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playground_paper.py
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import torch
from tool import use_tensorboard
import tool.optimizer as optimizer
import data.data_loader_scaler as data_loader
import tool.metrics as metrics
from model_pack import model_paper
import numpy as np
import matplotlib.pyplot as plt
def main(config):
device = None
if config['use_cuda']:
device = torch.device(config['cuda_num'])
print(">>> Load train, valid dataset <<<")
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_paper.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))
def train(train_loader, epoch, config, device, model, criterion, writer, optim):
for batch_idx, data in enumerate(train_loader):
x_data = data[:][0].transpose(1, 2)
y_data = data[:][1]
if config['use_cuda']:
x_data = x_data.to(device)
y_data = y_data.to(device)
# 모델 예측 진행
y_pred = model(x_data).reshape(-1)
# 예측결과에 대한 Loss 계산
loss = criterion(y_pred, y_data)
# 역전파 수행
optim.zero_grad()
loss.backward()
optim.step()
writer.add_scalar("Loss/Train MSELoss", loss/1000, epoch * len(train_loader) + batch_idx)
y_pred = y_pred.cpu().detach().numpy()
y_data = y_data.cpu().detach().numpy()
mse_score = metrics.mean_squared_error(y_data, y_pred)
r2_score = metrics.r2_score(y_data, y_pred)
mae_score = metrics.mean_absolute_error(y_data, y_pred)
rmse_score = np.sqrt(mse_score)
mape_score = metrics.mean_absolute_percentage_error(y_data, y_pred)
writer.add_scalar('MSE Score/train', mse_score, epoch * len(train_loader) + batch_idx)
writer.add_scalar('R2 Score/train', r2_score, epoch * len(train_loader) + batch_idx)
writer.add_scalar('MAE Score/train', mae_score, epoch * len(train_loader) + batch_idx)
writer.add_scalar('RMSE Score/train', rmse_score, epoch * len(train_loader) + batch_idx)
writer.add_scalar('MAPE Score/train', mape_score, epoch * len(train_loader) + batch_idx)
def validation(valid_loader, epoch, config, device, model, criterion, writer):
with torch.no_grad():
total_label = []
total_pred = []
total_x = []
for batch_idx, data in enumerate(valid_loader):
x_data = data[:][0].transpose(1, 2)
y_data = data[:][1]
if config['use_cuda']:
x_data = x_data.to(device)
y_data = y_data.to(device)
# 모델 예측 진행
y_pred = model(x_data).reshape(-1)
# 예측결과에 대한 Loss 계산
loss = criterion(y_pred, y_data)
writer.add_scalar('Loss/Validation MSELoss', loss / 1000, epoch * len(valid_loader) + batch_idx)
y_pred = y_pred.cpu()
for temp in data[:][0]:
x = temp[:, 0].cpu()
total_x.append(np.array(x.tolist()).mean())
total_label += y_data.tolist()
total_pred += y_pred.tolist()
mse_score = metrics.mean_squared_error(total_label, total_pred)
r2_score = metrics.r2_score(total_label, total_pred)
mae_score = metrics.mean_absolute_error(total_label, total_pred)
rmse_score = np.sqrt(mse_score)
mape_score = metrics.mean_absolute_percentage_error(total_label, total_pred)
writer.add_scalar('MSE Score/Validation', mse_score, epoch)
writer.add_scalar('R2 Score/Validation', r2_score, epoch)
writer.add_scalar('MAE Score/Validation', mae_score, epoch)
writer.add_scalar('RMSE Score/Validation', rmse_score, epoch)
writer.add_scalar('MAPE Score/Validation', mape_score, epoch)
fig = plt.figure(figsize=(24, 16))
plt.scatter(total_x, total_pred, color='blue', alpha=0.2, label='prediction')
plt.scatter(total_x, total_label, color='red', alpha=0.2, label='groundtruth')
plt.legend()
plt.grid(True)
plt.xlabel("rssi (dbm)")
plt.ylabel("distance (meter)")
plt.title("Prediction Result")
plt.yticks(np.arange(0, 70, 5))
writer.add_figure('PathLoss Prediction', fig, epoch)
data_size = int(len(total_x) / 16)
fig_detail = plt.figure(figsize=(16, 16))
plt.subplots(constrained_layout=True)
for i in range(16):
plt.subplot(4, 4, 1 + i)
if i < 15:
plt.scatter(total_x[data_size * i: data_size * (i + 1)], total_pred[data_size * i: data_size * (i + 1)], color='blue', alpha=0.2, label='prediction')
plt.scatter(total_x[data_size * i: data_size * (i + 1)], total_label[data_size * i: data_size * (i + 1)], color='red', alpha=0.2, label='groundtruth')
plt.legend()
plt.grid(True)
plt.xlabel("rssi (dbm)")
plt.ylabel("distance (meter)")
plt.yticks(np.arange(0, 70, 5))
plt.title("PathLoss Prediction with Detail")
else:
plt.scatter(total_x[data_size * i:], total_pred[data_size * i:], color='blue', alpha=0.2, label='prediction')
plt.scatter(total_x[data_size * i:], total_label[data_size * i:], color='red', alpha=0.2, label='groundtruth')
plt.legend()
plt.grid(True)
plt.xlabel("rssi (dbm)")
plt.ylabel("distance (meter)")
plt.yticks(np.arange(0, 70, 5))
plt.title("PathLoss Prediction with Detail")
plt.subplots(constrained_layout=True)
writer.add_figure('VisualizeValidationDetail', fig_detail, epoch)
if __name__ == '__main__':
configuration = {
}
print(">>> Training Bluetooth PathLoss <<<")
print("train configuration ->->->")
print(configuration)
main(config=configuration)