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ex4_SP-II_darcy2D.py
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import os
from torch.utils.data import DataLoader
from timeit import default_timer
from utilities import *
from copy import deepcopy
import h5py
from scipy.io import loadmat
import fourierpack as sp
import functools
import matplotlib
device = torch.device("cuda")
data_name = 'darcy-bench'
from NOs_dict.models import SinNO_II as Model
#### fixing seeds
torch.manual_seed(0)
np.random.seed(0)
torch.cuda.manual_seed(0)
torch.backends.cudnn.deterministic = True
from utilities import get_args
import argparse
def get_args():
parser = argparse.ArgumentParser('Spectral Operator Learning', add_help=False)
parser.add_argument('--data-dict', default='data/, type=str, help='dataset folder')
parser.add_argument('--data-para', default='100.0', type=str, help='dataset parameter beta')
parser.add_argument('--epochs', default=500, type=int, help='training iterations')
parser.add_argument('--sub', default=1, type=int, help='sub-sample on the data')
parser.add_argument('--lr', default=4e-3, type=float, help='learning rate')
parser.add_argument('--bw', default=1, type=int, help='band width')
parser.add_argument('--width', default=32, type=int, help='width')
parser.add_argument('--batch-size', default=20, type=int, help='batch size')
parser.add_argument('--step-size', default=100, type=int, help='step size for the StepLR (if used)')
parser.add_argument('--modes', default=24, type=int, help='Fourier-like modes')
parser.add_argument('--triL', default=0, type=int, help='')
parser.add_argument('--sol-skipflag', default=0, type=int, help='')
parser.add_argument('--suffix', default='', type=str, help='')
return parser.parse_args()
#### parameters settings
args = get_args()
epochs = args.epochs # default 500
step_size = args.step_size # for StepLR, default 50
batch_size = args.batch_size # default 100
sub = args.sub # default 1
learning_rate = args.lr # default 1e-3
bandwidth = args.bw # default 1
modes = args.modes
triL = args.triL
sol_skipflag = args.sol_skipflag
suffix = args.suffix
width = args.width
suffix += '-plat'
print('!! Using FCT-II')
suffix += '-FCT-II'
gamma = 0.5 # for StepLR
weight_decay = 1e-6
wd = -6
train_size, test_size = 9000, 1000
num_workers = 0
beta = args.data_para
data_PATH = args.data_dict + '2D_DarcyFlow_beta' + beta + '_Train.hdf5'
file_name = 'sp-' + data_name + str(sub) + '-beta' + beta + '-modes' + str(modes) + '-width' + str(width) \
+ '-bw' + str(bandwidth)+ '-triL' + str(triL) + '-wd' + str(wd) + suffix
result_PATH = 'model/' + file_name + '.pkl'
print('data:', data_PATH)
print('result_PATH:', result_PATH)
print('batch_size', batch_size, 'learning_rate', learning_rate, 'epochs', epochs, 'bandwidth', bandwidth)
print('weight_decay', weight_decay, 'width', width, 'modes', modes, 'sub', sub, 'triL', triL)
import os
if os.path.exists(result_PATH):
print("----------Warning: pre-trained model already exists:")
print(result_PATH)
## main
## model
model = Model(3, modes, width, bandwidth, dim=2, triL=triL, skip=sol_skipflag).to(device)
ntrain, ntest = train_size, test_size
raw_data = h5py.File(data_PATH, 'r')
# pad BC
raw_num, raw_x, raw_y = raw_data['nu'].shape
x_data, y_data = raw_data['nu'], raw_data['tensor'][:, 0, ...]
x_train, x_test = torch.from_numpy(x_data[:train_size, ...]), torch.from_numpy(x_data[-test_size:, ...])
y_train, y_test = torch.from_numpy(y_data[:train_size, ...]), torch.from_numpy(y_data[-test_size:, ...])
_, Nx, Ny = x_train.shape
s = Nx
x_train = x_train.reshape(ntrain, s, s, 1)
x_test = x_test.reshape(ntest, s, s, 1)
grid_x, grid_y = np.array(raw_data['x-coordinate']), \
np.array(raw_data['y-coordinate'])
grid_x = torch.from_numpy(grid_x).view(1, Nx, 1, 1)
grid_y = torch.from_numpy(grid_y).view(1, 1, Ny, 1)
x_train = torch.cat([x_train.view(ntrain, Nx, Ny, 1)
, grid_x.repeat(ntrain, 1, Ny, 1), grid_y.repeat(ntrain, Nx, 1, 1)], dim=-1).type(
torch.float32)
x_test = torch.cat([x_test.view(ntest, Nx, Ny, 1)
, grid_x.repeat(ntest, 1, Ny, 1), grid_y.repeat(ntest, Nx, 1, 1)], dim=-1).type(
torch.float32)
train_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_train, y_train),
batch_size=batch_size,
num_workers=num_workers,
shuffle=True)
test_loader = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(x_test, y_test), batch_size=batch_size,
num_workers=num_workers,
shuffle=False)
if epochs == 0: # load model
print('model:' + result_PATH + ' loaded!')
loader = torch.load(result_PATH)
model.load_state_dict(loader['model'])
print('test_l2:', loader['loss_list'][-1])
print('model parameters number =', count_params(model))
# optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
from Adam import Adam
optimizer = Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
# scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, threshold=1e-1, patience=50, verbose=True)
train_list, loss_list = [], []
t1 = default_timer()
myloss = LpLoss(size_average=False)
for ep in range(epochs):
model.train()
t1 = default_timer()
train_mse = 0
train_l2 = 0
for x, y in train_loader:
x, y = x.to(device), y.to(device)
optimizer.zero_grad()
out = model(x).reshape(batch_size, s, s)
mse = F.mse_loss(out.reshape(batch_size, -1), y.view(batch_size, -1), reduction='mean')
l2 = myloss(out.reshape(batch_size, -1), y.view(batch_size, -1))
l2.backward()
optimizer.step()
train_mse += mse.item()
train_l2 += l2.item()
# scheduler.step()
model.eval()
test_l2 = 0.0
with torch.no_grad():
for x, y in test_loader:
x, y = x.to(device), y.to(device)
out = model(x).reshape(batch_size, s, s)
test_l2 += myloss(out.reshape(batch_size, -1), y.reshape(batch_size, -1)).item()
train_l2 /= ntrain
test_l2 /= ntest
scheduler.step(train_l2)
train_list.append(train_l2)
loss_list.append(test_l2)
t2 = default_timer()
if (ep + 1) % 100 == 0 or ep <= 20:
print(ep, str(t2 - t1)[:4], optimizer.state_dict()['param_groups'][0]['lr'], \
train_mse, train_l2, test_l2)
if epochs >= 500:
torch.save({
'model': model.state_dict(), 'batch_size': batch_size, 'learning_rate': learning_rate, 'epochs': epochs,
'weight_decay': weight_decay, 'width': width, 'modes': modes, 'sub': sub,
'loss_list': loss_list, 'train_list': train_list
}, result_PATH)
j = -1
yy = model(x_test[:100, ...].to(device)).detach().cpu()[..., 0]
x_unif = torch.linspace(0, 1, Nx)
X, Y = torch.meshgrid(x_unif, x_unif)
peer_loss = LpLoss(reduction=False)
test_err = torch.tensor([], device=device)
with torch.no_grad():
for x, y in test_loader:
x, y = x.to(device), y.to(device)
out = model(x).reshape(batch_size, s, s)
test_err = torch.cat([test_err,
peer_loss(out.reshape(batch_size, -1), y.reshape(batch_size, -1))],
dim=0)
print('test_l2', test_err.sum().item() / test_size)
print('test_l2 min-max:', test_err.min().item(), test_err.max().item())
peer_loss = LpLoss(reduction=False)
test_err = peer_loss(yy.view(y_test[:100, ...].shape[0], -1), y_test[:100, ...].view(y_test[:100, ...].shape[0], -1))
# print(test_err)
plt.cla()
j += 1
fig, axs = plt.subplots(2, 2, num=0, clear=True)
im = axs[0][0].contourf(X, Y, y_test[j, ...], cmap=plt.get_cmap('Spectral'))
fig.colorbar(im, ax=axs[0, 0])
im = axs[0][1].contourf(X, Y, yy[j, ...], cmap=plt.get_cmap('Spectral'))
fig.colorbar(im, ax=axs[0, 1])
im = axs[1][0].contourf(X, Y, yy[j, ...] - y_test[j, ...], cmap=plt.get_cmap('Spectral'))
fig.colorbar(im, ax=axs[1, 0])
im = axs[1][1].contourf(X, Y, x_test[j, ..., 0], cmap=plt.get_cmap('Spectral'))
fig.colorbar(im, ax=axs[1, 1])
axs[0][0].set_axis_off()
axs[0][1].set_axis_off()
axs[1][0].set_axis_off()
axs[1][1].set_axis_off()
plt.show()
#
plt.cla()
j += 1
fig, axs = plt.subplots(2, 2, num=0, clear=True)
im = axs[0, 0].imshow(y_test[j, ...])
fig.colorbar(im, ax=axs[0, 0])
im = axs[0, 1].imshow(yy[j, ...])
fig.colorbar(im, ax=axs[0, 1])
im = axs[1, 0].imshow(yy[j, ...] - y_test[j, ...])
fig.colorbar(im, ax=axs[1, 0])
im = axs[1, 1].imshow(x_test[j, ..., 0])
fig.colorbar(im, ax=axs[1, 1])
axs[0][0].set_axis_off()
axs[0][1].set_axis_off()
axs[1][0].set_axis_off()
axs[1][1].set_axis_off()
plt.show()
#
# plt.cla()
# j += 1
# fig, axs = plt.subplots(2, 3, num=0, clear=True)
# im = axs[0][0].contourf(X, Y, y_test[j, ...], cmap=plt.get_cmap('Spectral'))
# fig.colorbar(im, ax=axs[0, 0])
# im = axs[0][1].contourf(X, Y, yy[j, ...], cmap=plt.get_cmap('Spectral'))
# fig.colorbar(im, ax=axs[0, 1])
# im = axs[1][0].contourf(X, Y, yy[j, ...] - y_test[j, ...], cmap=plt.get_cmap('Spectral'))
# fig.colorbar(im, ax=axs[1, 0])
# im = axs[1][1].contourf(X, Y, x_test[j, ..., 1], cmap=plt.get_cmap('Spectral'))
# fig.colorbar(im, ax=axs[1, 1])
# im = axs[1][2].contourf(X, Y, x_test[j, ..., 0], cmap=plt.get_cmap('Spectral'))
# fig.colorbar(im, ax=axs[1, 2])
# axs[0][0].set_axis_off()
# axs[0][1].set_axis_off()
# axs[1][0].set_axis_off()
# axs[1][1].set_axis_off()
# plt.show()