_name)
if len(data_category) == 2:
train_model(model=model,
dataloaders=data_loader,
trainer=trainer,
node_num=conf['node_num'],
loss_func=loss,
optimizer=optimizer,
normal=normal,
scheduler=scheduler,
folder=save_folder,
tensorboard_folder=run_folder,
device=device,
**conf['train'])
# test_model(folder = save_folder)
else:
train_baseline(model=model,
dataloaders=data_loader,
trainer=trainer,
optimizer=optimizer,
normal=normal,
scheduler=scheduler,
folder=save_folder,
tensorboard_folder=run_folder,
device=device,
**conf['train'])
test_baseline(folder=save_folder,
trainer=trainer,
model=model,
normal=normal,
dataloaders=data_loader,
device=device)
if __name__ == '__main__':
# parser = argparse.ArgumentParser()
# parser.add_argument('--config', required=True, type=str,
# help='Configuration filename for restoring the model.')
# parser.add_argument('--resume', required=False, type=bool, default=False,
# help='Resume.')
# parser.add_argument('--test', required=False, type=bool, default=False,
# help='Test.')
#
# args = parser.parse_args()
con = 'evoconv2-config'
data = ['taxi']
with open(os.path.join('config', f'{con}.yaml')) as f:
conf = yaml.safe_load(f)
train(conf, data)