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test_case_burger.py
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test_case_burger.py
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import os
import time
import numpy as np
import torch
from scipy.interpolate import griddata
from torch.utils.tensorboard import SummaryWriter
import torch.optim as optim
# import torch.nn.functional as F
# from torch_geometric.nn import global_mean_pool
from torch_geometric.loader import DataLoader
from tqdm import tqdm
import matplotlib.pyplot as plt
import h5py
from utils import train_test_split, get_cur_time, initialize_model, initialize_dataset
def visualize_alpha(writer, model, epoch):
alphas = model.alpha
# alphas = np.array(alphas, dtype=np.float32)
num_order = len(alphas[1][0])
for i in range(num_order):
writer.add_histogram(f"Alpha Order {i}", alphas[1][:, i], epoch)
def visualize_coefficients(writer, model, epoch):
coefficients = model.coefficient[1]
# coefficients = coefficients.detach().cpu().numpy()
writer.add_histogram("Coefficients", coefficients, epoch)
def visualize_errors_by_layer(writer, model, epoch):
errors = model.errors
for i, error in enumerate(errors):
# error = error.detach().cpu().numpy()
writer.add_histogram(f"Error Layer {i}", error, epoch)
def visualize_clusters(writer, data, model, epoch):
clusters = model.cluster
# clusters = clusters.detach().cpu().numpy()
fig = plt.figure()
plt.scatter(data.pos[:, 0].detach().cpu().numpy(), data.pos[:, 1].detach().cpu().numpy(), c=clusters.detach().cpu().numpy(), cmap="viridis")
plt.colorbar()
plt.title(f"Clusters (Epoch: {epoch})")
writer.add_figure("Clusters", fig, epoch)
plt.close(fig)
def visualize_prediction(writer, data, model, epoch):
x, edge_index, edge_attr = data.x, data.edge_index, data.edge_attr
pred = model(x, edge_index, edge_attr).detach().cpu().numpy()
x = data.pos_high[:, 0].detach().cpu().numpy()
y = data.pos_high[:, 1].detach().cpu().numpy()
# x = data.pos[:, 0].detach().cpu().numpy()
# y = data.pos[:, 1].detach().cpu().numpy()
x_values = np.unique(x)
y_values = np.unique(y)
temp_grid = pred.squeeze().reshape(len(x_values), len(y_values))
fig = plt.figure(figsize=(8, 6))
plt.contourf(x_values, y_values, temp_grid, levels=np.linspace(0, 1, 100))
# plt.contourf(x_values, y_values, temp_grid)
plt.colorbar(label='Velocity Magnitude')
plt.title('Velocity Contour Plot')
plt.xlabel('x')
plt.ylabel('y')
writer.add_figure("Prediction", fig, epoch)
plt.close(fig)
temp_grid_true = data.y.cpu().detach().numpy().squeeze().reshape(len(x_values), len(y_values))
fig = plt.figure(figsize=(8, 6))
plt.contourf(x_values, y_values, temp_grid_true, levels=np.linspace(0, 1, 100))
# plt.contourf(x_values, y_values, temp_grid_true)
# limit the three figures to have the same colorbar
plt.colorbar(label='Velocity Magnitude')
plt.title('Velocity Contour Plot')
plt.xlabel('x')
plt.ylabel('y')
writer.add_figure("True", fig, epoch)
plt.close(fig)
temp_grid_error = np.abs(temp_grid - temp_grid_true)
fig = plt.figure(figsize=(8, 6))
plt.contourf(x_values, y_values, temp_grid_error, levels=np.linspace(0, 1, 100))
# plt.contourf(x_values, y_values, temp_grid_error)
plt.colorbar(label='Velocity Magnitude')
plt.title('Velocity Error Map')
plt.xlabel('x')
plt.ylabel('y')
writer.add_figure("Error", fig, epoch)
plt.close(fig)
x_low = data.pos[:, 0].detach().cpu().numpy()
y_low = data.pos[:, 1].detach().cpu().numpy()
x_values_low = np.unique(x_low)
y_values_low = np.unique(y_low)
# temp_grid_low = data.x.detach().cpu().numpy().squeeze().reshape(len(x_values_low), len(y_values_low))
temp_grid_low = data.x[:, 0].detach().cpu().numpy().squeeze().reshape(len(x_values), len(y_values))
fig = plt.figure(figsize=(8, 6))
# plt.contourf(x_values_low, y_values_low, temp_grid_low, levels=np.linspace(0, 1, 100), cmap="RdBu_r")
plt.contourf(x_values, y_values, temp_grid_low, levels=np.linspace(0, 1, 100))
# plt.contourf(x_values, y_values, temp_grid_low)
plt.colorbar(label='Velocity Magnitude')
plt.title('Velocity Contour Map')
plt.xlabel('x')
plt.ylabel('y')
writer.add_figure("Low Resolution", fig, epoch)
plt.close(fig)
def train():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = initialize_model(type='TEECNet', in_channel=1, width=16, out_channel=1, num_layers=3).to(device)
# model = initialize_model(type='NeuralOperator', in_channel=1, out_channel=1, width=64, ker_width=512, depth=6).to(device)
print('The model has {} parameters'.format(sum(p.numel() for p in model.parameters() if p.requires_grad)))
optimizer = optim.Adam(model.parameters(), lr=0.0001, weight_decay=5e-4)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.5)
dataset = initialize_dataset(dataset='BurgersDataset', root='dataset/burger', res_low=0, res_high=1, pre_transform='interpolate_high')
train_dataset, test_dataset = train_test_split(dataset, 0.8)
train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=8, shuffle=False)
sim_start_time = get_cur_time()
writer = SummaryWriter('runs/burger/CFDError/{}'.format(sim_start_time))
os.makedirs('test_cases/burger/CFDError/{}'.format(sim_start_time), exist_ok=True)
t1 = time.time()
for epoch in range(5000):
model.train()
loss_all = 0
# i_sample = 0
for data in train_loader:
# model.train()
# i_sample += 1
# if i_sample > 200:
# break
data = data.to(device)
x, edge_index, edge_attr = data.x, data.edge_index, data.edge_attr
optimizer.zero_grad()
out = model(x, edge_index, edge_attr)
# if data.y.dim() == 1:
# data.y = data.y.unsqueeze(-1)
loss = torch.nn.functional.mse_loss(out, data.y)
# r2_accuracy = r2_score(data.y.cpu().detach().numpy(), out.cpu().detach().numpy())
loss.backward()
loss_all += loss.item()
optimizer.step()
# following code evaluates the model performance with each training sample
# if i_sample in [1, 2, 5, 50, 200]:
# model.eval()
# with torch.no_grad():
# data = test_dataset[np.random.randint(len(test_dataset))]
# data = data.to(device)
# out = model(data)
# if data.y.dim() == 1:
# data.y = data.y.unsqueeze(-1)
# loss = torch.nn.functional.mse_loss(out, data.y)
# r2_accuracy = r2_score(data.y.cpu().detach().numpy(), out.cpu().detach().numpy())
# writer.add_scalar('Loss/test', loss, i_sample)
# writer.add_scalar('R2 Accuracy/test', r2_accuracy, i_sample)
# visualize_prediction(writer, data, model, i_sample)
# visualize_alpha(writer, model, i_sample)
scheduler.step()
writer.add_scalar('Loss/train', loss_all / len(train_loader), epoch)
# visualize_alpha(writer, model, epoch)
# visualize_coefficients(writer, model, epoch)
# visualize_clusters(writer, data, model, epoch)
# visualize_errors_by_layer(writer, model, epoch)
visualize_prediction(writer, data[0], model, epoch)
print('Epoch: {:02d}, Loss: {:.4f}'.format(epoch, loss_all / len(train_loader)))
if epoch % 10 == 0:
model.eval()
loss_all = 0
for data in test_loader:
data = data.to(device)
x, edge_index, edge_attr = data.x, data.edge_index, data.edge_attr
out = model(x, edge_index, edge_attr)
if data.y.dim() == 1:
data.y = data.y.unsqueeze(-1)
loss = torch.nn.functional.mse_loss(out, data.y)
loss_all += loss.item()
writer.add_scalar('Loss/test', loss_all / len(test_loader), epoch)
torch.save(model.state_dict(), 'test_cases/burger/CFDError/{}/model_{}.pt'.format(sim_start_time, epoch))
print('Epoch: {:02d}, Loss: {:.4f}'.format(epoch, loss_all / len(test_loader)))
t2 = time.time()
print('Training time: {:.4f} s'.format(t2 - t1))
torch.save(model.state_dict(), 'test_cases/burger/CFDError/{}/model.pt'.format(sim_start_time))
writer.close()
if __name__ == '__main__':
train()