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

calculate_datasets_statistics.py

@@ -4,7 +4,7 @@
 
 # # 加载CIFAR-10数据集
 # transform = transforms.Compose([transforms.ToTensor()])
-# trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
+# trainset = torchvision.datasets.CIFAR10(root='./datasets', train=True, download=True, transform=transform)
 # trainloader = torch.utils.data.DataLoader(trainset, batch_size=10000, shuffle=False, num_workers=2)
 
 # # 将所有数据加载到内存中
@@ -18,6 +18,10 @@
 # print(f'Mean: {mean}')
 # print(f'Std: {std}')
 
+# results:
+# Mean: tensor([0.4935, 0.4834, 0.4472])
+# Std: tensor([0.2476, 0.2446, 0.2626])  
+
 import torch
 from torchvision import datasets, transforms
 from torch.utils.data import DataLoader
@@ -35,6 +39,7 @@ dataset_name = args.dataset
 
 # 设置数据集的transform（这里只使用了ToTensor）
 transform = transforms.Compose([
+    transforms.Resize((224, 224)),
     transforms.ToTensor()
 ])
 
@@ -47,7 +52,10 @@ mean = torch.zeros(3)
 std = torch.zeros(3)
 nb_samples = 0
 
+count = 0
 for data in dataloader:
+    count += 1
+    print(f'Processing batch {count}/{len(dataloader)}', end='\r')
     batch_samples = data[0].size(0)
     data = data[0].view(batch_samples, data[0].size(1), -1)
     mean += data.mean(2).sum(0)

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)

preprocess_aircraft.py

@@ -3,39 +3,51 @@ import shutil
 
 # 数据集路径
 dataset_path = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/images'
-output_path = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/sorted_images'
+test_output_path = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/test_sorted_images'
+train_output_path = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/train_sorted_images'
 
 # 类别文件，例如 'images_variant_trainval.txt'
-labels_file = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/images_variant_test.txt'
+# 有两个文件，一个是训练集和验证集，一个是测试集
+test_labels_file = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/images_variant_test.txt'
+train_labels_file = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/images_variant_train.txt'
 
 # 创建输出文件夹
-if not os.path.exists(output_path):
-    os.makedirs(output_path)
+if not os.path.exists(test_output_path):
+    os.makedirs(test_output_path)
+if not os.path.exists(train_output_path):
+    os.makedirs(train_output_path)
 
 # 读取类别文件
-with open(labels_file, 'r') as f:
-    lines = f.readlines()
+with open(test_labels_file, 'r') as f:
+    test_lines = f.readlines()
+with open(train_labels_file, 'r') as f:
+    train_lines = f.readlines()
 
-count = 0
+def sort_images(lines, output_path):
+    count = 0
+    for line in lines:
+        count += 1
+        print(f'Processing image {count}/{len(lines)}', end='\r')
+        parts = line.strip().split(' ')
+        image_name = parts[0] + '.jpg'
+        category = '_'.join(parts[1:]).replace('/', '_')
 
-for line in lines:
-    count += 1
-    print(f'Processing image {count}/{len(lines)}', end='\r')
-    parts = line.strip().split(' ')
-    image_name = parts[0] + '.jpg'
-    category = '_'.join(parts[1:]).replace('/', '_')
+        # 创建类别文件夹
+        category_path = os.path.join(output_path, category)
+        if not os.path.exists(category_path):
+            os.makedirs(category_path)
 
-    # 创建类别文件夹
-    category_path = os.path.join(output_path, category)
-    if not os.path.exists(category_path):
-        os.makedirs(category_path)
+        # 移动图像到对应类别文件夹
+        src = os.path.join(dataset_path, image_name)
+        dst = os.path.join(category_path, image_name)
+        if os.path.exists(src):
+            shutil.move(src, dst)
+        else:
+            print(f'Image {image_name} not found!')
 
-    # 移动图像到对应类别文件夹
-    src = os.path.join(dataset_path, image_name)
-    dst = os.path.join(category_path, image_name)
-    if os.path.exists(src):
-        shutil.move(src, dst)
-    else:
-        print(f'Image {image_name} not found!')
+print("Sorting test images into folders by category...")
+sort_images(test_lines, test_output_path)
+print("Sorting train images into folders by category...")
+sort_images(train_lines, train_output_path)
 
 print("Images have been sorted into folders by category.")