def main(config):
device = None
if config['use_cuda']:
device = torch.device(config['cuda_num'])
print(">>> load train, valid, test dataloader <<<")
train_dataloader, valid_dataloader, test_dataloader = data_loader.load_path_loss_with_detail_dataset(
input_dir=config['input_dir'],
model_type=config['model_type'],
num_workers=config['num_workers'],
batch_size=config['batch_size'],
shuffle=config['shuffle'],
input_size=config['sequence_length'])
print(">>> setup tensorboard <<<")
writer = use_tensorboard.set_tensorboard_writer('{}/{}'.format(
config['tensorboard_writer_path'], config['section_message']))
print(">>> model load <<<")
model = model_reconst.model_load(model_configure=config)
print(">>> loss, optimizer setup <<<")
criterion = optimizer.set_criterion(config['criterion'])
optim = optimizer.set_optimizer(config['optimizer'],
model=model,
learning_rate=config['learning_rate'])
num_of_epoch = config['epoch']
for epoch in range(num_of_epoch):
print("start training ... [ {}/{} epoch ]".format(epoch, num_of_epoch))
train(train_loader=train_dataloader,
epoch=epoch,
config=config,
device=device,
model=model,
criterion=criterion,
writer=writer,
optim=optim)
validation(valid_loader=valid_dataloader,
epoch=epoch,
config=config,
device=device,
model=model,
criterion=criterion,
writer=writer)
torch.save(
{
"epoch": epoch,
"model": model,
"model_state_dict": model.state_dict()
}, "{}/{}_epoch_{}.pt".format(config['checkpoint_dir'],
config['section_message'], epoch))
print(data.shape)
x_data = data.transpose(1, 2)
x_data = x_data.cuda()
y_pred = nn_model(x_data).reshape(-1).cpu().numpy()
total_pred.extend(y_pred)
total_pred = np.array(total_pred)
print('mean : ', total_pred.mean())
print('median : ', np.median(total_pred))
print('max : ', total_pred.max())
print('min : ', total_pred.min())
return {'mean': total_pred.mean(), 'median': np.median(total_pred),
'max': total_pred.max(), 'min': total_pred.min()}
if __name__ == '__main__':
inference_model = model_reconst.model_load(model_config)
pole_line_count = 2
height = 30
width = 50
pole_point = []
circles = []
for i in range(pole_line_count):
pole_point.append([i*height, 0])
pole_point.append([i*height, width])
pole_data_path = [
"dataset/v8/test_dataset01/test1_type01_point_f8-8a-5e-45-75-c5_39_15_10/f8-8a-5e-45-75-c5_39_15_10_pol0-0.csv",
# pole1
"dataset/v8/test_dataset01/test1_type01_point_f8-8a-5e-45-75-c5_39_15_10/f8-8a-5e-45-75-c5_39_15_10_pol50-0.csv",
# pole2
"dataset/v8/test_dataset01/test1_type01_point_f8-8a-5e-45-75-c5_39_15_10/f8-8a-5e-45-75-c5_39_15_10_pol0-30.csv",
# pole3