add data

download_data.py

@@ -12,6 +12,7 @@
     'NS-Re100Part0': 'https://hkzdata.s3.us-west-2.amazonaws.com/PINO/data/NS_fine_Re100_T128_part0.npy', 
     'burgers': 'https://hkzdata.s3.us-west-2.amazonaws.com/PINO/data/burgers_pino.mat', 
     'NS-Re500_T300_id0': 'https://hkzdata.s3.us-west-2.amazonaws.com/PINO/NS-Re500_T300_id0.npy',
+    'NS-Re500_T300_id0-shuffle': 'https://hkzdata.s3.us-west-2.amazonaws.com/PINO/data/NS-Re500_T300_id0-shuffle.npy',
     'darcy-train': 'https://hkzdata.s3.us-west-2.amazonaws.com/PINO/data/piececonst_r421_N1024_smooth1.mat', 
     'darcy-test': 'https://hkzdata.s3.us-west-2.amazonaws.com/PINO/data/piececonst_r421_N1024_smooth2.mat', 
     'cavity': 'https://hkzdata.s3.us-west-2.amazonaws.com/PINO/data/cavity.mat',

inference.py

@@ -1,7 +1,10 @@
+'''
+This code generates the prediction on one instance. 
+Both the ground truth and the prediction are saved in a .pt file.
+'''
 import os
 import yaml
 from argparse import ArgumentParser
-import random
 
 import torch
 from torch.utils.data import DataLoader
@@ -17,7 +20,8 @@
 def get_pred(args):
     with open(args.config, 'r') as stream:
         config = yaml.load(stream, yaml.FullLoader)
-
+    basedir = os.path.join('exp', config['log']['logdir'])
+    save_path = os.path.join(basedir, 'predictions','prediction.pt')
     device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 
     # prepare data
@@ -55,7 +59,7 @@ def get_pred(args):
         torch.save({
             'truth': u.cpu(),
             'pred': out.cpu(),
-        }, 're500-1_8s-800-fno-50k-prediction.pt')
+        }, save_path)
         break