test_bc_h_vortex.py

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', return_idx=True)
test_dataset = BoundedNSDataset(args, dataset_file=TEST_PATH, split='test', return_idx=True)
train_loader = train_dataset.loader()
test_loader = test_dataset.loader()

boundary, domain = process_boundary_condition(BOUNDARY_PATH, ds_rate=(r1,r2))


################################################################
# 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, (index, xx, yy) in enumerate(tqdm(test_loader)):
        index = index.to(device) * 0.01
        loss = 0
        xx = xx.to(device)
        yy = yy.to(device)
        xx /= 256.0

        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
            im, _ = model(xx, index)
            im *= 256.0
            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!')