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exp_sea_h_vortex.py
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import os
from timeit import default_timer
from datetime import datetime, timedelta
import numpy as np
import matplotlib.pyplot as plt
from utils.data_factory import SeaDataset, SeaDatasetMemory
from utils.utilities3 import *
from utils.params import get_args
from utils.adam import Adam
from model_dict import get_model
from tqdm import tqdm
time_str = (datetime.now()).strftime("%Y%m%d_%H%M%S")
torch.manual_seed(0)
np.random.seed(0)
torch.cuda.manual_seed(0)
torch.backends.cudnn.deterministic = True
################################################################
# configs
################################################################
args = get_args(time=time_str)
padding = [int(p) for p in args.padding.split(',')]
ntrain = args.ntrain
ntest = args.ntest
N = args.ntotal
args.in_channels = args.in_dim * args.in_var
args.out_channels = args.out_dim * args.out_var
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
patch_size = tuple(int(x) for x in args.patch_size.split(','))
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
################################################################
# models
################################################################
model = get_model(args)
################################################################
# load data and data normalization
################################################################
train_dataset = SeaDatasetMemory(args, region=args.region, split='train', return_idx=True)
test_dataset = SeaDatasetMemory(args, region=args.region, split='test', return_idx=True)
train_loader = train_dataset.loader()
test_loader = test_dataset.loader()
land, sea = get_land_sea_mask(args.data_path, args.fill_value)
################################################################
# training and evaluation
################################################################
optimizer = Adam(model.parameters(), lr=learning_rate, weight_decay=1e-4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
myloss = LpLoss(size_average=False, channel_wise=False)
step = 1
min_test_l2_full = 114514
for ep in range(epochs):
model.train()
t1 = default_timer()
train_l2_step = 0
train_l2_full = 0
for batch_idx, (index, xx, yy) in enumerate(tqdm(train_loader)):
index = index.to(device) * 0.01
loss = 0
xx = xx.to(device)
yy = yy.to(device)
for t in range(0, T_out, step):
y = yy[..., t*args.out_var : (t + step)*args.out_var]
im, vel_loss = model(xx, index)
# print(xx.shape, y.shape)
loss += nn.MSELoss().cuda()(im, y)
loss += vel_loss
if t == 0:
pred = im
else:
pred = torch.cat((pred, im), -1)
xx = torch.cat((xx[..., step*args.in_var:], 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()
t2 = default_timer()
test_l2_step = 0
test_l2_full = 0
with torch.no_grad():
for batch_idx, (index, xx, yy) in enumerate(test_loader):
index = index.to(device) * 0.01
loss = 0
xx = xx.to(device)
yy = yy.to(device)
for t in range(0, T_out, step):
y = yy[..., t*args.out_var : (t + step)*args.out_var]
im, _ = model(xx, index)
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*args.in_var:], im), dim=-1)
test_l2_step += loss.item()
test_l2_full += myloss(pred.reshape(batch_size, -1), yy.reshape(batch_size, -1)).item()
scheduler.step()
if test_l2_full / ntest < min_test_l2_full:
min_test_l2_full = test_l2_full / ntest
print(ep, t2 - t1, train_l2_step / ntrain / (T_out / step), train_l2_full / ntrain,
test_l2_step / ntest / (T_out / step),
test_l2_full / ntest, 'new_best!')
print('save best model')
torch.save(model.state_dict(), os.path.join(args.run_save_path, model_save_name[:-3]+f'_best.pt'))
pd = pred[-1, :, :, -5:].detach().cpu().numpy()
gt = yy[-1, :, :, -5:].detach().cpu().numpy()
vars = ['thetao', 'so', 'uo', 'vo', 'zos']
for i in range(5):
visual(pd[...,i], os.path.join(args.run_save_path, f'best_{vars[i]}_pred.png'))
visual(gt[...,i], os.path.join(args.run_save_path, f'best_{vars[i]}_gt.png'))
visual(np.abs(gt-pd)[...,i], os.path.join(args.run_save_path, f'best_{vars[i]}_err.png'))
else:
print(ep, t2 - t1, train_l2_step / ntrain / (T_out / step), train_l2_full / ntrain,
test_l2_step / ntest / (T_out / step),
test_l2_full / ntest)
if ep % 10 == 0:
# if not os.path.exists(model_save_path):
# os.makedirs(model_save_path)
print('save latest model')
torch.save(model.state_dict(), os.path.join(args.run_save_path, model_save_name[:-3]+f'_latest.pt'))
if ep % 100 == 0:
pd = pred[-1, :, :, -5:].detach().cpu().numpy()
gt = yy[-1, :, :, -5:].detach().cpu().numpy()
vars = ['thetao', 'so', 'uo', 'vo', 'zos']
for i in range(5):
visual(pd[...,i], os.path.join(args.run_save_path, f'ep_{ep}_{vars[i]}_pred.png'))
visual(gt[...,i], os.path.join(args.run_save_path, f'ep_{ep}_{vars[i]}_gt.png'))
visual(np.abs(gt-pd)[...,i], os.path.join(args.run_save_path, f'ep_{ep}_{vars[i]}_err.png'))