update gitignore for merge

.gitignore

@@ -1,2 +1,6 @@
 __pycache__/
-datasets/
+./datasets/
+swap_results.csv
+swap_results_*
+cifar-10*
+NAS-Bench-201-*

analyze.py

@@ -0,0 +1,75 @@
+import csv
+import matplotlib.pyplot as plt
+from scipy import stats
+import pandas as pd
+import argparse
+
+def plot(l, thousands, filename):
+    lenth = len(l)
+    threshold = [0, 10000, 20000, 30000, 40000, 50000, 60000, 70000]
+    labels = ['0-10k', '10k-20k,', '20k-30k', '30k-40k', '40k-50k', '50k-60k', '60k-70k']
+    l = [i/lenth for i in l]
+    l = l[:7]
+    thousands = thousands[60:]
+    thousands_labels = [str(i) + 'k' for i in range(60, 70)]
+    plt.figure(figsize=(8, 6))
+    plt.subplots_adjust(top=0.85)
+    plt.title('Distribution of Swap Scores over 60k')
+    plt.bar(thousands_labels, thousands)
+    for i, v in enumerate(thousands):
+        plt.text(i, v + 0.01, str(v), ha='center', va='bottom')
+    plt.savefig(filename + '_60k.png') 
+
+    datasets = filename.split('_')[-1].split('.')[0]
+    plt.figure(figsize=(8, 6))
+    plt.subplots_adjust(top=0.85)
+    # plt.ylim(0,0.3)
+    plt.title('Distribution of Swap Scores in ' + datasets)
+    plt.bar(labels, l)
+    for i, v in enumerate(l):
+        plt.text(i, v + 0.01, str(round(v, 2)), ha='center', va='bottom')
+    plt.savefig(filename)
+
+def analyse(filename):
+    l = [0 for i in range(10)]
+    scores = []
+    count = 0
+    best_value = -1
+    with open(filename) as file:
+        reader = csv.reader(file)
+        header = next(reader)
+        data = [row for row in reader]
+        thousands = [0 for i in range(70)]
+        
+        for row in data:
+            score = row[0]
+            best_value = max(best_value, float(score))
+            # print(score)
+            ind = float(score) // 10000
+            ind = int(ind)
+            l[ind] += 1
+            thousands[int(float(score) // 1000)] += 1
+            acc = row[1]
+            index = row[2]
+            datas = list(zip(score, acc, index))
+            scores.append(score)
+    print(max(scores))
+    results = pd.DataFrame(datas, columns=['swap_score', 'valid_acc', 'index'])
+    print(results['swap_score'].max())
+    print(best_value)
+    plot(l, thousands, filename + '.png')
+    return stats.spearmanr(results.swap_score, results.valid_acc)[0]
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--filename', type=str, help='Filename to analyze', default='swap_results.csv')
+
+    args = parser.parse_args()
+
+    print(analyse('output' + '/' + args.filename))
+
+
+
+
+
+

calculate_datasets_statistics.py

@@ -0,0 +1,69 @@
+# import torch
+# import torchvision
+# import torchvision.transforms as transforms
+
+# # 加载CIFAR-10数据集
+# transform = transforms.Compose([transforms.ToTensor()])
+# 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
+# data = next(iter(trainloader))
+# images, _ = data
+
+# # 计算每个通道的均值和标准差
+# mean = images.mean([0, 2, 3])
+# std = images.std([0, 2, 3])
+
+# 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
+import argparse
+
+parser = argparse.ArgumentParser(description='Calculate mean and std of dataset')
+parser.add_argument('--dataset', type=str, default='cifar10', help='dataset name')
+parser.add_argument('--data_path', type=str, default='./datasets/cifar-10-batches-py', help='path to dataset image folder')
+
+args = parser.parse_args()
+
+# 设置数据集路径
+dataset_path = args.data_path
+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()
+])
+
+# 使用ImageFolder加载数据集
+dataset = datasets.ImageFolder(root=dataset_path, transform=transform)
+dataloader = DataLoader(dataset, batch_size=64, shuffle=False, num_workers=4)
+
+# 初始化变量来累积均值和标准差
+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)
+    std += data.std(2).sum(0)
+    nb_samples += batch_samples
+
+mean /= nb_samples
+std /= nb_samples
+
+print(f'Mean: {mean}')
+print(f'Std: {std}')

correlation.py

@@ -39,35 +39,42 @@ parser.add_argument('--seed', default=0, type=int, help='random seed')
 parser.add_argument('--device', default="cuda", type=str, nargs='?', help='setup device (cpu, mps or cuda)')
 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)
 
     # arch_info = pd.read_csv(args.data_path+'/DARTS_archs_CIFAR10.csv', names=['genotype', 'valid_acc'], sep=',')
     
-    train_data, _, _ = get_datasets('cifar10', args.data_path, (args.input_samples, 3, 32, 32), -1)
+    train_data, _, _ = get_datasets(args.datasets, args.data_path, (args.input_samples, 3, 32, 32), -1)
     train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.input_samples, num_workers=0, pin_memory=True)
     loader = iter(train_loader)
     inputs, _ = next(loader)  
 
     results = []
 
+    # 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 i in range(nasbench_len):
+    for ind in range(args.start_index,nasbench_len):
         # print(f'Evaluating network: {index}')
-        print(f'Evaluating network: {i}')
-
-        config = api.get_net_config(i, 'cifar10')
+        print(f'Evaluating network: {ind}')
+        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.query_by_index(i, 'cifar10')
+        # acc = nas_results[111].get_eval('ori-test')
+        # 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))
+        # print(type(network))
         start_time = time.time()
 
         # network = Network(3, 10, 1, eval(i.genotype))
@@ -93,13 +100,15 @@ if __name__ == "__main__":
         print(f'Average SWAP score: {np.mean(swap_score)}')
         print(f'Elapsed time: {end_time - start_time:.2f} seconds')
 
-        results.append([np.mean(swap_score), acc, i])
+        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)
 
     print()    
     print(f'Spearman\'s Correlation Coefficient: {stats.spearmanr(results.swap_score, results.valid_acc)[0]}')
-    results.to_csv('swap_results.csv', float_format='%.4f', index=False)
 
     
 

preprocess_aircraft.py

@@ -0,0 +1,53 @@
+import os
+import shutil
+
+# 数据集路径
+dataset_path = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/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'
+# 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(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(test_labels_file, 'r') as f:
+    test_lines = f.readlines()
+with open(train_labels_file, 'r') as f:
+    train_lines = f.readlines()
+
+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('/', '_')
+
+        # 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!')
+
+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.")

src/datasets/utilities.py

@@ -13,7 +13,8 @@ Dataset2Class = {'cifar10': 10,
                  'ImageNet16' : 1000,
                  'ImageNet16-120': 120,
                  'ImageNet16-150': 150,
-                 'ImageNet16-200': 200}
+                 'ImageNet16-200': 200,
+                 'aircraft': 100}
 
 class RandChannel(object):
     # randomly pick channels from input
@@ -46,6 +47,10 @@ def get_datasets(name, root, input_size, cutout=-1):
     elif name.startswith('ImageNet16'):
         mean = [0.481098, 0.45749, 0.407882]
         std  = [0.247922, 0.240235, 0.255255]
+    elif name == 'aircraft':
+        mean = [0.4785, 0.5100, 0.5338]
+        std  = [0.1845, 0.1830, 0.2060]
+
     else:
         raise TypeError("Unknow dataset : {:}".format(name))
 
@@ -55,6 +60,12 @@ def get_datasets(name, root, input_size, cutout=-1):
         if cutout > 0 : lists += [CUTOUT(cutout)]
         train_transform = transforms.Compose(lists)
         test_transform  = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
+    elif name == 'aircraft':
+        lists = [transforms.RandomCrop(input_size[1], padding=0), transforms.ToTensor(), transforms.Normalize(mean, std)]
+        if cutout > 0 : lists += [CUTOUT(cutout)]
+        train_transform = transforms.Compose(lists)
+        test_transform  = transforms.Compose([transforms.Resize((224,224)), transforms.ToTensor(), transforms.Normalize(mean, std)])
+
     elif name.startswith('ImageNet16'):
         lists = [transforms.RandomCrop(input_size[1], padding=0), transforms.ToTensor(), transforms.Normalize(mean, std), RandChannel(input_size[0])]
         if cutout > 0 : lists += [CUTOUT(cutout)]
@@ -86,9 +97,12 @@ def get_datasets(name, root, input_size, cutout=-1):
         train_data = dset.CIFAR100(root, train=True , transform=train_transform, download=True)
         test_data  = dset.CIFAR100(root, train=False, transform=test_transform , download=True)
         assert len(train_data) == 50000 and len(test_data) == 10000
+    elif name == 'aircraft':
+        train_data = dset.ImageFolder(osp.join(root, 'train_sorted_images'), train_transform)
+        test_data  = dset.ImageFolder(osp.join(root, 'test_sorted_images'),  test_transform)
     elif name.startswith('imagenet-1k'):
         train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform)
-        test_data  = dset.ImageFolder(osp.join(root, 'val'),   test_transform)
+        test_data  = dset.ImageFolder(osp.join(root, 'test'),   test_transform)
     elif name == 'ImageNet16':
         root = osp.join(root, 'ImageNet16')
         train_data = ImageNet16(root, True , train_transform)