add resize to resize the images; cancel the acc; update the folder path

correlation.py

@@ -40,9 +40,9 @@ parser.add_argument('--device', default="cuda", type=str, nargs='?', help='setup
 parser.add_argument('--repeats', default=32, type=int, nargs='?', help='times of calculating the training-free metric')
 parser.add_argument('--input_samples', default=16, type=int, nargs='?', help='input batch size for training-free metric')
 parser.add_argument('--datasets', default='cifar10', type=str, help='input datasets')
+parser.add_argument('--start_index', default=0, type=int, help='start index of the networks to evaluate')
 
 args = parser.parse_args()
-
 if __name__ == "__main__":
     
     device = torch.device(args.device)
@@ -58,18 +58,21 @@ if __name__ == "__main__":
 
     # nasbench_len = 15625
     nasbench_len = 15625
+    filename = f'output/swap_results_{args.datasets}.csv'
+    if args.datasets == 'aircraft':
+        api_datasets = 'cifar10'
     
     # for index, i in arch_info.iterrows():
-    for ind in range(nasbench_len):
+    for ind in range(args.start_index,nasbench_len):
         # print(f'Evaluating network: {index}')
         print(f'Evaluating network: {ind}')
-
-        config = api.get_net_config(ind, args.datasets)
+        config = api.get_net_config(ind, api_datasets)
         network = get_cell_based_tiny_net(config)
         # nas_results = api.query_by_index(i, 'cifar10')
         # acc = nas_results[111].get_eval('ori-test')
-        nas_results = api.get_more_info(ind, args.datasets, None, hp=200, is_random=False)
-        acc = nas_results['test-accuracy']
+        # nas_results = api.get_more_info(ind, api_datasets, None, hp=200, is_random=False)
+        # acc = nas_results['test-accuracy']
+        acc = 99
 
         # print(type(network))
         start_time = time.time()
@@ -98,6 +101,8 @@ if __name__ == "__main__":
         print(f'Elapsed time: {end_time - start_time:.2f} seconds')
 
         results.append([np.mean(swap_score), acc, ind])
+        with open(filename, 'a') as f:
+            f.write(f'{np.mean(swap_score)},{acc},{ind}\n')
 
     results = pd.DataFrame(results, columns=['swap_score', 'valid_acc', 'index'])
     results.to_csv('output/swap_results.csv', float_format='%.4f', index=False)