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exp_sea.py
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exp_sea.py
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import torch.nn.functional as F
import matplotlib.pyplot as plt
import matplotlib
from data_provider.sea_temperature_norm import InputHandle
matplotlib.use('Agg')
from timeit import default_timer
from utils.utilities3 import *
from utils.params import get_args
from model_dict import get_model
from utils.adam import Adam
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
import math
import os
torch.manual_seed(0)
np.random.seed(0)
torch.cuda.manual_seed(0)
torch.backends.cudnn.deterministic = True
################################################################
# configs
################################################################
args = get_args()
ntrain = args.ntrain
ntest = args.ntest
in_channels = args.in_dim
out_channels = args.out_dim
r1 = args.h_down
r2 = args.w_down
s1 = int(((args.h - 1) / r1) + 1)
s2 = int(((args.w - 1) / r2) + 1)
T_in = args.T_in
T_out = args.T_out
batch_size = args.batch_size
learning_rate = args.learning_rate
epochs = args.epochs
step_size = args.step_size
gamma = args.gamma
model_save_path = args.model_save_path
model_save_name = args.model_save_name
if args.anylearn == 1:
results_save_path = os.path.join(model_save_path.split('/')[0], 'results')
os.mkdir(results_save_path)
else:
results_save_path = '../results_temp'
os.makedirs(results_save_path, exist_ok=True)
################################################################
# models
################################################################
model = get_model(args)
print(count_params(model))
################################################################
# load data and data normalization
################################################################
train_params = {
'path': args.data_path,
'total_length': T_in+T_out,
'input_length': T_in,
'type': 'train'
}
test_params = {
'path': args.data_path,
'total_length': T_in+T_out,
'input_length': T_in,
'type': 'valid'
}
train_loader = DataLoader(InputHandle(train_params), batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=20)
test_loader = DataLoader(InputHandle(test_params), batch_size=args.batch_size, shuffle=False, drop_last=True, num_workers=20)
################################################################
# training and evaluation
################################################################
optimizer = Adam(model.parameters(), lr=learning_rate, weight_decay=1e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=gamma)
writer = SummaryWriter('results/logdir')
myloss = LpLoss(size_average=False)
train_iter = 0
step = 1
t1 = default_timer()
train_l2_step = 0
train_l2_full = 0
for ep in range(epochs):
for xx, yy, time_data, position in train_loader:
train_iter = train_iter + 1
loss = 0
xx = xx.to(device)
yy = yy.to(device)
time_data = time_data.to(device)
position = position.to(device)
for t in range(0, T_out, step):
# print(t)
y = yy[..., t:t + step]
if 'Helm' in args.model:
im, helm, vel = model(xx)
elif 'embed' in args.model:
im = model(xx)
else:
im = model(xx)
loss += myloss(im.reshape(batch_size, -1), y.reshape(batch_size, -1))
if t == 0:
pred = im
else:
pred = torch.cat((pred, im), -1)
xx = torch.cat((xx[..., step:], im), dim=-1)
train_l2_step += loss.item()
l2_full = myloss(pred.reshape(batch_size, -1), yy.reshape(batch_size, -1))
train_l2_full += l2_full.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
if train_iter % ntrain == 0:
t2 = default_timer()
print(train_iter, t2 - t1, train_l2_step / ntrain / (T_out / step), train_l2_full / ntrain)
t1 = default_timer()
writer.add_scalar('train_l2_step',
train_l2_step / ntrain / (T_out / step),
train_iter)
writer.add_scalar('train_l2_full',
train_l2_full / ntrain / (T_out / step),
train_iter)
train_l2_step = 0
train_l2_full = 0
scheduler.step()
if train_iter % ntest == 0:
print('Start testing...')
test_l2_step = 0
test_l2_full = 0
MSE_test = 0
save_path = os.path.join(results_save_path, str(train_iter))
os.mkdir(save_path)
with torch.no_grad():
sample = 0
for xx, yy, time_data, position in test_loader:
loss = 0
sample = sample + 1
xx = xx.to(device)
yy = yy.to(device)
time_data = time_data.to(device)
position = position.to(device)
if 'Helm' in args.model:
helm_list = []
vel_list = []
for t in range(0, T_out, step):
y = yy[..., t:t + step]
if 'Helm' in args.model:
im, helm, vel = model(xx)#, time_data[..., t:t+T_in], time_data[..., t+T_in:t+T_in+step], position)
helm_list.append(helm.detach().cpu().numpy())
vel_list.append(vel.detach().cpu().numpy())
elif 'embed' in args.model:
im = model(xx, time_data[..., t:t+T_in], time_data[..., t+T_in:t+T_in+step], position)
else:
im = model(xx)
loss += myloss(im.reshape(batch_size, -1), y.reshape(batch_size, -1))
if t == 0:
pred = im
else:
pred = torch.cat((pred, im), -1)
xx = torch.cat((xx[..., step:], im), dim=-1)
test_l2_step += loss.item()
yy = yy[..., :T_out]
test_l2_full += myloss(pred.reshape(batch_size, -1), yy.reshape(batch_size, -1)).item()
MSE_test += nn.MSELoss()(pred,yy).item()
if sample % 10 == 0 and sample <= 200:
X, Y = np.meshgrid(np.arange(0, yy.shape[-2], 1), np.arange(yy.shape[-3], 0, -1))
save_path_one = os.path.join(save_path, str(sample))
os.mkdir(save_path_one)
pred = pred.detach().cpu().numpy()
yy = yy.detach().cpu().numpy()
for t in range(T_out):
plt.imshow(pred[0, ..., t])
plt.colorbar()
plt.savefig(os.path.join(save_path_one, 'pd_{}.jpg'.format(str(100+t)[1:])))
plt.clf()
plt.imshow(yy[0, ..., t])
plt.colorbar()
plt.savefig(os.path.join(save_path_one, 'gt_{}.jpg'.format(str(100+t)[1:])))
plt.clf()
err = pred[0, ..., t] - yy[0, ..., t]
# m = max(abs(err.max()), abs(err.min()))
plt.imshow(err, cmap='coolwarm', vmax = 1, vmin = -1)
plt.colorbar()
plt.savefig(os.path.join(save_path_one, 'err_{}.jpg'.format(str(100+t)[1:])))
plt.clf()
if 'Helm' in args.model:
plt.imshow(helm_list[t][0,0])
plt.colorbar()
plt.savefig(os.path.join(save_path_one, 'phi_{}.jpg'.format(str(100+t)[1:])))
plt.clf()
plt.imshow(helm_list[t][0,1])
plt.colorbar()
plt.savefig(os.path.join(save_path_one, 'vorticity_{}.jpg'.format(str(100+t)[1:])))
plt.clf()
plt.imshow(yy[0, ..., t])
vel_draw = np.flip(vel_list[t][0], axis=-2)
vel_draw[1:] = -vel_draw[1:]
plt.quiver(X[::4, ::4] + 1.5, Y[::4, ::4] - 2, vel_draw[0, ::4, ::4],
vel_draw[1, ::4, ::4], scale_units='xy', scale=1)
plt.savefig(os.path.join(save_path_one, 'gt_flow_{}.jpg'.format(str(100+t)[1:])))
plt.clf()
model.train()
print(test_l2_step / sample / (T_out / step),
test_l2_full / sample, MSE_test / sample)
writer.add_scalar('test_l2_step',
test_l2_step / sample / (T_out / step),
train_iter)
writer.add_scalar('test_l2_full',
test_l2_full / sample,
train_iter)
writer.add_scalar('MSE_test',
MSE_test / sample,
train_iter)
if not os.path.exists(os.path.join(model_save_path,str(train_iter))):
os.makedirs(os.path.join(model_save_path,str(train_iter)))
print('save model')
torch.save(model.state_dict(), os.path.join(os.path.join(model_save_path,str(train_iter)), model_save_name))