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test_bc_h.py
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import os
from pathlib import Path
import logging
import pickle
from timeit import default_timer
from datetime import datetime, timedelta
import numpy as np
import matplotlib.pyplot as plt
from utils.data_factory import get_bc_dataset, BoundedNSDataset
from utils.utilities3 import *
from utils.params import get_args, get_test_args
from utils.adam import Adam
from model_dict import get_model
from tqdm import tqdm
torch.manual_seed(0)
np.random.seed(0)
torch.cuda.manual_seed(0)
torch.backends.cudnn.deterministic = True
################################################################
# configs
################################################################
test_args = get_test_args()
ckpt_dir = test_args.ckpt_dir
dataset_nickname = test_args.dataset_nickname
model_name = test_args.model_name
time_str = test_args.time_str
milestone = test_args.milestone
T_out = test_args.T_out
args = get_args(cfg_file=Path(ckpt_dir)/dataset_nickname/model_name/time_str/'configs.txt')
test_save_path = os.path.join(args.run_save_path, f'test_{milestone}_{T_out}')
if not os.path.isdir(test_save_path):
os.makedirs(test_save_path)
LOG_FORMAT = "%(message)s"
logger = logging.getLogger('Loss logger')
logger.setLevel(logging.INFO)
f_handler = logging.FileHandler(os.path.join(test_save_path, args.log_save_name))
f_handler.setLevel(logging.INFO)
f_handler.setFormatter(logging.Formatter(LOG_FORMAT))
logger.addHandler(f_handler)
TRAIN_PATH = os.path.join(test_args.data_path, 're0.25_4c_gray_10000.npy')
TEST_PATH = os.path.join(test_args.data_path, 're0.25_4c_gray_10000.npy')
BOUNDARY_PATH = os.path.join(test_args.data_path, 'boundary_4c_rot.npy')
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, ckpt_dir=Path(ckpt_dir)/dataset_nickname/model_name/time_str)
state_dict = torch.load(Path(ckpt_dir)/dataset_nickname/model_name/time_str/ (model_save_name[:-3]+f'_{milestone}.pt'))
model.load_state_dict(state_dict)
################################################################
# load data and data normalization
################################################################
train_dataset = BoundedNSDataset(args, dataset_file=TRAIN_PATH, split='train')
test_dataset = BoundedNSDataset(args, dataset_file=TEST_PATH, split='test')
train_loader = train_dataset.loader()
test_loader = test_dataset.loader()
boundary, domain = process_boundary_condition(BOUNDARY_PATH, ds_rate=(r1,r2))
if 'DeepLag' in args.model:
model.set_bdydom(boundary, domain)
if args.resample_strategy == 'uniform' or args.resample_strategy == 'learned':
model.num_samples = min(model.num_samples, s1*s2)
elif args.resample_strategy == 'boundary':
model.num_samples = min(model.num_samples, model.coo_boundary_ms[0].shape[0])
elif args.resample_strategy == 'domain':
model.num_samples = min(model.num_samples, model.coo_domain_ms[0].shape[0])
################################################################
# evaluation
################################################################
myloss = LpLoss(size_average=False)
step = 1
min_test_l2_full = 114514
t1 = default_timer()
test_l2_step = 0
test_l2_full = 0
timewise_l2_step = torch.zeros(T_out//step).to(device)
timewise_l2_full = torch.zeros(T_out//step).to(device)
print('ready')
with torch.no_grad():
for batch_idx, (xx, yy) in enumerate(tqdm(test_loader)):
loss = 0
xx = xx.to(device)
yy = yy.to(device)
if 'DeepLag' in args.model:
h_x_q, h_coo_q, h_coo_offset_q = [], [], []
for i in range(model.num_layers):
if args.resample_strategy == 'uniform':
num_samples = model.num_samples // (4**i)
coo_q = torch.cat([
torch.randint(0,model.img_h_layers[i]-1,(batch_size,num_samples,1)),
torch.randint(0,model.img_w_layers[i]-1,(batch_size,num_samples,1))
], dim=-1).to(torch.float32) # b k 2
elif args.resample_strategy == 'boundary':
num_samples = min(model.num_samples//(4**i), model.coo_boundary_ms[i].shape[0])
idx_coo_sample = torch.multinomial(1./torch.ones(model.coo_boundary_ms[i].shape[0]), num_samples, replacement=False) # k
coo_q = model.coo_boundary_ms[i][idx_coo_sample][None, ...].repeat(batch_size,1,1).to(torch.float32) # b k 2
elif args.resample_strategy == 'domain':
num_samples = min(model.num_samples//(4**i), model.coo_domain_ms[i].shape[0])
idx_coo_sample = torch.multinomial(1./torch.ones(model.coo_domain_ms[i].shape[0]), num_samples, replacement=False) # k
coo_q = model.coo_domain_ms[i][idx_coo_sample][None, ...].repeat(batch_size,1,1).to(torch.float32) # b k 2
elif args.resample_strategy == 'learned':
num_samples = model.num_samples // (4**i)
coo_q = None # new_prob
num_chan = args.d_model*(2**i) if i < model.num_layers-1 else args.d_model*(2**(i-1))
h_x_q.append(torch.zeros(batch_size, num_samples, num_chan).to(device))
h_coo_q.append(coo_q.to(device) if args.resample_strategy != 'learned' else None) # new_prob
h_coo_offset_q.append(torch.zeros(batch_size, num_samples, 2).to(device))
for i, t in enumerate(range(0, T_out, step)):
y = yy[..., t*args.out_var : (t + step)*args.out_var] # B H W C_out=V_out
if 'DeepLag' in args.model:
im, h_x_q, h_coo_q, h_coo_offset_q, coo_offset_xys = model(xx, h_x_q, h_coo_q, h_coo_offset_q) # B H W C_out=V_out # with coo_offset
else:
im = model(xx)
loss += myloss(im.reshape(batch_size, -1), y.reshape(batch_size, -1))
timewise_l2_step[i] += myloss(im, y)
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()
for i, t in enumerate(range(0, T_out, step)):
timewise_l2_full[i] += myloss(pred[..., i*args.in_var:(i+1)*args.in_var], yy[..., i*args.in_var:(i+1)*args.in_var])
t2 = default_timer()
if test_l2_full / ntest < min_test_l2_full:
print(t2 - t1,
'test_rel_l2:',
test_l2_step / ntest / (T_out / step),
test_l2_full / ntest,
'timewise_l2:',
timewise_l2_step / ntest,
timewise_l2_full / ntest)
logger.info(f'{t2 - t1} ' + \
f'test_rel_l2: {test_l2_step / ntest / (T_out / step)} {test_l2_full / ntest} ' + \
f'timewise_l2: {timewise_l2_step / ntest} {timewise_l2_full / ntest}')
pd = pred[-1, :, :, -1].detach().cpu().numpy()
gt = yy[-1, :, :, -1].detach().cpu().numpy()
visual(pd, os.path.join(test_save_path, f'{milestone}_pred.png'))
visual(gt, os.path.join(test_save_path, f'{milestone}_gt.png'))
visual(np.abs(gt-pd), os.path.join(test_save_path, f'{milestone}_err.png'))
else:
raise Exception('Abnormal loss!')