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exp_smoke.py
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import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
from timeit import default_timer
from data_provider.smoke import SmokeDatasetMemory
from utils.utilities3 import *
from utils.params import get_args
from model_dict import get_model
from utils.adam import Adam
import math
from torch.utils.tensorboard import SummaryWriter
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
N = args.ntotal
in_channels = args.in_dim
out_channels = args.out_dim
r1 = args.z_down
r2 = args.h_down
r3 = args.w_down
s1 = int(((args.z - 1) / r1) + 1)
s2 = int(((args.h - 1) / r2) + 1)
s3 = int(((args.w - 1) / r3) + 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'
################################################################
# models
################################################################
model = get_model(args)
print(count_params(model))
################################################################
# load data and data normalization
################################################################
test_batch_size = 4
train_dataset = SmokeDatasetMemory(args, split='train')
test_dataset = SmokeDatasetMemory(args, split='test')# var='f'
train_loader = train_dataset.loader()
test_loader = test_dataset.loader()
torch_boundary = torch.ones(s1, s2, s3)
torch_boundary[1:-1, 1:-1, 1:-1] = 0
torch_boundary = 1.0 - torch_boundary.to(device).unsqueeze(0).float()
torch_mask = torch.ones(s1, s2, s3).to(device).unsqueeze(0).float()
################################################################
# 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)
step = 1
for ep in range(epochs):
model.train()
t1 = default_timer()
train_l2_step = 0
train_l2_full = 0
cnt = 0
for xx, yy in train_loader:
cnt += 1
if cnt % 10 == 0:
print(cnt*2)
loss = 0
train_batch_size = batch_size
train_T_out = T_out
xx = xx.to(device)
yy = yy.to(device)
for t in range(0, train_T_out, step):
# print(t)
y = yy[..., t:t + step]
if 'Helm' in args.model:
im, helm, vel = model(xx, torch_mask.repeat(xx.shape[0],1,1,1), torch_boundary.repeat(xx.shape[0],1,1,1))
else:
im = model(xx, torch_mask.repeat(xx.shape[0],1,1,1))
loss += myloss(im.reshape(xx.shape[0], -1), y.reshape(xx.shape[0], -1))
if t == 0:
pred = im.unsqueeze(-1)
else:
pred = torch.cat((pred, im.unsqueeze(-1)), -1)
xx = torch.cat((xx[..., step:], im.unsqueeze(-1)), dim=-1)
train_l2_step += loss.item()
l2_full = myloss(pred.reshape(xx.shape[0], -1), yy.reshape(xx.shape[0], -1))
train_l2_full += l2_full.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
# break
test_l2_step = 0
test_l2_full = 0
ep_save_path = os.path.join(results_save_path, str(ep))
with torch.no_grad():
sample = 0
for xx, yy in test_loader:
sample = sample + 1
loss = 0
xx = xx.to(device)
yy = yy.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, torch_mask.repeat(xx.shape[0], 1, 1, 1),
torch_boundary.repeat(xx.shape[0], 1, 1, 1))
helm_list.append(helm.detach().cpu().numpy())
vel_list.append(vel.detach().cpu().numpy())
else:
im = model(xx, torch_mask.repeat(xx.shape[0],1,1,1))
loss += myloss(im.reshape(xx.shape[0], -1), y.reshape(xx.shape[0], -1))
if t == 0:
pred = im.unsqueeze(-1)
else:
pred = torch.cat((pred, im.unsqueeze(-1)), -1)
xx = torch.cat((xx[..., step:], im.unsqueeze(-1)), dim=-1)
test_l2_step += loss.item()
test_l2_full += myloss(pred.reshape(xx.shape[0], -1), yy.reshape(xx.shape[0], -1)).item()
if ep % 5 == 0 and sample % 20 == 0:
X, Y = np.meshgrid(np.arange(0, yy.shape[-4], 1), np.arange(yy.shape[-3], 0, -1))
# print(yy.shape, X.shape, Y.shape, vel_list[0].shape)
save_path_one = os.path.join(ep_save_path, str(sample))
os.makedirs(save_path_one, exist_ok=True)
pred = pred.detach().cpu().numpy()
yy = yy.detach().cpu().numpy()
for t in range(T_out):
plt.imshow(pred[0, ..., 16, 0, t], vmax=1,vmin=0)
plt.colorbar()
plt.savefig(os.path.join(save_path_one, 'pd_{}.jpg'.format(str(100+t)[1:])))
plt.clf()
plt.imshow(yy[0, ..., 16, 0, t], vmax=1,vmin=0)
plt.colorbar()
plt.savefig(os.path.join(save_path_one, 'gt_{}.jpg'.format(str(100+t)[1:])))
plt.clf()
plt.imshow(pred[0, ..., 16, 0, t] - yy[0, ..., 16, 0, t], vmin=-0.5, vmax=0.5, cmap='coolwarm')
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, 8:-8, 8:-8, 24])
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, 3, 8:-8, 8:-8, 24])
plt.colorbar()
plt.savefig(os.path.join(save_path_one, 'vorticity_{}.jpg'.format(str(100+t)[1:])))
plt.clf()
vel_draw = np.flip(vel_list[t][0, :2, ..., 16], axis=-3)
vel_draw[1:] = -vel_draw[1:]
plt.imshow(yy[0, ..., 16, 0, t], vmax=1,vmin=0)
plt.quiver(X[::2, ::2] + 0.5, Y[::2, ::2] - 1, vel_draw[0, ::2, ::2],
vel_draw[1, ::2, ::2], scale_units='xy', scale=1)
plt.savefig(os.path.join(save_path_one, 'gt_flow_{}.jpg'.format(str(100+t)[1:])))
plt.clf()
t2 = default_timer()
scheduler.step()
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)
writer.add_scalar('train_l2_step',
train_l2_step / ntrain / (T_out / step),
ep)
writer.add_scalar('test_l2_step',
test_l2_step / ntest / (T_out / step),
ep)
writer.add_scalar('test_l2_full',
test_l2_full / ntest,
ep)
if ep % step_size == 0:
if not os.path.exists(model_save_path):
os.makedirs(model_save_path)
print('save model')
torch.save(model.state_dict(), os.path.join(model_save_path, model_save_name.format(ep)))